{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "from ultralytics import YOLO\n",
    "from torchvision import models, transforms\n",
    "from sklearn.cluster import KMeans\n",
    "import torch\n",
    "from matplotlib import pyplot as plt\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = YOLO(\"yolov8n.pt\").to('cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "print(model.device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Paths\n",
    "images_folder = 'dataset/cats'  # Replace with your image folder path\n",
    "output_folder = 'output/'  # Folder to save cropped cat images\n",
    "os.makedirs(output_folder, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to save bounding boxes in YOLO format\n",
    "def save_yolo_format(bounding_boxes, filename, image_width, image_height):\n",
    "    with open(filename, 'w') as f:\n",
    "        for box in bounding_boxes:\n",
    "            class_id = box['class_id']\n",
    "            # Normalize the bounding box coordinates\n",
    "            x_min = box['x_min']\n",
    "            y_min = box['y_min']\n",
    "            x_max = box['x_max']\n",
    "            y_max = box['y_max']\n",
    "            \n",
    "            # Center and size of the bounding box\n",
    "            x_center = (x_min + x_max) / 2.0 / image_width\n",
    "            y_center = (y_min + y_max) / 2.0 / image_height\n",
    "            width = (x_max - x_min) / float(image_width)\n",
    "            height = (y_max - y_min) / float(image_height)\n",
    "\n",
    "            # Write in YOLO format: class_id x_center y_center width height\n",
    "            f.write(f\"{class_id} {x_center} {y_center} {width} {height}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\1818949000-IMG-20240118-WA0001.jpg: 640x384 1 cat, 1 chair, 34.0ms\n",
      "Speed: 5.0ms preprocess, 34.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 384)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_103335.jpg: 640x480 1 cat, 1 bear, 2 chairs, 147.3ms\n",
      "Speed: 7.0ms preprocess, 147.3ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_141622.jpg: 640x480 1 person, 2 cats, 61.6ms\n",
      "Speed: 7.0ms preprocess, 61.6ms inference, 4.4ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_184600.jpg: 640x480 1 person, 1 cat, 1 remote, 1 book, 1 teddy bear, 70.0ms\n",
      "Speed: 8.0ms preprocess, 70.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_195833.jpg: 640x480 1 cat, 1 bed, 15.1ms\n",
      "Speed: 5.0ms preprocess, 15.1ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_200052.jpg: 640x480 1 person, 1 cat, 13.0ms\n",
      "Speed: 2.0ms preprocess, 13.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_200057.jpg: 640x480 1 cat, 1 bed, 11.0ms\n",
      "Speed: 4.0ms preprocess, 11.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_200102.jpg: 640x480 1 person, 1 cat, 1 bed, 18.5ms\n",
      "Speed: 5.0ms preprocess, 18.5ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201439.jpg: 640x480 1 cat, 13.0ms\n",
      "Speed: 2.0ms preprocess, 13.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201442.jpg: 640x480 1 cat, 1 bottle, 16.0ms\n",
      "Speed: 3.9ms preprocess, 16.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201443.jpg: 640x480 1 cat, 1 bottle, 24.2ms\n",
      "Speed: 4.8ms preprocess, 24.2ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201446.jpg: 640x480 1 cat, 1 bed, 82.0ms\n",
      "Speed: 6.0ms preprocess, 82.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201447.jpg: 640x480 1 cat, 1 dog, 39.0ms\n",
      "Speed: 3.7ms preprocess, 39.0ms inference, 10.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201448.jpg: 640x480 1 cat, 1 dog, 40.0ms\n",
      "Speed: 6.0ms preprocess, 40.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201452.jpg: 640x480 1 cat, 1 handbag, 24.7ms\n",
      "Speed: 5.0ms preprocess, 24.7ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201454.jpg: 640x480 1 cat, 1 dog, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201458.jpg: 640x480 1 cat, 1 handbag, 30.1ms\n",
      "Speed: 4.0ms preprocess, 30.1ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201503.jpg: 640x480 1 dog, 1 handbag, 26.0ms\n",
      "Speed: 5.0ms preprocess, 26.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201510.jpg: 640x480 1 cat, 1 handbag, 24.5ms\n",
      "Speed: 5.0ms preprocess, 24.5ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_232857.jpg: 640x480 1 cat, 1 suitcase, 126.9ms\n",
      "Speed: 5.0ms preprocess, 126.9ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240310_140207.jpg: 640x480 1 cat, 10.4ms\n",
      "Speed: 4.0ms preprocess, 10.4ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240311_195350.jpg: 640x480 1 cat, 1 handbag, 1 bed, 12.0ms\n",
      "Speed: 2.0ms preprocess, 12.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240311_195352.jpg: 640x480 1 cat, 12.0ms\n",
      "Speed: 4.0ms preprocess, 12.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240311_195357.jpg: 640x480 1 cat, 1 chair, 17.0ms\n",
      "Speed: 4.0ms preprocess, 17.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240311_195403.jpg: 640x480 1 cat, 20.0ms\n",
      "Speed: 4.0ms preprocess, 20.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_180219.jpg: 640x480 1 cat, 1 couch, 28.0ms\n",
      "Speed: 4.0ms preprocess, 28.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185655.jpg: 640x480 1 cat, 1 toilet, 77.6ms\n",
      "Speed: 6.0ms preprocess, 77.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185658.jpg: 640x480 2 cats, 16.0ms\n",
      "Speed: 4.0ms preprocess, 16.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185700.jpg: 640x480 4 cats, 1 bowl, 17.0ms\n",
      "Speed: 4.0ms preprocess, 17.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185702.jpg: 640x480 2 cats, 12.0ms\n",
      "Speed: 2.0ms preprocess, 12.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185718(0).jpg: 640x480 2 cats, 1 bowl, 1 chair, 19.0ms\n",
      "Speed: 4.1ms preprocess, 19.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185718.jpg: 640x480 2 cats, 1 bowl, 17.0ms\n",
      "Speed: 4.0ms preprocess, 17.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185719.jpg: 640x480 1 cat, 70.0ms\n",
      "Speed: 5.0ms preprocess, 70.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185729.jpg: 640x480 1 cat, 44.0ms\n",
      "Speed: 4.0ms preprocess, 44.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185730.jpg: 640x480 1 cat, 1 chair, 29.0ms\n",
      "Speed: 4.0ms preprocess, 29.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185733.jpg: 640x480 1 cat, 30.4ms\n",
      "Speed: 6.0ms preprocess, 30.4ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185734.jpg: 640x480 1 cat, 1 chair, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 4.9ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240313_203246.jpg: 640x480 1 person, 1 cat, 1 bed, 24.0ms\n",
      "Speed: 4.2ms preprocess, 24.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240313_203248.jpg: 640x480 1 cat, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_170048.jpg: 640x480 1 cat, 25.2ms\n",
      "Speed: 4.0ms preprocess, 25.2ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_170048_remastered.jpg: 640x480 1 cat, 1 dog, 25.0ms\n",
      "Speed: 5.0ms preprocess, 25.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_171004.jpg: 640x480 1 cat, 45.6ms\n",
      "Speed: 6.0ms preprocess, 45.6ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_171030.jpg: 640x480 1 dog, 19.0ms\n",
      "Speed: 4.7ms preprocess, 19.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191222.jpg: 640x480 1 cat, 1 chair, 24.0ms\n",
      "Speed: 3.0ms preprocess, 24.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191224(0).jpg: 640x480 1 cat, 1 umbrella, 17.0ms\n",
      "Speed: 2.0ms preprocess, 17.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191224.jpg: 640x480 1 cat, 1 bottle, 18.7ms\n",
      "Speed: 4.6ms preprocess, 18.7ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191225.jpg: 640x480 1 cat, 30.9ms\n",
      "Speed: 5.0ms preprocess, 30.9ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191226.jpg: 640x480 1 cat, 60.0ms\n",
      "Speed: 4.0ms preprocess, 60.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191227.jpg: 640x480 1 cat, 52.1ms\n",
      "Speed: 7.0ms preprocess, 52.1ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104313.jpg: 640x480 1 cat, 1 bottle, 9.0ms\n",
      "Speed: 3.0ms preprocess, 9.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104328.jpg: 640x480 1 cat, 1 bottle, 1 bowl, 9.0ms\n",
      "Speed: 3.0ms preprocess, 9.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104330.jpg: 640x480 1 cat, 1 bottle, 1 bowl, 9.0ms\n",
      "Speed: 3.0ms preprocess, 9.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104331.jpg: 640x480 1 cat, 9.0ms\n",
      "Speed: 2.0ms preprocess, 9.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104334.jpg: 640x480 1 person, 1 dog, 1 tv, 12.0ms\n",
      "Speed: 3.1ms preprocess, 12.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_220146.jpg: 640x480 1 person, 2 dogs, 93.0ms\n",
      "Speed: 4.0ms preprocess, 93.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_220153.jpg: 640x480 2 cats, 66.0ms\n",
      "Speed: 4.7ms preprocess, 66.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_220210.jpg: 640x480 1 person, 3 cats, 61.0ms\n",
      "Speed: 3.8ms preprocess, 61.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002355.jpg: 640x480 1 cat, 87.0ms\n",
      "Speed: 4.0ms preprocess, 87.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002404.jpg: 640x480 1 cat, 72.6ms\n",
      "Speed: 5.0ms preprocess, 72.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002407.jpg: 640x480 1 cat, 101.0ms\n",
      "Speed: 5.0ms preprocess, 101.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002416.jpg: 640x480 1 cat, 72.0ms\n",
      "Speed: 5.0ms preprocess, 72.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002419.jpg: 640x480 1 cat, 23.0ms\n",
      "Speed: 6.0ms preprocess, 23.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204422.jpg: 640x480 1 person, 1 cat, 1 bed, 74.8ms\n",
      "Speed: 6.0ms preprocess, 74.8ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204426.jpg: 640x480 1 cat, 1 bear, 1 chair, 31.7ms\n",
      "Speed: 6.0ms preprocess, 31.7ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204429.jpg: 640x480 1 cat, 1 chair, 88.7ms\n",
      "Speed: 6.0ms preprocess, 88.7ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204431.jpg: 640x480 1 cat, 94.0ms\n",
      "Speed: 4.0ms preprocess, 94.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204434.jpg: 640x480 1 cat, 1 bed, 73.2ms\n",
      "Speed: 5.0ms preprocess, 73.2ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204436.jpg: 640x480 1 cat, 1 elephant, 10.0ms\n",
      "Speed: 2.0ms preprocess, 10.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204501.jpg: 640x480 1 bear, 14.0ms\n",
      "Speed: 4.0ms preprocess, 14.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204503.jpg: 640x480 1 cat, 13.0ms\n",
      "Speed: 3.0ms preprocess, 13.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240318_194443.jpg: 640x480 1 cat, 1 bed, 1 teddy bear, 12.0ms\n",
      "Speed: 3.0ms preprocess, 12.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240320_171347.jpg: 640x480 1 bench, 1 cat, 41.6ms\n",
      "Speed: 5.0ms preprocess, 41.6ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185912(0).jpg: 640x480 2 teddy bears, 19.0ms\n",
      "Speed: 4.0ms preprocess, 19.0ms inference, 14.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185912.jpg: 640x480 1 cat, 1 teddy bear, 13.0ms\n",
      "Speed: 4.0ms preprocess, 13.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185914.jpg: 640x480 1 cat, 1 teddy bear, 31.0ms\n",
      "Speed: 3.0ms preprocess, 31.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185916.jpg: 640x480 1 cat, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 7.6ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185917.jpg: 640x480 1 cat, 28.0ms\n",
      "Speed: 4.0ms preprocess, 28.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185921.jpg: 640x480 1 cat, 49.3ms\n",
      "Speed: 5.0ms preprocess, 49.3ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185922.jpg: 640x480 1 cat, 27.0ms\n",
      "Speed: 4.0ms preprocess, 27.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185924.jpg: 640x480 1 cat, 30.0ms\n",
      "Speed: 4.6ms preprocess, 30.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185950.jpg: 480x640 1 cat, 76.7ms\n",
      "Speed: 5.0ms preprocess, 76.7ms inference, 6.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_190008.jpg: 480x640 1 dog, 1 bed, 23.0ms\n",
      "Speed: 4.0ms preprocess, 23.0ms inference, 4.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_190014.jpg: 480x640 1 cat, 1 dog, 20.0ms\n",
      "Speed: 4.0ms preprocess, 20.0ms inference, 3.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193424.jpg: 640x480 2 benchs, 1 cat, 1 dining table, 33.5ms\n",
      "Speed: 4.0ms preprocess, 33.5ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193425.jpg: 640x480 1 bench, 46.0ms\n",
      "Speed: 4.0ms preprocess, 46.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193430.jpg: 640x480 1 dog, 25.0ms\n",
      "Speed: 5.0ms preprocess, 25.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193431.jpg: 640x480 1 bench, 1 cat, 25.0ms\n",
      "Speed: 5.1ms preprocess, 25.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193433.jpg: 640x480 1 bench, 1 cat, 24.9ms\n",
      "Speed: 3.6ms preprocess, 24.9ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193448.jpg: 640x480 1 surfboard, 49.0ms\n",
      "Speed: 5.5ms preprocess, 49.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193501.jpg: 640x480 (no detections), 9.0ms\n",
      "Speed: 2.0ms preprocess, 9.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175352.jpg: 640x480 1 cat, 12.0ms\n",
      "Speed: 5.0ms preprocess, 12.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175354.jpg: 640x480 1 cat, 8.0ms\n",
      "Speed: 3.0ms preprocess, 8.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175358.jpg: 640x480 1 cat, 8.0ms\n",
      "Speed: 2.0ms preprocess, 8.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175402.jpg: 640x480 1 cat, 9.0ms\n",
      "Speed: 3.0ms preprocess, 9.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175405.jpg: 640x480 1 cat, 9.0ms\n",
      "Speed: 3.0ms preprocess, 9.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175406.jpg: 640x480 1 cat, 13.0ms\n",
      "Speed: 3.0ms preprocess, 13.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175407.jpg: 640x480 1 cat, 22.0ms\n",
      "Speed: 5.0ms preprocess, 22.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240325_163728.jpg: 640x480 2 cats, 1 laptop, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_131427.jpg: 640x480 1 cat, 3 chairs, 5 potted plants, 3 vases, 25.0ms\n",
      "Speed: 3.0ms preprocess, 25.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_131431.jpg: 640x480 1 cat, 1 suitcase, 40.5ms\n",
      "Speed: 5.0ms preprocess, 40.5ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183749.jpg: 640x480 1 cat, 9.0ms\n",
      "Speed: 3.0ms preprocess, 9.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183757.jpg: 640x480 1 cat, 10.0ms\n",
      "Speed: 3.0ms preprocess, 10.0ms inference, 3.2ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183759.jpg: 640x480 1 cat, 10.8ms\n",
      "Speed: 3.0ms preprocess, 10.8ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183803.jpg: 640x480 1 cat, 8.0ms\n",
      "Speed: 2.0ms preprocess, 8.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183810.jpg: 480x640 1 cat, 10.5ms\n",
      "Speed: 2.0ms preprocess, 10.5ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135547.jpg: 640x480 1 cat, 12.0ms\n",
      "Speed: 4.0ms preprocess, 12.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135550.jpg: 640x480 1 cat, 14.0ms\n",
      "Speed: 4.0ms preprocess, 14.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135553.jpg: 640x480 1 cat, 1 bed, 81.0ms\n",
      "Speed: 7.0ms preprocess, 81.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135555.jpg: 640x480 1 cat, 1 couch, 1 tv, 62.9ms\n",
      "Speed: 6.0ms preprocess, 62.9ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135643.jpg: 640x480 1 cat, 10.9ms\n",
      "Speed: 3.0ms preprocess, 10.9ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240329_223942(0).jpg: 640x480 1 cat, 19.9ms\n",
      "Speed: 4.0ms preprocess, 19.9ms inference, 3.5ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240329_223942.jpg: 640x480 1 cat, 1 chair, 13.0ms\n",
      "Speed: 3.0ms preprocess, 13.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240329_223945.jpg: 640x480 1 cat, 16.0ms\n",
      "Speed: 3.0ms preprocess, 16.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224228.jpg: 640x480 1 cat, 14.0ms\n",
      "Speed: 3.0ms preprocess, 14.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224230.jpg: 640x480 1 cat, 1 dining table, 22.0ms\n",
      "Speed: 3.0ms preprocess, 22.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224232.jpg: 640x480 2 cats, 37.0ms\n",
      "Speed: 5.0ms preprocess, 37.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224233.jpg: 640x480 1 cat, 44.6ms\n",
      "Speed: 5.0ms preprocess, 44.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224236.jpg: 640x480 1 cat, 46.0ms\n",
      "Speed: 4.0ms preprocess, 46.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224237.jpg: 640x480 1 cat, 31.0ms\n",
      "Speed: 5.0ms preprocess, 31.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224242.jpg: 640x480 1 cat, 35.4ms\n",
      "Speed: 7.0ms preprocess, 35.4ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152307.jpg: 640x480 1 cat, 1 chair, 40.0ms\n",
      "Speed: 4.6ms preprocess, 40.0ms inference, 6.8ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152308.jpg: 640x480 1 cat, 1 chair, 51.0ms\n",
      "Speed: 4.6ms preprocess, 51.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152310.jpg: 640x480 1 cat, 58.5ms\n",
      "Speed: 5.0ms preprocess, 58.5ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152316.jpg: 640x480 1 cat, 57.0ms\n",
      "Speed: 6.6ms preprocess, 57.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152338.jpg: 640x480 1 cat, 67.0ms\n",
      "Speed: 4.0ms preprocess, 67.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152341.jpg: 640x480 1 cat, 1 chair, 31.0ms\n",
      "Speed: 4.9ms preprocess, 31.0ms inference, 10.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233450.jpg: 640x480 2 chairs, 1 book, 43.0ms\n",
      "Speed: 3.0ms preprocess, 43.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233452.jpg: 640x480 1 chair, 32.0ms\n",
      "Speed: 4.0ms preprocess, 32.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233500.jpg: 640x480 1 cat, 33.0ms\n",
      "Speed: 4.0ms preprocess, 33.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233501.jpg: 640x480 1 cat, 1 bed, 28.0ms\n",
      "Speed: 4.0ms preprocess, 28.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233502.jpg: 640x480 1 cat, 32.0ms\n",
      "Speed: 5.0ms preprocess, 32.0ms inference, 5.8ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233503.jpg: 640x480 1 bench, 1 cat, 31.0ms\n",
      "Speed: 4.0ms preprocess, 31.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233504.jpg: 640x480 1 cat, 37.0ms\n",
      "Speed: 4.0ms preprocess, 37.0ms inference, 5.8ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233505.jpg: 640x480 1 cat, 34.0ms\n",
      "Speed: 4.0ms preprocess, 34.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233506.jpg: 640x480 1 cat, 45.0ms\n",
      "Speed: 6.0ms preprocess, 45.0ms inference, 6.6ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233553.jpg: 480x640 (no detections), 30.0ms\n",
      "Speed: 5.1ms preprocess, 30.0ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233600.jpg: 480x640 1 cat, 22.0ms\n",
      "Speed: 3.0ms preprocess, 22.0ms inference, 5.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233601.jpg: 480x640 (no detections), 24.5ms\n",
      "Speed: 4.0ms preprocess, 24.5ms inference, 2.5ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233605.jpg: 480x640 (no detections), 23.0ms\n",
      "Speed: 4.0ms preprocess, 23.0ms inference, 2.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233736.jpg: 640x480 1 bench, 1 apple, 1 bed, 59.4ms\n",
      "Speed: 5.0ms preprocess, 59.4ms inference, 10.9ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233740.jpg: 640x480 1 bench, 27.6ms\n",
      "Speed: 4.0ms preprocess, 27.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233759.jpg: 640x480 (no detections), 36.2ms\n",
      "Speed: 8.0ms preprocess, 36.2ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233835.jpg: 640x480 1 bench, 34.9ms\n",
      "Speed: 6.0ms preprocess, 34.9ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234112.jpg: 640x480 2 cats, 1 potted plant, 32.0ms\n",
      "Speed: 4.0ms preprocess, 32.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234115.jpg: 640x480 1 cat, 1 dining table, 30.9ms\n",
      "Speed: 5.0ms preprocess, 30.9ms inference, 6.5ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234507.jpg: 640x480 1 bench, 1 cat, 1 chair, 29.4ms\n",
      "Speed: 4.0ms preprocess, 29.4ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234510.jpg: 640x480 1 cat, 2 chairs, 33.0ms\n",
      "Speed: 4.0ms preprocess, 33.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234512.jpg: 640x480 1 cat, 33.0ms\n",
      "Speed: 5.0ms preprocess, 33.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234514.jpg: 640x480 1 cat, 28.0ms\n",
      "Speed: 5.0ms preprocess, 28.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234520.jpg: 640x480 2 cats, 1 bed, 1 toilet, 30.0ms\n",
      "Speed: 5.0ms preprocess, 30.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234523.jpg: 640x480 1 cat, 45.3ms\n",
      "Speed: 6.0ms preprocess, 45.3ms inference, 37.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234524.jpg: 640x480 2 cats, 41.9ms\n",
      "Speed: 4.0ms preprocess, 41.9ms inference, 6.7ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234614.jpg: 640x480 1 bird, 41.0ms\n",
      "Speed: 6.5ms preprocess, 41.0ms inference, 7.8ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234630.jpg: 640x480 1 cat, 1 dog, 45.0ms\n",
      "Speed: 7.0ms preprocess, 45.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234749.jpg: 640x480 1 cat, 10.0ms\n",
      "Speed: 3.0ms preprocess, 10.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_235449.jpg: 640x480 1 cat, 11.1ms\n",
      "Speed: 3.0ms preprocess, 11.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_235454.jpg: 640x480 1 chair, 1 teddy bear, 11.0ms\n",
      "Speed: 3.0ms preprocess, 11.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240414_204830.jpg: 640x480 1 cow, 9.0ms\n",
      "Speed: 3.0ms preprocess, 9.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240414_204832.jpg: 640x480 2 cats, 8.0ms\n",
      "Speed: 3.0ms preprocess, 8.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240415_191936.jpg: 640x480 1 cat, 13.1ms\n",
      "Speed: 3.0ms preprocess, 13.1ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240416_131400.jpg: 640x480 1 person, 1 cat, 1 chair, 1 couch, 1 tv, 13.0ms\n",
      "Speed: 4.0ms preprocess, 13.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240421_133252.jpg: 640x480 1 dog, 30.0ms\n",
      "Speed: 5.0ms preprocess, 30.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240421_133313.jpg: 640x480 1 dog, 1 suitcase, 29.9ms\n",
      "Speed: 7.0ms preprocess, 29.9ms inference, 10.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240423_103248.jpg: 480x640 1 cat, 31.0ms\n",
      "Speed: 4.0ms preprocess, 31.0ms inference, 5.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240424_204037.jpg: 640x480 2 cats, 1 chair, 16.0ms\n",
      "Speed: 3.0ms preprocess, 16.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240425_224816.jpg: 640x480 1 cat, 15.8ms\n",
      "Speed: 3.0ms preprocess, 15.8ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240425_224817(0).jpg: 640x480 1 cat, 13.0ms\n",
      "Speed: 3.0ms preprocess, 13.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240425_224817.jpg: 640x480 1 cat, 12.0ms\n",
      "Speed: 4.0ms preprocess, 12.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240425_224819.jpg: 640x480 1 cat, 21.0ms\n",
      "Speed: 7.0ms preprocess, 21.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240426_133718.jpg: 640x480 1 cat, 21.0ms\n",
      "Speed: 6.0ms preprocess, 21.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240426_133720.jpg: 640x480 1 cat, 42.5ms\n",
      "Speed: 5.0ms preprocess, 42.5ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_113516.jpg: 640x480 1 cat, 75.0ms\n",
      "Speed: 4.0ms preprocess, 75.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_113519.jpg: 640x480 1 cat, 1 chair, 56.5ms\n",
      "Speed: 5.0ms preprocess, 56.5ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_130031.jpg: 640x480 1 cat, 25.0ms\n",
      "Speed: 5.0ms preprocess, 25.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_130032.jpg: 640x480 1 cat, 24.0ms\n",
      "Speed: 4.0ms preprocess, 24.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_130041.jpg: 640x480 1 cat, 98.1ms\n",
      "Speed: 8.0ms preprocess, 98.1ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240503_173937.jpg: 640x480 1 cat, 33.0ms\n",
      "Speed: 5.0ms preprocess, 33.0ms inference, 5.5ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240503_173948.jpg: 480x640 1 cat, 1 chair, 28.0ms\n",
      "Speed: 4.6ms preprocess, 28.0ms inference, 4.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185557.jpg: 640x480 1 bench, 1 cat, 1 backpack, 1 chair, 1 potted plant, 31.0ms\n",
      "Speed: 5.0ms preprocess, 31.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185559.jpg: 640x480 1 cat, 1 bear, 1 tv, 25.0ms\n",
      "Speed: 6.0ms preprocess, 25.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185601.jpg: 640x480 1 cat, 24.0ms\n",
      "Speed: 4.0ms preprocess, 24.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185604.jpg: 640x480 1 cat, 24.7ms\n",
      "Speed: 5.0ms preprocess, 24.7ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185606.jpg: 640x480 2 cats, 1 dog, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185608.jpg: 640x480 2 cats, 1 potted plant, 25.4ms\n",
      "Speed: 5.0ms preprocess, 25.4ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185612.jpg: 640x480 1 cat, 26.0ms\n",
      "Speed: 4.8ms preprocess, 26.0ms inference, 5.4ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_185650.jpg: 640x480 2 cats, 4 chairs, 39.6ms\n",
      "Speed: 4.0ms preprocess, 39.6ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_185651.jpg: 640x480 2 cats, 1 cup, 2 chairs, 24.2ms\n",
      "Speed: 4.1ms preprocess, 24.2ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_185652.jpg: 640x480 2 cats, 1 cup, 2 chairs, 40.2ms\n",
      "Speed: 5.0ms preprocess, 40.2ms inference, 6.6ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_185656.jpg: 640x480 1 bottle, 2 bowls, 3 chairs, 3 potted plants, 1 dining table, 1 tv, 3 vases, 25.2ms\n",
      "Speed: 5.0ms preprocess, 25.2ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_194053.jpg: 640x480 1 cat, 1 chair, 36.0ms\n",
      "Speed: 4.0ms preprocess, 36.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_210228.jpg: 640x480 1 cat, 24.5ms\n",
      "Speed: 4.0ms preprocess, 24.5ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_071046.jpg: 640x480 1 cat, 1 dog, 25.1ms\n",
      "Speed: 5.0ms preprocess, 25.1ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_071050.jpg: 640x480 1 cat, 2 bottles, 1 refrigerator, 24.0ms\n",
      "Speed: 4.0ms preprocess, 24.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_071052.jpg: 640x480 1 cat, 2 bottles, 91.6ms\n",
      "Speed: 6.0ms preprocess, 91.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_071053.jpg: 640x480 1 cat, 2 bottles, 41.9ms\n",
      "Speed: 6.5ms preprocess, 41.9ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_072915.jpg: 640x480 1 cat, 1 couch, 12.0ms\n",
      "Speed: 3.0ms preprocess, 12.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_231919.jpg: 640x480 1 cat, 1 mouse, 8.9ms\n",
      "Speed: 3.0ms preprocess, 8.9ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_231920.jpg: 640x480 1 cat, 8.0ms\n",
      "Speed: 3.0ms preprocess, 8.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_235311.jpg: 640x480 1 cat, 1 bowl, 12.1ms\n",
      "Speed: 3.0ms preprocess, 12.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_235315.jpg: 640x480 1 cat, 10.4ms\n",
      "Speed: 3.6ms preprocess, 10.4ms inference, 4.5ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_235317.jpg: 640x480 1 cat, 1 bowl, 12.0ms\n",
      "Speed: 3.0ms preprocess, 12.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090846.jpg: 640x480 1 cat, 19.0ms\n",
      "Speed: 4.5ms preprocess, 19.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090900.jpg: 640x480 1 cat, 19.9ms\n",
      "Speed: 5.0ms preprocess, 19.9ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090907.jpg: 640x480 1 cat, 20.0ms\n",
      "Speed: 4.0ms preprocess, 20.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090911.jpg: 640x480 1 cat, 32.0ms\n",
      "Speed: 5.0ms preprocess, 32.0ms inference, 5.9ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090915.jpg: 640x480 1 person, 1 dog, 34.8ms\n",
      "Speed: 5.0ms preprocess, 34.8ms inference, 11.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180307.jpg: 640x480 2 cats, 25.4ms\n",
      "Speed: 5.0ms preprocess, 25.4ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180310.jpg: 640x480 2 cats, 1 handbag, 1 suitcase, 27.1ms\n",
      "Speed: 4.0ms preprocess, 27.1ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180316.jpg: 640x480 1 cat, 1 couch, 25.0ms\n",
      "Speed: 3.0ms preprocess, 25.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180318.jpg: 640x480 1 cat, 1 book, 24.2ms\n",
      "Speed: 4.0ms preprocess, 24.2ms inference, 5.8ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180331.jpg: 640x480 1 cat, 25.0ms\n",
      "Speed: 3.0ms preprocess, 25.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180334.jpg: 640x480 1 cat, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180528.jpg: 640x480 1 dog, 214.6ms\n",
      "Speed: 6.0ms preprocess, 214.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180532.jpg: 640x480 1 cat, 16.7ms\n",
      "Speed: 4.0ms preprocess, 16.7ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180534.jpg: 640x480 (no detections), 11.7ms\n",
      "Speed: 3.0ms preprocess, 11.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180536.jpg: 640x480 (no detections), 12.0ms\n",
      "Speed: 3.0ms preprocess, 12.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180542.jpg: 640x480 1 person, 18.0ms\n",
      "Speed: 3.0ms preprocess, 18.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180847.jpg: 640x480 1 cat, 33.8ms\n",
      "Speed: 4.0ms preprocess, 33.8ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180849.jpg: 640x480 2 cats, 1 suitcase, 42.0ms\n",
      "Speed: 4.0ms preprocess, 42.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180853.jpg: 640x480 1 cat, 1 couch, 118.4ms\n",
      "Speed: 5.0ms preprocess, 118.4ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180937.jpg: 640x480 1 cat, 12.0ms\n",
      "Speed: 2.0ms preprocess, 12.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180939.jpg: 640x480 1 cat, 10.0ms\n",
      "Speed: 4.0ms preprocess, 10.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_193135.jpg: 640x480 1 person, 3 cats, 1 dog, 2 chairs, 9.1ms\n",
      "Speed: 4.0ms preprocess, 9.1ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_193136.jpg: 640x480 1 person, 4 cats, 12.0ms\n",
      "Speed: 4.0ms preprocess, 12.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_213919(0).jpg: 640x480 1 cat, 1 chair, 18.0ms\n",
      "Speed: 4.0ms preprocess, 18.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_213919.jpg: 640x480 1 cat, 1 chair, 1 couch, 1 bed, 26.0ms\n",
      "Speed: 4.9ms preprocess, 26.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_223849.jpg: 640x480 1 cat, 1 chair, 1 bed, 29.0ms\n",
      "Speed: 3.0ms preprocess, 29.0ms inference, 9.5ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_223851.jpg: 640x480 1 cat, 1 cup, 2 chairs, 1 tv, 1 book, 88.0ms\n",
      "Speed: 4.0ms preprocess, 88.0ms inference, 15.5ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240603_225020(0).jpg: 640x480 1 cat, 1 dog, 15.0ms\n",
      "Speed: 3.0ms preprocess, 15.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240603_225020.jpg: 640x480 1 cat, 11.0ms\n",
      "Speed: 3.0ms preprocess, 11.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240603_225023.jpg: 640x480 1 dog, 13.1ms\n",
      "Speed: 3.0ms preprocess, 13.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240603_225026.jpg: 640x480 1 dog, 11.0ms\n",
      "Speed: 2.0ms preprocess, 11.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240603_225027.jpg: 640x480 1 dog, 15.0ms\n",
      "Speed: 4.0ms preprocess, 15.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192457.jpg: 640x480 1 cat, 1 couch, 16.0ms\n",
      "Speed: 4.1ms preprocess, 16.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192458(0).jpg: 640x480 1 cat, 1 couch, 43.0ms\n",
      "Speed: 6.0ms preprocess, 43.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192458.jpg: 640x480 1 cat, 1 couch, 89.2ms\n",
      "Speed: 10.0ms preprocess, 89.2ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192459(0).jpg: 640x480 1 cat, 1 couch, 20.1ms\n",
      "Speed: 3.0ms preprocess, 20.1ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192459.jpg: 640x480 1 cat, 1 couch, 56.0ms\n",
      "Speed: 8.0ms preprocess, 56.0ms inference, 11.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192501(0).jpg: 640x480 1 cat, 1 couch, 78.0ms\n",
      "Speed: 6.0ms preprocess, 78.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192501.jpg: 640x480 1 cat, 9.0ms\n",
      "Speed: 3.0ms preprocess, 9.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192502(0).jpg: 640x480 1 cat, 1 couch, 13.0ms\n",
      "Speed: 3.0ms preprocess, 13.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192502.jpg: 640x480 1 cat, 1 couch, 13.0ms\n",
      "Speed: 4.0ms preprocess, 13.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192503.jpg: 640x480 1 cat, 1 dog, 1 couch, 11.0ms\n",
      "Speed: 4.0ms preprocess, 11.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192504.jpg: 640x480 1 cat, 1 bed, 13.0ms\n",
      "Speed: 4.0ms preprocess, 13.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192505.jpg: 640x480 1 cat, 1 bed, 16.0ms\n",
      "Speed: 4.0ms preprocess, 16.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192506.jpg: 640x480 1 cat, 1 couch, 1 bed, 42.0ms\n",
      "Speed: 4.0ms preprocess, 42.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192508.jpg: 640x480 1 cat, 121.6ms\n",
      "Speed: 5.7ms preprocess, 121.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192509.jpg: 640x480 1 cat, 1 bed, 36.3ms\n",
      "Speed: 4.2ms preprocess, 36.3ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192510.jpg: 640x480 1 dog, 1 bed, 49.0ms\n",
      "Speed: 6.0ms preprocess, 49.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192511.jpg: 640x480 1 dog, 2 beds, 27.0ms\n",
      "Speed: 44.0ms preprocess, 27.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192512(0).jpg: 640x480 1 cat, 1 dog, 1 bed, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192512.jpg: 640x480 1 dog, 1 bed, 25.0ms\n",
      "Speed: 5.0ms preprocess, 25.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192513.jpg: 640x480 1 cat, 1 remote, 24.8ms\n",
      "Speed: 5.0ms preprocess, 24.8ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192514.jpg: 640x480 1 cat, 1 bed, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192515(0).jpg: 640x480 1 cat, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192515.jpg: 640x480 1 cat, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192533.jpg: 640x480 1 cat, 1 bed, 144.5ms\n",
      "Speed: 5.0ms preprocess, 144.5ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192534(0).jpg: 640x480 1 cat, 53.3ms\n",
      "Speed: 5.0ms preprocess, 53.3ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192534.jpg: 640x480 1 cat, 65.0ms\n",
      "Speed: 4.5ms preprocess, 65.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192535.jpg: 640x480 1 cat, 70.8ms\n",
      "Speed: 4.0ms preprocess, 70.8ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192537.jpg: 640x480 1 cat, 1 bed, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192538(0).jpg: 640x480 1 cat, 28.0ms\n",
      "Speed: 4.0ms preprocess, 28.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192538.jpg: 640x480 1 cat, 1 couch, 1 bed, 38.0ms\n",
      "Speed: 5.6ms preprocess, 38.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192539(0).jpg: 640x480 1 cat, 32.0ms\n",
      "Speed: 3.5ms preprocess, 32.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192539.jpg: 640x480 1 cat, 92.7ms\n",
      "Speed: 3.0ms preprocess, 92.7ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192542.jpg: 640x480 1 cat, 27.0ms\n",
      "Speed: 4.0ms preprocess, 27.0ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192544.jpg: 640x480 1 cat, 1 couch, 1 bed, 23.0ms\n",
      "Speed: 6.1ms preprocess, 23.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205057.jpg: 640x480 1 cat, 1 book, 41.0ms\n",
      "Speed: 4.0ms preprocess, 41.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205103.jpg: 640x480 1 cat, 34.6ms\n",
      "Speed: 3.0ms preprocess, 34.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205105.jpg: 640x480 1 cat, 46.8ms\n",
      "Speed: 5.0ms preprocess, 46.8ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205110.jpg: 640x480 1 cat, 40.0ms\n",
      "Speed: 6.0ms preprocess, 40.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205113.jpg: 640x480 1 cat, 51.0ms\n",
      "Speed: 4.0ms preprocess, 51.0ms inference, 7.7ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205119.jpg: 480x640 1 cat, 27.6ms\n",
      "Speed: 5.0ms preprocess, 27.6ms inference, 4.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205121.jpg: 480x640 1 cat, 29.0ms\n",
      "Speed: 4.0ms preprocess, 29.0ms inference, 5.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240623_192721.jpg: 640x480 2 cats, 1 dog, 35.8ms\n",
      "Speed: 6.0ms preprocess, 35.8ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240624_200545.jpg: 640x480 1 cat, 1 bottle, 1 potted plant, 46.0ms\n",
      "Speed: 4.0ms preprocess, 46.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240624_200548.jpg: 640x480 1 cat, 115.1ms\n",
      "Speed: 4.0ms preprocess, 115.1ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240624_200549.jpg: 640x480 1 cat, 9.0ms\n",
      "Speed: 2.0ms preprocess, 9.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012349.jpg: 640x480 1 cat, 10.0ms\n",
      "Speed: 2.0ms preprocess, 10.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012352.jpg: 640x480 1 cat, 12.0ms\n",
      "Speed: 2.0ms preprocess, 12.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012400.jpg: 640x480 1 cat, 21.3ms\n",
      "Speed: 3.0ms preprocess, 21.3ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012402.jpg: 640x480 1 cat, 11.0ms\n",
      "Speed: 3.0ms preprocess, 11.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012407.jpg: 640x480 1 cat, 15.0ms\n",
      "Speed: 5.0ms preprocess, 15.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012408.jpg: 640x480 1 cat, 35.0ms\n",
      "Speed: 5.0ms preprocess, 35.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_231410.jpg: 640x480 1 cat, 23.8ms\n",
      "Speed: 4.0ms preprocess, 23.8ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_231412.jpg: 640x480 1 cat, 25.0ms\n",
      "Speed: 6.0ms preprocess, 25.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_231413.jpg: 640x480 1 cat, 1 tv, 159.3ms\n",
      "Speed: 5.0ms preprocess, 159.3ms inference, 10.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240704_202652.jpg: 640x480 1 cat, 1 dog, 8.0ms\n",
      "Speed: 4.0ms preprocess, 8.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240711_221427.jpg: 640x480 1 cat, 19.0ms\n",
      "Speed: 3.0ms preprocess, 19.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240711_221435.jpg: 640x480 3 cats, 18.1ms\n",
      "Speed: 3.0ms preprocess, 18.1ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165019.jpg: 640x480 1 cat, 2 potted plants, 1 bed, 1 vase, 35.0ms\n",
      "Speed: 4.0ms preprocess, 35.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165021.jpg: 640x480 2 cats, 1 couch, 5 potted plants, 1 bed, 1 tv, 1 vase, 22.0ms\n",
      "Speed: 3.7ms preprocess, 22.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165025.jpg: 640x480 1 cat, 7 potted plants, 2 beds, 1 vase, 25.0ms\n",
      "Speed: 3.5ms preprocess, 25.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165028.jpg: 640x480 1 cat, 1 bottle, 2 cups, 1 chair, 3 potted plants, 1 bed, 1 vase, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165029.jpg: 640x480 1 cat, 3 potted plants, 1 bed, 3 vases, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165031.jpg: 640x480 1 cat, 6 potted plants, 1 bed, 3 vases, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165033.jpg: 640x480 2 cats, 1 cup, 1 chair, 2 potted plants, 1 tv, 1 keyboard, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165040.jpg: 640x480 1 cat, 1 chair, 2 potted plants, 1 bed, 1 vase, 26.3ms\n",
      "Speed: 5.0ms preprocess, 26.3ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_224858.jpg: 640x480 1 cat, 1 bed, 24.0ms\n",
      "Speed: 4.0ms preprocess, 24.0ms inference, 5.5ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_224902.jpg: 640x480 1 cat, 1 bed, 25.6ms\n",
      "Speed: 4.0ms preprocess, 25.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_224903.jpg: 640x480 1 cat, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_224905.jpg: 640x480 1 cat, 25.0ms\n",
      "Speed: 5.0ms preprocess, 25.0ms inference, 6.5ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240725_133737.jpg: 640x480 1 person, 1 cup, 77.5ms\n",
      "Speed: 4.0ms preprocess, 77.5ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240726_225449.jpg: 640x480 1 bench, 2 cats, 8.0ms\n",
      "Speed: 3.0ms preprocess, 8.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240727_085826.jpg: 640x480 1 person, 1 cat, 14.0ms\n",
      "Speed: 3.0ms preprocess, 14.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240802_203206.jpg: 640x480 1 cat, 9.0ms\n",
      "Speed: 3.0ms preprocess, 9.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102342.jpg: 480x640 2 persons, 1 cat, 11.0ms\n",
      "Speed: 3.0ms preprocess, 11.0ms inference, 2.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102347.jpg: 480x640 1 person, 2 cats, 10.3ms\n",
      "Speed: 3.0ms preprocess, 10.3ms inference, 2.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102351.jpg: 480x640 2 persons, 2 cats, 9.0ms\n",
      "Speed: 4.0ms preprocess, 9.0ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102354.jpg: 480x640 2 persons, 1 cat, 1 suitcase, 17.0ms\n",
      "Speed: 5.0ms preprocess, 17.0ms inference, 4.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102402.jpg: 480x640 1 cat, 1 umbrella, 11.0ms\n",
      "Speed: 4.6ms preprocess, 11.0ms inference, 3.4ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102407.jpg: 480x640 2 persons, 1 cat, 16.0ms\n",
      "Speed: 4.0ms preprocess, 16.0ms inference, 5.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102411.jpg: 480x640 1 person, 1 cat, 20.1ms\n",
      "Speed: 4.0ms preprocess, 20.1ms inference, 4.5ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102415.jpg: 480x640 1 person, 1 cat, 18.1ms\n",
      "Speed: 4.0ms preprocess, 18.1ms inference, 3.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_113806.jpg: 640x480 1 person, 1 cat, 2 suitcases, 27.0ms\n",
      "Speed: 4.0ms preprocess, 27.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_145117.jpg: 640x480 1 cat, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_145118.jpg: 640x480 1 cat, 41.0ms\n",
      "Speed: 9.1ms preprocess, 41.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171056.jpg: 640x480 1 cat, 1 chair, 3 books, 49.9ms\n",
      "Speed: 6.0ms preprocess, 49.9ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171058.jpg: 640x480 1 cat, 1 chair, 2 books, 25.0ms\n",
      "Speed: 3.0ms preprocess, 25.0ms inference, 5.3ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171100.jpg: 640x480 1 dog, 3 books, 111.5ms\n",
      "Speed: 3.0ms preprocess, 111.5ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171104.jpg: 640x480 1 cat, 1 suitcase, 1 chair, 4 books, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 5.5ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171110.jpg: 640x480 1 cat, 1 suitcase, 6 books, 24.0ms\n",
      "Speed: 5.0ms preprocess, 24.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171111.jpg: 640x480 1 cat, 1 chair, 3 books, 79.0ms\n",
      "Speed: 7.0ms preprocess, 79.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_203211.jpg: 640x480 2 cats, 24.0ms\n",
      "Speed: 4.0ms preprocess, 24.0ms inference, 5.6ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_203213.jpg: 640x480 2 cats, 1 dog, 25.5ms\n",
      "Speed: 4.0ms preprocess, 25.5ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_203217.jpg: 640x480 2 cats, 24.8ms\n",
      "Speed: 5.0ms preprocess, 24.8ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_203219.jpg: 640x480 2 cats, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_164652.jpg: 640x480 1 cat, 1 cake, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_164655.jpg: 640x480 2 cats, 24.3ms\n",
      "Speed: 5.0ms preprocess, 24.3ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_171436.jpg: 640x480 1 cat, 1 cup, 2 bowls, 1 book, 101.6ms\n",
      "Speed: 9.5ms preprocess, 101.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_171438.jpg: 640x480 1 cat, 2 bowls, 9.8ms\n",
      "Speed: 3.0ms preprocess, 9.8ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_171443.jpg: 640x480 1 cat, 2 bowls, 16.5ms\n",
      "Speed: 4.5ms preprocess, 16.5ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_173348.jpg: 640x480 1 cat, 10.0ms\n",
      "Speed: 2.0ms preprocess, 10.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_173355.jpg: 640x480 1 cat, 8.0ms\n",
      "Speed: 3.0ms preprocess, 8.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_173426.jpg: 480x640 1 cat, 13.0ms\n",
      "Speed: 3.0ms preprocess, 13.0ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211309.jpg: 640x480 2 cats, 43.0ms\n",
      "Speed: 5.0ms preprocess, 43.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211312.jpg: 640x480 1 cat, 62.0ms\n",
      "Speed: 4.0ms preprocess, 62.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211313.jpg: 640x480 1 cat, 23.0ms\n",
      "Speed: 4.0ms preprocess, 23.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211320.jpg: 640x480 1 cat, 59.0ms\n",
      "Speed: 5.0ms preprocess, 59.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211321.jpg: 640x480 1 cat, 37.5ms\n",
      "Speed: 5.0ms preprocess, 37.5ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211322.jpg: 640x480 1 cat, 11.0ms\n",
      "Speed: 3.0ms preprocess, 11.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211330.jpg: 640x480 2 cats, 3 chairs, 9.2ms\n",
      "Speed: 2.0ms preprocess, 9.2ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211333.jpg: 640x480 1 cat, 1 chair, 10.0ms\n",
      "Speed: 3.0ms preprocess, 10.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240820_144924.jpg: 640x480 1 bench, 1 cat, 10.5ms\n",
      "Speed: 2.0ms preprocess, 10.5ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240820_145200.jpg: 640x480 1 bench, 1 cat, 11.0ms\n",
      "Speed: 3.0ms preprocess, 11.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144759.jpg: 640x480 1 cat, 2 chairs, 1 book, 11.0ms\n",
      "Speed: 3.0ms preprocess, 11.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144801.jpg: 640x480 1 cat, 1 book, 15.0ms\n",
      "Speed: 4.0ms preprocess, 15.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144802.jpg: 640x480 (no detections), 24.0ms\n",
      "Speed: 4.0ms preprocess, 24.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144806.jpg: 640x480 1 cat, 1 dog, 1 tie, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144808.jpg: 640x480 1 cat, 1 dog, 24.9ms\n",
      "Speed: 4.0ms preprocess, 24.9ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144856.jpg: 640x480 1 person, 25.5ms\n",
      "Speed: 4.5ms preprocess, 25.5ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144935.jpg: 640x480 2 cats, 1 dog, 1 chair, 24.0ms\n",
      "Speed: 5.0ms preprocess, 24.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145212.jpg: 640x480 2 persons, 3 dogs, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145214.jpg: 640x480 2 persons, 2 cats, 1 chair, 26.1ms\n",
      "Speed: 4.0ms preprocess, 26.1ms inference, 3.9ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145834.jpg: 640x480 2 cats, 1 dog, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145836.jpg: 640x480 2 cats, 1 chair, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145837.jpg: 640x480 2 cats, 1 dog, 71.4ms\n",
      "Speed: 4.0ms preprocess, 71.4ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145839.jpg: 640x480 2 cats, 1 dog, 23.0ms\n",
      "Speed: 4.0ms preprocess, 23.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145844.jpg: 640x480 2 cats, 1 dog, 56.2ms\n",
      "Speed: 7.0ms preprocess, 56.2ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145846.jpg: 640x480 1 person, 1 cat, 1 dog, 1 suitcase, 13.7ms\n",
      "Speed: 3.0ms preprocess, 13.7ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145849.jpg: 640x480 2 cats, 1 chair, 8.0ms\n",
      "Speed: 3.0ms preprocess, 8.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150802.jpg: 640x480 1 teddy bear, 8.0ms\n",
      "Speed: 2.0ms preprocess, 8.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150805.jpg: 640x480 1 teddy bear, 11.0ms\n",
      "Speed: 3.0ms preprocess, 11.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150807.jpg: 480x640 1 cat, 9.0ms\n",
      "Speed: 4.1ms preprocess, 9.0ms inference, 2.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150810.jpg: 480x640 1 person, 2 cats, 9.0ms\n",
      "Speed: 3.0ms preprocess, 9.0ms inference, 2.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150813.jpg: 480x640 1 person, 1 cat, 1 couch, 21.0ms\n",
      "Speed: 3.0ms preprocess, 21.0ms inference, 5.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150852.jpg: 640x480 1 cat, 23.6ms\n",
      "Speed: 4.0ms preprocess, 23.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_152201.jpg: 640x480 2 cats, 1 dog, 25.0ms\n",
      "Speed: 5.0ms preprocess, 25.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_152205.jpg: 640x480 2 cats, 28.0ms\n",
      "Speed: 4.0ms preprocess, 28.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_152211.jpg: 640x480 2 cats, 25.0ms\n",
      "Speed: 5.0ms preprocess, 25.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_152212.jpg: 640x480 1 cat, 1 dog, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240827_224300.jpg: 640x480 1 cat, 1 couch, 129.6ms\n",
      "Speed: 6.0ms preprocess, 129.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240827_224303.jpg: 640x480 1 cat, 66.0ms\n",
      "Speed: 5.0ms preprocess, 66.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240828_102222.jpg: 640x480 1 cat, 1 chair, 10.0ms\n",
      "Speed: 3.0ms preprocess, 10.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240828_102229.jpg: 640x480 1 cat, 7.0ms\n",
      "Speed: 2.0ms preprocess, 7.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240903_211802.jpg: 640x480 1 cat, 9.0ms\n",
      "Speed: 2.5ms preprocess, 9.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240903_211803.jpg: 640x480 1 cat, 10.0ms\n",
      "Speed: 3.0ms preprocess, 10.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240903_211811.jpg: 640x480 1 cat, 11.0ms\n",
      "Speed: 3.0ms preprocess, 11.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240903_211818.jpg: 640x480 1 cat, 1 dog, 1 couch, 1 bed, 12.0ms\n",
      "Speed: 3.0ms preprocess, 12.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240903_211821.jpg: 640x480 1 person, 1 cat, 1 frisbee, 1 chair, 1 bed, 90.7ms\n",
      "Speed: 12.0ms preprocess, 90.7ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240907_115059.jpg: 480x640 1 cat, 1 couch, 1 bed, 20.2ms\n",
      "Speed: 4.0ms preprocess, 20.2ms inference, 3.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240907_115105.jpg: 480x640 1 cat, 2 beds, 19.0ms\n",
      "Speed: 5.0ms preprocess, 19.0ms inference, 5.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240907_115118.jpg: 480x640 1 cat, 1 bed, 20.0ms\n",
      "Speed: 4.0ms preprocess, 20.0ms inference, 4.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_190713.jpg: 640x480 1 cat, 2 beds, 28.0ms\n",
      "Speed: 4.0ms preprocess, 28.0ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_190829.jpg: 640x480 1 cat, 1 bed, 159.9ms\n",
      "Speed: 4.0ms preprocess, 159.9ms inference, 10.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_194734.jpg: 640x480 (no detections), 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_194737.jpg: 640x480 2 cats, 70.8ms\n",
      "Speed: 4.0ms preprocess, 70.8ms inference, 3.5ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_194739.jpg: 640x480 2 cats, 8.0ms\n",
      "Speed: 2.0ms preprocess, 8.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_194752.jpg: 640x480 1 cat, 1 bed, 10.0ms\n",
      "Speed: 3.0ms preprocess, 10.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_194753.jpg: 640x480 1 handbag, 1 bed, 14.0ms\n",
      "Speed: 3.0ms preprocess, 14.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240909_000642.jpg: 480x640 1 cat, 1 chair, 1 bed, 14.0ms\n",
      "Speed: 4.0ms preprocess, 14.0ms inference, 3.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240909_000646.jpg: 480x640 2 cats, 12.0ms\n",
      "Speed: 3.0ms preprocess, 12.0ms inference, 2.5ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124611.jpg: 640x480 1 cat, 1 bed, 27.3ms\n",
      "Speed: 4.0ms preprocess, 27.3ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124612.jpg: 640x480 1 cat, 1 dog, 1 bed, 12.0ms\n",
      "Speed: 3.0ms preprocess, 12.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124619.jpg: 640x480 1 person, 1 bed, 1 teddy bear, 62.9ms\n",
      "Speed: 5.0ms preprocess, 62.9ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124621.jpg: 640x480 1 person, 1 bed, 1 teddy bear, 41.0ms\n",
      "Speed: 4.0ms preprocess, 41.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124624.jpg: 640x480 1 dog, 24.0ms\n",
      "Speed: 4.0ms preprocess, 24.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124720.jpg: 640x480 1 bench, 1 cat, 1 bed, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124723.jpg: 640x480 1 cat, 74.1ms\n",
      "Speed: 5.0ms preprocess, 74.1ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124734.jpg: 640x480 1 cat, 2 beds, 25.0ms\n",
      "Speed: 5.0ms preprocess, 25.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124738.jpg: 640x480 1 cat, 1 bed, 25.0ms\n",
      "Speed: 4.6ms preprocess, 25.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124741.jpg: 640x480 1 cat, 1 couch, 1 bed, 26.7ms\n",
      "Speed: 4.0ms preprocess, 26.7ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091314.jpg: 640x480 1 cat, 39.0ms\n",
      "Speed: 5.0ms preprocess, 39.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091319.jpg: 640x480 1 cat, 39.0ms\n",
      "Speed: 5.0ms preprocess, 39.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091320.jpg: 640x480 1 cat, 25.2ms\n",
      "Speed: 3.0ms preprocess, 25.2ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091321.jpg: 640x480 (no detections), 24.5ms\n",
      "Speed: 4.9ms preprocess, 24.5ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091322.jpg: 640x480 1 cat, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 5.3ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091323.jpg: 640x480 1 cat, 142.5ms\n",
      "Speed: 6.0ms preprocess, 142.5ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091329.jpg: 480x640 1 cat, 21.0ms\n",
      "Speed: 4.7ms preprocess, 21.0ms inference, 2.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091845.jpg: 640x480 1 cat, 27.0ms\n",
      "Speed: 5.0ms preprocess, 27.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091846.jpg: 640x480 1 cat, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091848.jpg: 640x480 1 bird, 1 cat, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091850.jpg: 640x480 1 cat, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 3.7ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091852.jpg: 640x480 1 bird, 1 cat, 26.0ms\n",
      "Speed: 4.0ms preprocess, 26.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091859.jpg: 640x480 1 cat, 25.1ms\n",
      "Speed: 4.0ms preprocess, 25.1ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091902.jpg: 640x480 1 cat, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091905.jpg: 640x480 1 cat, 25.6ms\n",
      "Speed: 4.1ms preprocess, 25.6ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_193740.jpg: 640x480 1 cat, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_193742.jpg: 640x480 1 cat, 24.6ms\n",
      "Speed: 4.0ms preprocess, 24.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174138.jpg: 640x480 1 chair, 1 bed, 1 mouse, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174140.jpg: 640x480 2 surfboards, 40.5ms\n",
      "Speed: 3.0ms preprocess, 40.5ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174204.jpg: 640x480 1 cat, 1 toilet, 98.7ms\n",
      "Speed: 5.0ms preprocess, 98.7ms inference, 10.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174614.jpg: 640x480 2 cats, 1 laptop, 76.9ms\n",
      "Speed: 4.0ms preprocess, 76.9ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174616.jpg: 640x480 1 cat, 20.4ms\n",
      "Speed: 4.0ms preprocess, 20.4ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174617.jpg: 640x480 1 person, 26.0ms\n",
      "Speed: 3.0ms preprocess, 26.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174620.jpg: 640x480 1 person, 146.7ms\n",
      "Speed: 5.0ms preprocess, 146.7ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174622.jpg: 640x480 1 person, 11.0ms\n",
      "Speed: 3.0ms preprocess, 11.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174636.jpg: 640x480 1 person, 1 cat, 12.0ms\n",
      "Speed: 3.0ms preprocess, 12.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_215106.jpg: 640x480 1 cat, 1 bed, 11.0ms\n",
      "Speed: 3.0ms preprocess, 11.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_215113.jpg: 640x480 1 person, 1 cat, 1 bottle, 1 chair, 1 bed, 1 tv, 11.0ms\n",
      "Speed: 2.0ms preprocess, 11.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_215119.jpg: 640x480 1 cat, 1 bottle, 1 bed, 1 tv, 15.0ms\n",
      "Speed: 3.0ms preprocess, 15.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240920_192026.jpg: 640x480 1 cat, 16.0ms\n",
      "Speed: 3.0ms preprocess, 16.0ms inference, 5.7ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240924_111824.jpg: 480x640 1 cat, 20.0ms\n",
      "Speed: 4.0ms preprocess, 20.0ms inference, 5.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240924_111829.jpg: 480x640 1 cat, 22.5ms\n",
      "Speed: 3.0ms preprocess, 22.5ms inference, 4.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240924_111831.jpg: 480x640 1 cat, 1 chair, 32.4ms\n",
      "Speed: 4.0ms preprocess, 32.4ms inference, 5.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162434.jpg: 640x480 2 cats, 1 potted plant, 34.0ms\n",
      "Speed: 7.0ms preprocess, 34.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162437.jpg: 640x480 1 cat, 1 potted plant, 47.5ms\n",
      "Speed: 5.0ms preprocess, 47.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162442.jpg: 640x480 1 person, 1 cat, 34.0ms\n",
      "Speed: 5.0ms preprocess, 34.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162443.jpg: 640x480 1 person, 1 cat, 25.0ms\n",
      "Speed: 5.0ms preprocess, 25.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162445.jpg: 640x480 1 person, 1 cat, 256.1ms\n",
      "Speed: 4.0ms preprocess, 256.1ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162452.jpg: 640x480 1 person, 1 cat, 13.0ms\n",
      "Speed: 4.0ms preprocess, 13.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162458.jpg: 640x480 1 cat, 2 chairs, 2 potted plants, 16.7ms\n",
      "Speed: 4.0ms preprocess, 16.7ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162500.jpg: 640x480 1 cat, 13.5ms\n",
      "Speed: 3.0ms preprocess, 13.5ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162503.jpg: 640x480 1 cat, 1 chair, 2 potted plants, 22.0ms\n",
      "Speed: 6.0ms preprocess, 22.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_100748.jpg: 480x640 2 persons, 1 cat, 19.0ms\n",
      "Speed: 4.0ms preprocess, 19.0ms inference, 4.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_101009.jpg: 640x480 1 cat, 33.0ms\n",
      "Speed: 4.0ms preprocess, 33.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_101010.jpg: 640x480 1 dog, 38.0ms\n",
      "Speed: 4.0ms preprocess, 38.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_101012.jpg: 640x480 1 umbrella, 44.5ms\n",
      "Speed: 4.0ms preprocess, 44.5ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_101014.jpg: 640x480 1 cat, 59.4ms\n",
      "Speed: 4.0ms preprocess, 59.4ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_101016.jpg: 640x480 1 cat, 32.0ms\n",
      "Speed: 4.0ms preprocess, 32.0ms inference, 44.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_184523.jpg: 480x640 1 cat, 25.0ms\n",
      "Speed: 5.0ms preprocess, 25.0ms inference, 4.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240930_151252.jpg: 640x480 3 persons, 1 dog, 1 teddy bear, 37.7ms\n",
      "Speed: 4.0ms preprocess, 37.7ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240930_151259.jpg: 640x480 1 cat, 43.7ms\n",
      "Speed: 4.0ms preprocess, 43.7ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241002_212916.jpg: 640x480 2 cats, 58.8ms\n",
      "Speed: 5.0ms preprocess, 58.8ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241002_212925.jpg: 640x480 2 cats, 1 handbag, 54.4ms\n",
      "Speed: 4.0ms preprocess, 54.4ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241002_212949.jpg: 640x480 2 cats, 47.6ms\n",
      "Speed: 4.0ms preprocess, 47.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241004_112619.jpg: 640x480 1 cat, 1 bowl, 1 potted plant, 54.0ms\n",
      "Speed: 4.0ms preprocess, 54.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241004_112626.jpg: 640x480 1 cat, 1 bowl, 38.8ms\n",
      "Speed: 6.6ms preprocess, 38.8ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241008_094705.jpg: 640x480 1 cat, 1 bowl, 39.0ms\n",
      "Speed: 4.5ms preprocess, 39.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241008_094939.jpg: 640x480 1 cat, 1 dog, 1 couch, 49.5ms\n",
      "Speed: 4.0ms preprocess, 49.5ms inference, 6.8ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241008_094940.jpg: 640x480 2 cats, 32.0ms\n",
      "Speed: 4.0ms preprocess, 32.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241009_121555.jpg: 640x480 2 beds, 67.6ms\n",
      "Speed: 8.7ms preprocess, 67.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241009_121558.jpg: 640x480 1 cat, 1 suitcase, 67.9ms\n",
      "Speed: 4.0ms preprocess, 67.9ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241009_121601.jpg: 640x480 2 persons, 34.0ms\n",
      "Speed: 4.0ms preprocess, 34.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191519.jpg: 640x480 1 cat, 94.8ms\n",
      "Speed: 5.0ms preprocess, 94.8ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191520.jpg: 640x480 1 cat, 65.0ms\n",
      "Speed: 5.0ms preprocess, 65.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191523.jpg: 640x480 1 cat, 66.0ms\n",
      "Speed: 5.0ms preprocess, 66.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191525.jpg: 640x480 1 cat, 101.4ms\n",
      "Speed: 4.0ms preprocess, 101.4ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191527.jpg: 640x480 1 cat, 1 sink, 19.0ms\n",
      "Speed: 4.0ms preprocess, 19.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191529.jpg: 640x480 1 cat, 28.6ms\n",
      "Speed: 7.0ms preprocess, 28.6ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_132210.jpg: 640x480 1 cat, 13.0ms\n",
      "Speed: 4.0ms preprocess, 13.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_132216.jpg: 640x480 1 cat, 20.0ms\n",
      "Speed: 3.0ms preprocess, 20.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_132218.jpg: 640x480 1 dog, 18.5ms\n",
      "Speed: 5.0ms preprocess, 18.5ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_184948.jpg: 640x480 1 cat, 23.0ms\n",
      "Speed: 4.0ms preprocess, 23.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_184949.jpg: 640x480 1 cat, 1 bowl, 70.8ms\n",
      "Speed: 6.0ms preprocess, 70.8ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241022_023307.jpg: 640x480 1 cat, 1 bottle, 65.0ms\n",
      "Speed: 4.0ms preprocess, 65.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241024_134452.jpg: 640x480 1 cat, 1 bed, 21.0ms\n",
      "Speed: 5.0ms preprocess, 21.0ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241101_145241.jpg: 640x480 1 cat, 1 bed, 27.0ms\n",
      "Speed: 4.0ms preprocess, 27.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241115_195434.jpg: 640x480 1 person, 1 cat, 1 chair, 114.2ms\n",
      "Speed: 6.0ms preprocess, 114.2ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241115_195439.jpg: 640x480 1 person, 1 cat, 1 chair, 56.0ms\n",
      "Speed: 4.0ms preprocess, 56.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241115_200624.jpg: 640x480 2 persons, 2 cats, 1 bowl, 1 bed, 103.2ms\n",
      "Speed: 6.0ms preprocess, 103.2ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241115_200626.jpg: 640x480 1 person, 1 cat, 24.0ms\n",
      "Speed: 4.0ms preprocess, 24.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241124_214430.jpg: 640x480 1 bottle, 1 bed, 21.0ms\n",
      "Speed: 4.0ms preprocess, 21.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241124_220257.jpg: 640x480 1 cat, 1 bowl, 1 bed, 117.5ms\n",
      "Speed: 5.0ms preprocess, 117.5ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241124_220259.jpg: 640x480 1 cat, 1 bed, 1 dining table, 11.0ms\n",
      "Speed: 4.0ms preprocess, 11.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241129_085724.jpg: 640x480 1 bench, 13.0ms\n",
      "Speed: 4.0ms preprocess, 13.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241206_164037.jpg: 640x480 1 person, 1 cat, 10.0ms\n",
      "Speed: 3.0ms preprocess, 10.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241206_164040.jpg: 640x480 2 cats, 12.0ms\n",
      "Speed: 2.0ms preprocess, 12.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241217_102418.jpg: 640x480 1 cat, 1 bowl, 15.0ms\n",
      "Speed: 5.0ms preprocess, 15.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132045.jpg: 640x480 1 cat, 1 bed, 16.0ms\n",
      "Speed: 3.0ms preprocess, 16.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132051.jpg: 640x480 2 cats, 42.0ms\n",
      "Speed: 6.0ms preprocess, 42.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132058.jpg: 640x480 2 cats, 69.5ms\n",
      "Speed: 5.5ms preprocess, 69.5ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132101.jpg: 480x640 2 cats, 55.1ms\n",
      "Speed: 4.0ms preprocess, 55.1ms inference, 5.0ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132106.jpg: 480x640 1 cat, 17.6ms\n",
      "Speed: 6.0ms preprocess, 17.6ms inference, 4.5ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241224_180740.jpg: 640x480 (no detections), 43.2ms\n",
      "Speed: 4.0ms preprocess, 43.2ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_130054.jpg: 640x480 1 cat, 1 bowl, 1 couch, 1 bed, 62.9ms\n",
      "Speed: 4.6ms preprocess, 62.9ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134500.jpg: 640x480 1 cat, 1 toilet, 49.9ms\n",
      "Speed: 4.0ms preprocess, 49.9ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134517.jpg: 640x480 1 bear, 1 toilet, 37.5ms\n",
      "Speed: 6.0ms preprocess, 37.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134521.jpg: 640x480 1 person, 59.3ms\n",
      "Speed: 6.0ms preprocess, 59.3ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134625.jpg: 640x480 (no detections), 176.1ms\n",
      "Speed: 5.0ms preprocess, 176.1ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134627.jpg: 640x480 1 cat, 10.0ms\n",
      "Speed: 3.0ms preprocess, 10.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134634.jpg: 640x480 1 dog, 10.0ms\n",
      "Speed: 3.0ms preprocess, 10.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134635.jpg: 640x480 2 teddy bears, 15.1ms\n",
      "Speed: 3.0ms preprocess, 15.1ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135201.jpg: 640x480 1 cat, 12.0ms\n",
      "Speed: 3.0ms preprocess, 12.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135214.jpg: 640x480 (no detections), 15.0ms\n",
      "Speed: 3.2ms preprocess, 15.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135215.jpg: 640x480 1 dog, 1 chair, 36.9ms\n",
      "Speed: 4.0ms preprocess, 36.9ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135219.jpg: 640x480 1 person, 1 bicycle, 1 cat, 1 dog, 58.0ms\n",
      "Speed: 5.0ms preprocess, 58.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135343(0).jpg: 640x480 1 cat, 3 chairs, 47.0ms\n",
      "Speed: 4.0ms preprocess, 47.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135343.jpg: 640x480 2 cats, 1 chair, 59.6ms\n",
      "Speed: 3.0ms preprocess, 59.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135345.jpg: 640x480 1 cat, 1 chair, 72.6ms\n",
      "Speed: 4.0ms preprocess, 72.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135347.jpg: 640x480 2 cats, 1 dog, 58.0ms\n",
      "Speed: 4.0ms preprocess, 58.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135352.jpg: 640x480 1 cat, 42.0ms\n",
      "Speed: 4.0ms preprocess, 42.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135354.jpg: 640x480 1 cat, 65.0ms\n",
      "Speed: 6.0ms preprocess, 65.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135707.jpg: 640x480 3 chairs, 1 bed, 104.3ms\n",
      "Speed: 6.0ms preprocess, 104.3ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135708.jpg: 640x480 2 chairs, 12.1ms\n",
      "Speed: 3.0ms preprocess, 12.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135710.jpg: 640x480 1 cat, 2 chairs, 16.4ms\n",
      "Speed: 3.0ms preprocess, 16.4ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135712.jpg: 640x480 1 cat, 1 chair, 13.1ms\n",
      "Speed: 4.0ms preprocess, 13.1ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_140923.jpg: 640x480 (no detections), 11.0ms\n",
      "Speed: 3.0ms preprocess, 11.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_140924.jpg: 640x480 2 persons, 14.0ms\n",
      "Speed: 3.0ms preprocess, 14.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241227_150819.jpg: 640x480 1 person, 1 cat, 1 tv, 17.0ms\n",
      "Speed: 3.0ms preprocess, 17.0ms inference, 4.4ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241227_150826.jpg: 640x480 1 person, 1 cat, 1 chair, 1 tv, 1 laptop, 15.0ms\n",
      "Speed: 4.0ms preprocess, 15.0ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250105_143721.jpg: 640x480 2 cats, 2 chairs, 43.0ms\n",
      "Speed: 6.0ms preprocess, 43.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250105_143723.jpg: 640x480 1 cat, 31.0ms\n",
      "Speed: 5.0ms preprocess, 31.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250105_143727.jpg: 640x480 2 cats, 42.6ms\n",
      "Speed: 5.0ms preprocess, 42.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250112_203850.jpg: 640x480 2 cats, 1 sports ball, 59.5ms\n",
      "Speed: 6.0ms preprocess, 59.5ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250112_203857.jpg: 640x480 2 persons, 1 cat, 57.4ms\n",
      "Speed: 4.0ms preprocess, 57.4ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250112_203858.jpg: 640x480 1 cat, 1 teddy bear, 38.0ms\n",
      "Speed: 4.0ms preprocess, 38.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250112_203900.jpg: 640x480 1 dog, 1 book, 66.0ms\n",
      "Speed: 6.0ms preprocess, 66.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250112_203903.jpg: 640x480 1 sheep, 1 sports ball, 55.5ms\n",
      "Speed: 6.0ms preprocess, 55.5ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174816.jpg: 640x480 1 person, 1 cat, 43.0ms\n",
      "Speed: 4.0ms preprocess, 43.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174818.jpg: 640x480 (no detections), 36.4ms\n",
      "Speed: 4.0ms preprocess, 36.4ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174833.jpg: 640x480 1 person, 1 teddy bear, 45.0ms\n",
      "Speed: 5.0ms preprocess, 45.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174904.jpg: 640x480 1 person, 1 chair, 1 tv, 1 clock, 1 teddy bear, 40.1ms\n",
      "Speed: 4.0ms preprocess, 40.1ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174905.jpg: 640x480 1 person, 1 chair, 1 tv, 2 teddy bears, 39.5ms\n",
      "Speed: 4.0ms preprocess, 39.5ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174908.jpg: 640x480 1 person, 1 tv, 1 teddy bear, 60.6ms\n",
      "Speed: 5.0ms preprocess, 60.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174912.jpg: 640x480 1 person, 2 chairs, 1 dining table, 1 teddy bear, 55.0ms\n",
      "Speed: 7.0ms preprocess, 55.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174913.jpg: 640x480 1 person, 1 chair, 1 teddy bear, 60.2ms\n",
      "Speed: 4.0ms preprocess, 60.2ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174915.jpg: 640x480 1 person, 2 chairs, 1 teddy bear, 68.1ms\n",
      "Speed: 5.6ms preprocess, 68.1ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174920.jpg: 640x480 1 person, 3 chairs, 1 tv, 41.0ms\n",
      "Speed: 4.0ms preprocess, 41.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174949(0).jpg: 640x480 1 person, 1 couch, 1 bed, 46.5ms\n",
      "Speed: 5.0ms preprocess, 46.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174949.jpg: 640x480 1 person, 1 couch, 1 bed, 1 book, 53.6ms\n",
      "Speed: 5.0ms preprocess, 53.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_175124.jpg: 640x480 (no detections), 53.0ms\n",
      "Speed: 4.0ms preprocess, 53.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250117_172955.jpg: 640x480 1 person, 1 cat, 1 bed, 52.1ms\n",
      "Speed: 4.0ms preprocess, 52.1ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250120_225956.jpg: 640x480 1 cat, 49.8ms\n",
      "Speed: 5.0ms preprocess, 49.8ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250121_190039.jpg: 640x480 1 cat, 1 dog, 62.0ms\n",
      "Speed: 6.0ms preprocess, 62.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250121_190043.jpg: 640x480 2 cats, 1 chair, 65.4ms\n",
      "Speed: 6.0ms preprocess, 65.4ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250121_190046.jpg: 640x480 1 dog, 1 chair, 3 books, 11.0ms\n",
      "Speed: 2.0ms preprocess, 11.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142944.jpg: 640x480 3 cats, 1 bed, 11.5ms\n",
      "Speed: 3.0ms preprocess, 11.5ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142946.jpg: 640x480 4 cats, 1 bed, 14.0ms\n",
      "Speed: 4.0ms preprocess, 14.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142948.jpg: 640x480 3 cats, 2 beds, 10.0ms\n",
      "Speed: 3.0ms preprocess, 10.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142949.jpg: 640x480 2 cats, 1 bed, 18.0ms\n",
      "Speed: 3.0ms preprocess, 18.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142953.jpg: 640x480 2 cats, 1 potted plant, 1 bed, 17.0ms\n",
      "Speed: 5.0ms preprocess, 17.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142958.jpg: 640x480 1 cat, 2 beds, 28.0ms\n",
      "Speed: 4.0ms preprocess, 28.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_191929.jpg: 640x480 1 dog, 2 books, 34.3ms\n",
      "Speed: 4.0ms preprocess, 34.3ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\IMG-20240730-WA0005.jpg: 640x480 2 persons, 1 couch, 45.4ms\n",
      "Speed: 5.0ms preprocess, 45.4ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\IMG_20240318_231934_252.webp: 640x384 1 cat, 34.5ms\n",
      "Speed: 3.0ms preprocess, 34.5ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 384)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\IMG_20241207_211456_238.jpg: 640x480 1 person, 1 cat, 1 bed, 63.9ms\n",
      "Speed: 5.0ms preprocess, 63.9ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\IMG_20241229_221404_122.jpg: 640x512 1 person, 2 dogs, 81.0ms\n",
      "Speed: 6.0ms preprocess, 81.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 512)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n",
      "Processed all images. YOLO format bounding boxes saved to output/\n"
     ]
    }
   ],
   "source": [
    "# Iterate through images in the dataset folder\n",
    "for image_name in os.listdir(images_folder):\n",
    "    image_path = os.path.join(images_folder, image_name)\n",
    "    image = cv2.imread(image_path)\n",
    "\n",
    "    # Run YOLO inference\n",
    "    results = model.predict(source=image_path, save=True)\n",
    "\n",
    "    # Prepare a list to collect bounding box data for YOLO format\n",
    "    bounding_boxes = []\n",
    "\n",
    "    # Get the image dimensions for normalization\n",
    "    image_height, image_width, _ = image.shape\n",
    "\n",
    "    # Iterate over the results to get bounding boxes\n",
    "    for result in results:\n",
    "        boxes = result.boxes  # List of detected boxes\n",
    "\n",
    "        for box in boxes:\n",
    "            # Get coordinates, class id, and confidence score\n",
    "            x_min, y_min, x_max, y_max = map(int, box.xyxy[0])  # Bounding box coordinates\n",
    "            class_id = int(box.cls[0])  # Class id\n",
    "\n",
    "            # Store the bounding box details\n",
    "            bounding_boxes.append({\n",
    "                \"class_id\": class_id,\n",
    "                \"x_min\": x_min,\n",
    "                \"y_min\": y_min,\n",
    "                \"x_max\": x_max,\n",
    "                \"y_max\": y_max\n",
    "            })\n",
    "\n",
    "    # Save the bounding boxes to a YOLO format .txt file\n",
    "    yolo_txt_filename = os.path.join(output_folder, f\"{image_name.split('.')[0]}.txt\")\n",
    "    save_yolo_format(bounding_boxes, yolo_txt_filename, image_width, image_height)\n",
    "\n",
    "    # Optionally: Draw bounding boxes on the image (to save as visual feedback)\n",
    "    for box in bounding_boxes:\n",
    "        x_min, y_min, x_max, y_max = box[\"x_min\"], box[\"y_min\"], box[\"x_max\"], box[\"y_max\"]\n",
    "        cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)\n",
    "\n",
    "print(f\"Processed all images. YOLO format bounding boxes saved to {output_folder}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\1818949000-IMG-20240118-WA0001.jpg: 640x384 1 cat, 1 chair, 28.4ms\n",
      "image 2/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_103335.jpg: 640x480 1 cat, 1 bear, 2 chairs, 84.9ms\n",
      "image 3/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_141622.jpg: 640x480 1 person, 2 cats, 145.9ms\n",
      "image 4/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_184600.jpg: 640x480 1 person, 1 cat, 1 remote, 1 book, 1 teddy bear, 77.2ms\n",
      "image 5/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_195833.jpg: 640x480 1 cat, 1 bed, 56.5ms\n",
      "image 6/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_200052.jpg: 640x480 1 person, 1 cat, 68.0ms\n",
      "image 7/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_200057.jpg: 640x480 1 cat, 1 bed, 48.0ms\n",
      "image 8/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_200102.jpg: 640x480 1 person, 1 cat, 1 bed, 41.0ms\n",
      "image 9/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201439.jpg: 640x480 1 cat, 49.0ms\n",
      "image 10/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201442.jpg: 640x480 1 cat, 1 bottle, 73.7ms\n",
      "image 11/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201443.jpg: 640x480 1 cat, 1 bottle, 66.1ms\n",
      "image 12/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201446.jpg: 640x480 1 cat, 1 bed, 73.9ms\n",
      "image 13/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201447.jpg: 640x480 1 cat, 1 dog, 67.5ms\n",
      "image 14/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201448.jpg: 640x480 1 cat, 1 dog, 17.0ms\n",
      "image 15/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201452.jpg: 640x480 1 cat, 1 handbag, 24.8ms\n",
      "image 16/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201454.jpg: 640x480 1 cat, 1 dog, 28.0ms\n",
      "image 17/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201458.jpg: 640x480 1 cat, 1 handbag, 25.0ms\n",
      "image 18/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201503.jpg: 640x480 1 dog, 1 handbag, 33.0ms\n",
      "image 19/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201510.jpg: 640x480 1 cat, 1 handbag, 25.0ms\n",
      "image 20/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_232857.jpg: 640x480 1 cat, 1 suitcase, 27.0ms\n",
      "image 21/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240310_140207.jpg: 640x480 1 cat, 97.6ms\n",
      "image 22/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240311_195350.jpg: 640x480 1 cat, 1 handbag, 1 bed, 17.0ms\n",
      "image 23/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240311_195352.jpg: 640x480 1 cat, 10.0ms\n",
      "image 24/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240311_195357.jpg: 640x480 1 cat, 1 chair, 16.0ms\n",
      "image 25/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240311_195403.jpg: 640x480 1 cat, 14.0ms\n",
      "image 26/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_180219.jpg: 640x480 1 cat, 1 couch, 13.8ms\n",
      "image 27/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185655.jpg: 640x480 1 cat, 1 toilet, 11.0ms\n",
      "image 28/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185658.jpg: 640x480 2 cats, 17.0ms\n",
      "image 29/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185700.jpg: 640x480 4 cats, 1 bowl, 12.0ms\n",
      "image 30/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185702.jpg: 640x480 2 cats, 13.0ms\n",
      "image 31/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185718(0).jpg: 640x480 2 cats, 1 bowl, 1 chair, 11.0ms\n",
      "image 32/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185718.jpg: 640x480 2 cats, 1 bowl, 11.0ms\n",
      "image 33/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185719.jpg: 640x480 1 cat, 14.0ms\n",
      "image 34/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185729.jpg: 640x480 1 cat, 21.0ms\n",
      "image 35/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185730.jpg: 640x480 1 cat, 1 chair, 18.0ms\n",
      "image 36/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185733.jpg: 640x480 1 cat, 18.5ms\n",
      "image 37/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185734.jpg: 640x480 1 cat, 1 chair, 14.0ms\n",
      "image 38/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240313_203246.jpg: 640x480 1 person, 1 cat, 1 bed, 63.7ms\n",
      "image 39/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240313_203248.jpg: 640x480 1 cat, 40.4ms\n",
      "image 40/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_170048.jpg: 640x480 1 cat, 14.8ms\n",
      "image 41/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_170048_remastered.jpg: 640x480 1 cat, 1 dog, 15.0ms\n",
      "image 42/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_171004.jpg: 640x480 1 cat, 18.0ms\n",
      "image 43/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_171030.jpg: 640x480 1 dog, 19.2ms\n",
      "image 44/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191222.jpg: 640x480 1 cat, 1 chair, 20.5ms\n",
      "image 45/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191224(0).jpg: 640x480 1 cat, 1 umbrella, 22.0ms\n",
      "image 46/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191224.jpg: 640x480 1 cat, 1 bottle, 23.0ms\n",
      "image 47/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191225.jpg: 640x480 1 cat, 25.0ms\n",
      "image 48/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191226.jpg: 640x480 1 cat, 24.0ms\n",
      "image 49/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191227.jpg: 640x480 1 cat, 39.5ms\n",
      "image 50/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104313.jpg: 640x480 1 cat, 1 bottle, 29.0ms\n",
      "image 51/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104328.jpg: 640x480 1 cat, 1 bottle, 1 bowl, 29.7ms\n",
      "image 52/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104330.jpg: 640x480 1 cat, 1 bottle, 1 bowl, 25.0ms\n",
      "image 53/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104331.jpg: 640x480 1 cat, 25.0ms\n",
      "image 54/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104334.jpg: 640x480 1 person, 1 dog, 1 tv, 26.6ms\n",
      "image 55/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_220146.jpg: 640x480 1 person, 2 dogs, 25.7ms\n",
      "image 56/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_220153.jpg: 640x480 2 cats, 146.7ms\n",
      "image 57/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_220210.jpg: 640x480 1 person, 3 cats, 13.0ms\n",
      "image 58/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002355.jpg: 640x480 1 cat, 11.0ms\n",
      "image 59/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002404.jpg: 640x480 1 cat, 14.0ms\n",
      "image 60/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002407.jpg: 640x480 1 cat, 19.1ms\n",
      "image 61/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002416.jpg: 640x480 1 cat, 11.0ms\n",
      "image 62/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002419.jpg: 640x480 1 cat, 16.1ms\n",
      "image 63/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204422.jpg: 640x480 1 person, 1 cat, 1 bed, 14.0ms\n",
      "image 64/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204426.jpg: 640x480 1 cat, 1 bear, 1 chair, 8.0ms\n",
      "image 65/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204429.jpg: 640x480 1 cat, 1 chair, 13.0ms\n",
      "image 66/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204431.jpg: 640x480 1 cat, 15.0ms\n",
      "image 67/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204434.jpg: 640x480 1 cat, 1 bed, 10.0ms\n",
      "image 68/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204436.jpg: 640x480 1 cat, 1 elephant, 9.0ms\n",
      "image 69/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204501.jpg: 640x480 1 bear, 12.0ms\n",
      "image 70/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204503.jpg: 640x480 1 cat, 10.0ms\n",
      "image 71/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240318_194443.jpg: 640x480 1 cat, 1 bed, 1 teddy bear, 14.5ms\n",
      "image 72/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240320_171347.jpg: 640x480 1 bench, 1 cat, 11.0ms\n",
      "image 73/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185912(0).jpg: 640x480 2 teddy bears, 14.0ms\n",
      "image 74/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185912.jpg: 640x480 1 cat, 1 teddy bear, 15.7ms\n",
      "image 75/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185914.jpg: 640x480 1 cat, 1 teddy bear, 14.0ms\n",
      "image 76/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185916.jpg: 640x480 1 cat, 19.0ms\n",
      "image 77/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185917.jpg: 640x480 1 cat, 12.0ms\n",
      "image 78/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185921.jpg: 640x480 1 cat, 13.0ms\n",
      "image 79/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185922.jpg: 640x480 1 cat, 16.8ms\n",
      "image 80/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185924.jpg: 640x480 1 cat, 16.0ms\n",
      "image 81/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185950.jpg: 480x640 1 cat, 17.0ms\n",
      "image 82/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_190008.jpg: 480x640 1 dog, 1 bed, 25.0ms\n",
      "image 83/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_190014.jpg: 480x640 1 cat, 1 dog, 27.0ms\n",
      "image 84/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193424.jpg: 640x480 2 benchs, 1 cat, 1 dining table, 58.7ms\n",
      "image 85/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193425.jpg: 640x480 1 bench, 54.0ms\n",
      "image 86/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193430.jpg: 640x480 1 dog, 45.0ms\n",
      "image 87/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193431.jpg: 640x480 1 bench, 1 cat, 161.7ms\n",
      "image 88/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193433.jpg: 640x480 1 bench, 1 cat, 13.0ms\n",
      "image 89/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193448.jpg: 640x480 1 surfboard, 13.4ms\n",
      "image 90/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193501.jpg: 640x480 (no detections), 13.2ms\n",
      "image 91/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175352.jpg: 640x480 1 cat, 13.0ms\n",
      "image 92/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175354.jpg: 640x480 1 cat, 25.0ms\n",
      "image 93/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175358.jpg: 640x480 1 cat, 30.5ms\n",
      "image 94/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175402.jpg: 640x480 1 cat, 21.0ms\n",
      "image 95/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175405.jpg: 640x480 1 cat, 28.0ms\n",
      "image 96/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175406.jpg: 640x480 1 cat, 22.0ms\n",
      "image 97/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175407.jpg: 640x480 1 cat, 23.0ms\n",
      "image 98/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240325_163728.jpg: 640x480 2 cats, 1 laptop, 38.0ms\n",
      "image 99/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_131427.jpg: 640x480 1 cat, 3 chairs, 5 potted plants, 3 vases, 31.0ms\n",
      "image 100/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_131431.jpg: 640x480 1 cat, 1 suitcase, 49.6ms\n",
      "image 101/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183749.jpg: 640x480 1 cat, 42.0ms\n",
      "image 102/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183757.jpg: 640x480 1 cat, 59.5ms\n",
      "image 103/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183759.jpg: 640x480 1 cat, 43.2ms\n",
      "image 104/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183803.jpg: 640x480 1 cat, 33.0ms\n",
      "image 105/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183810.jpg: 480x640 1 cat, 40.0ms\n",
      "image 106/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135547.jpg: 640x480 1 cat, 35.2ms\n",
      "image 107/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135550.jpg: 640x480 1 cat, 17.0ms\n",
      "image 108/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135553.jpg: 640x480 1 cat, 1 bed, 18.0ms\n",
      "image 109/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135555.jpg: 640x480 1 cat, 1 couch, 1 tv, 15.0ms\n",
      "image 110/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135643.jpg: 640x480 1 cat, 29.6ms\n",
      "image 111/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240329_223942(0).jpg: 640x480 1 cat, 12.0ms\n",
      "image 112/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240329_223942.jpg: 640x480 1 cat, 1 chair, 13.0ms\n",
      "image 113/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240329_223945.jpg: 640x480 1 cat, 14.0ms\n",
      "image 114/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224228.jpg: 640x480 1 cat, 37.0ms\n",
      "image 115/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224230.jpg: 640x480 1 cat, 1 dining table, 33.0ms\n",
      "image 116/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224232.jpg: 640x480 2 cats, 23.0ms\n",
      "image 117/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224233.jpg: 640x480 1 cat, 24.0ms\n",
      "image 118/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224236.jpg: 640x480 1 cat, 21.0ms\n",
      "image 119/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224237.jpg: 640x480 1 cat, 23.0ms\n",
      "image 120/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224242.jpg: 640x480 1 cat, 27.0ms\n",
      "image 121/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152307.jpg: 640x480 1 cat, 1 chair, 19.9ms\n",
      "image 122/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152308.jpg: 640x480 1 cat, 1 chair, 24.0ms\n",
      "image 123/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152310.jpg: 640x480 1 cat, 27.0ms\n",
      "image 124/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152316.jpg: 640x480 1 cat, 20.0ms\n",
      "image 125/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152338.jpg: 640x480 1 cat, 27.9ms\n",
      "image 126/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152341.jpg: 640x480 1 cat, 1 chair, 29.0ms\n",
      "image 127/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233450.jpg: 640x480 2 chairs, 1 book, 41.0ms\n",
      "image 128/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233452.jpg: 640x480 1 chair, 62.0ms\n",
      "image 129/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233500.jpg: 640x480 1 cat, 26.0ms\n",
      "image 130/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233501.jpg: 640x480 1 cat, 1 bed, 25.0ms\n",
      "image 131/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233502.jpg: 640x480 1 cat, 34.0ms\n",
      "image 132/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233503.jpg: 640x480 1 bench, 1 cat, 28.0ms\n",
      "image 133/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233504.jpg: 640x480 1 cat, 29.0ms\n",
      "image 134/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233505.jpg: 640x480 1 cat, 98.1ms\n",
      "image 135/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233506.jpg: 640x480 1 cat, 23.0ms\n",
      "image 136/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233553.jpg: 480x640 (no detections), 20.5ms\n",
      "image 137/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233600.jpg: 480x640 1 cat, 15.6ms\n",
      "image 138/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233601.jpg: 480x640 (no detections), 13.0ms\n",
      "image 139/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233605.jpg: 480x640 (no detections), 14.0ms\n",
      "image 140/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233736.jpg: 640x480 1 bench, 1 apple, 1 bed, 25.2ms\n",
      "image 141/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233740.jpg: 640x480 1 bench, 24.0ms\n",
      "image 142/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233759.jpg: 640x480 (no detections), 33.0ms\n",
      "image 143/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233835.jpg: 640x480 1 bench, 71.3ms\n",
      "image 144/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234112.jpg: 640x480 2 cats, 1 potted plant, 28.0ms\n",
      "image 145/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234115.jpg: 640x480 1 cat, 1 dining table, 31.0ms\n",
      "image 146/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234507.jpg: 640x480 1 bench, 1 cat, 1 chair, 31.0ms\n",
      "image 147/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234510.jpg: 640x480 1 cat, 2 chairs, 26.4ms\n",
      "image 148/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234512.jpg: 640x480 1 cat, 25.0ms\n",
      "image 149/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234514.jpg: 640x480 1 cat, 38.3ms\n",
      "image 150/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234520.jpg: 640x480 2 cats, 1 bed, 1 toilet, 83.0ms\n",
      "image 151/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234523.jpg: 640x480 1 cat, 26.0ms\n",
      "image 152/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234524.jpg: 640x480 2 cats, 27.0ms\n",
      "image 153/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234614.jpg: 640x480 1 bird, 24.0ms\n",
      "image 154/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234630.jpg: 640x480 1 cat, 1 dog, 23.0ms\n",
      "image 155/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234749.jpg: 640x480 1 cat, 24.0ms\n",
      "image 156/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_235449.jpg: 640x480 1 cat, 23.0ms\n",
      "image 157/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_235454.jpg: 640x480 1 chair, 1 teddy bear, 28.0ms\n",
      "image 158/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240414_204830.jpg: 640x480 1 cow, 65.8ms\n",
      "image 159/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240414_204832.jpg: 640x480 2 cats, 14.0ms\n",
      "image 160/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240415_191936.jpg: 640x480 1 cat, 17.0ms\n",
      "image 161/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240416_131400.jpg: 640x480 1 person, 1 cat, 1 chair, 1 couch, 1 tv, 23.0ms\n",
      "image 162/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240421_133252.jpg: 640x480 1 dog, 13.7ms\n",
      "image 163/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240421_133313.jpg: 640x480 1 dog, 1 suitcase, 14.0ms\n",
      "image 164/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240423_103248.jpg: 480x640 1 cat, 14.0ms\n",
      "image 165/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240424_204037.jpg: 640x480 2 cats, 1 chair, 17.0ms\n",
      "image 166/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240425_224816.jpg: 640x480 1 cat, 12.0ms\n",
      "image 167/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240425_224817(0).jpg: 640x480 1 cat, 30.0ms\n",
      "image 168/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240425_224817.jpg: 640x480 1 cat, 32.6ms\n",
      "image 169/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240425_224819.jpg: 640x480 1 cat, 23.0ms\n",
      "image 170/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240426_133718.jpg: 640x480 1 cat, 22.0ms\n",
      "image 171/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240426_133720.jpg: 640x480 1 cat, 19.0ms\n",
      "image 172/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_113516.jpg: 640x480 1 cat, 21.0ms\n",
      "image 173/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_113519.jpg: 640x480 1 cat, 1 chair, 14.0ms\n",
      "image 174/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_130031.jpg: 640x480 1 cat, 15.0ms\n",
      "image 175/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_130032.jpg: 640x480 1 cat, 25.0ms\n",
      "image 176/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_130041.jpg: 640x480 1 cat, 16.0ms\n",
      "image 177/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240503_173937.jpg: 640x480 1 cat, 46.1ms\n",
      "image 178/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240503_173948.jpg: 480x640 1 cat, 1 chair, 32.0ms\n",
      "image 179/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185557.jpg: 640x480 1 bench, 1 cat, 1 backpack, 1 chair, 1 potted plant, 71.0ms\n",
      "image 180/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185559.jpg: 640x480 1 cat, 1 bear, 1 tv, 27.3ms\n",
      "image 181/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185601.jpg: 640x480 1 cat, 29.0ms\n",
      "image 182/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185604.jpg: 640x480 1 cat, 54.0ms\n",
      "image 183/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185606.jpg: 640x480 2 cats, 1 dog, 34.0ms\n",
      "image 184/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185608.jpg: 640x480 2 cats, 1 potted plant, 86.3ms\n",
      "image 185/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185612.jpg: 640x480 1 cat, 74.0ms\n",
      "image 186/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_185650.jpg: 640x480 2 cats, 4 chairs, 29.0ms\n",
      "image 187/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_185651.jpg: 640x480 2 cats, 1 cup, 2 chairs, 32.7ms\n",
      "image 188/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_185652.jpg: 640x480 2 cats, 1 cup, 2 chairs, 24.9ms\n",
      "image 189/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_185656.jpg: 640x480 1 bottle, 2 bowls, 3 chairs, 3 potted plants, 1 dining table, 1 tv, 3 vases, 31.0ms\n",
      "image 190/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_194053.jpg: 640x480 1 cat, 1 chair, 26.5ms\n",
      "image 191/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_210228.jpg: 640x480 1 cat, 19.0ms\n",
      "image 192/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_071046.jpg: 640x480 1 cat, 1 dog, 30.5ms\n",
      "image 193/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_071050.jpg: 640x480 1 cat, 2 bottles, 1 refrigerator, 94.8ms\n",
      "image 194/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_071052.jpg: 640x480 1 cat, 2 bottles, 70.0ms\n",
      "image 195/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_071053.jpg: 640x480 1 cat, 2 bottles, 67.0ms\n",
      "image 196/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_072915.jpg: 640x480 1 cat, 1 couch, 25.0ms\n",
      "image 197/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_231919.jpg: 640x480 1 cat, 1 mouse, 29.0ms\n",
      "image 198/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_231920.jpg: 640x480 1 cat, 76.0ms\n",
      "image 199/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_235311.jpg: 640x480 1 cat, 1 bowl, 21.0ms\n",
      "image 200/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_235315.jpg: 640x480 1 cat, 31.0ms\n",
      "image 201/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_235317.jpg: 640x480 1 cat, 1 bowl, 87.9ms\n",
      "image 202/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090846.jpg: 640x480 1 cat, 25.0ms\n",
      "image 203/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090900.jpg: 640x480 1 cat, 29.6ms\n",
      "image 204/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090907.jpg: 640x480 1 cat, 65.8ms\n",
      "image 205/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090911.jpg: 640x480 1 cat, 35.0ms\n",
      "image 206/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090915.jpg: 640x480 1 person, 1 dog, 42.0ms\n",
      "image 207/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180307.jpg: 640x480 2 cats, 96.1ms\n",
      "image 208/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180310.jpg: 640x480 2 cats, 1 handbag, 1 suitcase, 18.0ms\n",
      "image 209/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180316.jpg: 640x480 1 cat, 1 couch, 12.0ms\n",
      "image 210/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180318.jpg: 640x480 1 cat, 1 book, 13.8ms\n",
      "image 211/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180331.jpg: 640x480 1 cat, 25.0ms\n",
      "image 212/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180334.jpg: 640x480 1 cat, 25.0ms\n",
      "image 213/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180528.jpg: 640x480 1 dog, 18.0ms\n",
      "image 214/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180532.jpg: 640x480 1 cat, 24.2ms\n",
      "image 215/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180534.jpg: 640x480 (no detections), 48.9ms\n",
      "image 216/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180536.jpg: 640x480 (no detections), 35.0ms\n",
      "image 217/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180542.jpg: 640x480 1 person, 21.0ms\n",
      "image 218/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180847.jpg: 640x480 1 cat, 23.0ms\n",
      "image 219/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180849.jpg: 640x480 2 cats, 1 suitcase, 20.0ms\n",
      "image 220/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180853.jpg: 640x480 1 cat, 1 couch, 24.0ms\n",
      "image 221/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180937.jpg: 640x480 1 cat, 29.0ms\n",
      "image 222/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180939.jpg: 640x480 1 cat, 26.0ms\n",
      "image 223/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_193135.jpg: 640x480 1 person, 3 cats, 1 dog, 2 chairs, 28.0ms\n",
      "image 224/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_193136.jpg: 640x480 1 person, 4 cats, 37.0ms\n",
      "image 225/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_213919(0).jpg: 640x480 1 cat, 1 chair, 41.0ms\n",
      "image 226/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_213919.jpg: 640x480 1 cat, 1 chair, 1 couch, 1 bed, 37.0ms\n",
      "image 227/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_223849.jpg: 640x480 1 cat, 1 chair, 1 bed, 50.0ms\n",
      "image 228/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_223851.jpg: 640x480 1 cat, 1 cup, 2 chairs, 1 tv, 1 book, 34.0ms\n",
      "image 229/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240603_225020(0).jpg: 640x480 1 cat, 1 dog, 91.3ms\n",
      "image 230/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240603_225020.jpg: 640x480 1 cat, 40.0ms\n",
      "image 231/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240603_225023.jpg: 640x480 1 dog, 27.5ms\n",
      "image 232/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240603_225026.jpg: 640x480 1 dog, 29.0ms\n",
      "image 233/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240603_225027.jpg: 640x480 1 dog, 23.0ms\n",
      "image 234/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192457.jpg: 640x480 1 cat, 1 couch, 33.0ms\n",
      "image 235/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192458(0).jpg: 640x480 1 cat, 1 couch, 30.0ms\n",
      "image 236/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192458.jpg: 640x480 1 cat, 1 couch, 69.0ms\n",
      "image 237/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192459(0).jpg: 640x480 1 cat, 1 couch, 23.0ms\n",
      "image 238/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192459.jpg: 640x480 1 cat, 1 couch, 19.2ms\n",
      "image 239/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192501(0).jpg: 640x480 1 cat, 1 couch, 31.2ms\n",
      "image 240/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192501.jpg: 640x480 1 cat, 27.0ms\n",
      "image 241/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192502(0).jpg: 640x480 1 cat, 1 couch, 76.5ms\n",
      "image 242/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192502.jpg: 640x480 1 cat, 1 couch, 31.0ms\n",
      "image 243/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192503.jpg: 640x480 1 cat, 1 dog, 1 couch, 30.8ms\n",
      "image 244/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192504.jpg: 640x480 1 cat, 1 bed, 31.0ms\n",
      "image 245/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192505.jpg: 640x480 1 cat, 1 bed, 26.0ms\n",
      "image 246/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192506.jpg: 640x480 1 cat, 1 couch, 1 bed, 26.4ms\n",
      "image 247/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192508.jpg: 640x480 1 cat, 27.0ms\n",
      "image 248/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192509.jpg: 640x480 1 cat, 1 bed, 25.0ms\n",
      "image 249/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192510.jpg: 640x480 1 dog, 1 bed, 23.0ms\n",
      "image 250/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192511.jpg: 640x480 1 dog, 2 beds, 23.0ms\n",
      "image 251/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192512(0).jpg: 640x480 1 cat, 1 dog, 1 bed, 26.0ms\n",
      "image 252/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192512.jpg: 640x480 1 dog, 1 bed, 42.0ms\n",
      "image 253/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192513.jpg: 640x480 1 cat, 1 remote, 42.0ms\n",
      "image 254/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192514.jpg: 640x480 1 cat, 1 bed, 45.0ms\n",
      "image 255/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192515(0).jpg: 640x480 1 cat, 29.8ms\n",
      "image 256/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192515.jpg: 640x480 1 cat, 36.0ms\n",
      "image 257/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192533.jpg: 640x480 1 cat, 1 bed, 33.0ms\n",
      "image 258/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192534(0).jpg: 640x480 1 cat, 36.1ms\n",
      "image 259/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192534.jpg: 640x480 1 cat, 31.4ms\n",
      "image 260/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192535.jpg: 640x480 1 cat, 30.1ms\n",
      "image 261/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192537.jpg: 640x480 1 cat, 1 bed, 28.0ms\n",
      "image 262/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192538(0).jpg: 640x480 1 cat, 35.0ms\n",
      "image 263/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192538.jpg: 640x480 1 cat, 1 couch, 1 bed, 27.0ms\n",
      "image 264/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192539(0).jpg: 640x480 1 cat, 36.0ms\n",
      "image 265/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192539.jpg: 640x480 1 cat, 31.2ms\n",
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      "image 267/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192544.jpg: 640x480 1 cat, 1 couch, 1 bed, 29.0ms\n",
      "image 268/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205057.jpg: 640x480 1 cat, 1 book, 33.0ms\n",
      "image 269/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205103.jpg: 640x480 1 cat, 51.0ms\n",
      "image 270/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205105.jpg: 640x480 1 cat, 43.0ms\n",
      "image 271/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205110.jpg: 640x480 1 cat, 27.0ms\n",
      "image 272/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205113.jpg: 640x480 1 cat, 28.0ms\n",
      "image 273/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205119.jpg: 480x640 1 cat, 24.0ms\n",
      "image 274/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205121.jpg: 480x640 1 cat, 20.1ms\n",
      "image 275/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240623_192721.jpg: 640x480 2 cats, 1 dog, 39.0ms\n",
      "image 276/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240624_200545.jpg: 640x480 1 cat, 1 bottle, 1 potted plant, 27.0ms\n",
      "image 277/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240624_200548.jpg: 640x480 1 cat, 30.8ms\n",
      "image 278/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240624_200549.jpg: 640x480 1 cat, 35.2ms\n",
      "image 279/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012349.jpg: 640x480 1 cat, 35.0ms\n",
      "image 280/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012352.jpg: 640x480 1 cat, 28.0ms\n",
      "image 281/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012400.jpg: 640x480 1 cat, 28.3ms\n",
      "image 282/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012402.jpg: 640x480 1 cat, 33.0ms\n",
      "image 283/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012407.jpg: 640x480 1 cat, 30.0ms\n",
      "image 284/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012408.jpg: 640x480 1 cat, 47.8ms\n",
      "image 285/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_231410.jpg: 640x480 1 cat, 35.1ms\n",
      "image 286/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_231412.jpg: 640x480 1 cat, 33.5ms\n",
      "image 287/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_231413.jpg: 640x480 1 cat, 1 tv, 35.1ms\n",
      "image 288/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240704_202652.jpg: 640x480 1 cat, 1 dog, 31.0ms\n",
      "image 289/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240711_221427.jpg: 640x480 1 cat, 36.0ms\n",
      "image 290/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240711_221435.jpg: 640x480 3 cats, 27.0ms\n",
      "image 291/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165019.jpg: 640x480 1 cat, 2 potted plants, 1 bed, 1 vase, 27.0ms\n",
      "image 292/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165021.jpg: 640x480 2 cats, 1 couch, 5 potted plants, 1 bed, 1 tv, 1 vase, 27.0ms\n",
      "image 293/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165025.jpg: 640x480 1 cat, 7 potted plants, 2 beds, 1 vase, 26.7ms\n",
      "image 294/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165028.jpg: 640x480 1 cat, 1 bottle, 2 cups, 1 chair, 3 potted plants, 1 bed, 1 vase, 29.0ms\n",
      "image 295/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165029.jpg: 640x480 1 cat, 3 potted plants, 1 bed, 3 vases, 32.0ms\n",
      "image 296/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165031.jpg: 640x480 1 cat, 6 potted plants, 1 bed, 3 vases, 29.0ms\n",
      "image 297/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165033.jpg: 640x480 2 cats, 1 cup, 1 chair, 2 potted plants, 1 tv, 1 keyboard, 33.0ms\n",
      "image 298/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165040.jpg: 640x480 1 cat, 1 chair, 2 potted plants, 1 bed, 1 vase, 31.0ms\n",
      "image 299/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_224858.jpg: 640x480 1 cat, 1 bed, 32.0ms\n",
      "image 300/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_224902.jpg: 640x480 1 cat, 1 bed, 49.0ms\n",
      "image 301/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_224903.jpg: 640x480 1 cat, 28.0ms\n",
      "image 302/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_224905.jpg: 640x480 1 cat, 34.0ms\n",
      "image 303/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240725_133737.jpg: 640x480 1 person, 1 cup, 30.0ms\n",
      "image 304/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240726_225449.jpg: 640x480 1 bench, 2 cats, 29.0ms\n",
      "image 305/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240727_085826.jpg: 640x480 1 person, 1 cat, 36.0ms\n",
      "image 306/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240802_203206.jpg: 640x480 1 cat, 34.0ms\n",
      "image 307/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102342.jpg: 480x640 2 persons, 1 cat, 26.3ms\n",
      "image 308/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102347.jpg: 480x640 1 person, 2 cats, 23.2ms\n",
      "image 309/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102351.jpg: 480x640 2 persons, 2 cats, 32.0ms\n",
      "image 310/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102354.jpg: 480x640 2 persons, 1 cat, 1 suitcase, 24.0ms\n",
      "image 311/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102402.jpg: 480x640 1 cat, 1 umbrella, 22.0ms\n",
      "image 312/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102407.jpg: 480x640 2 persons, 1 cat, 22.0ms\n",
      "image 313/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102411.jpg: 480x640 1 person, 1 cat, 23.7ms\n",
      "image 314/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102415.jpg: 480x640 1 person, 1 cat, 25.0ms\n",
      "image 315/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_113806.jpg: 640x480 1 person, 1 cat, 2 suitcases, 58.4ms\n",
      "image 316/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_145117.jpg: 640x480 1 cat, 12.2ms\n",
      "image 317/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_145118.jpg: 640x480 1 cat, 9.0ms\n",
      "image 318/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171056.jpg: 640x480 1 cat, 1 chair, 3 books, 10.0ms\n",
      "image 319/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171058.jpg: 640x480 1 cat, 1 chair, 2 books, 11.0ms\n",
      "image 320/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171100.jpg: 640x480 1 dog, 3 books, 9.0ms\n",
      "image 321/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171104.jpg: 640x480 1 cat, 1 suitcase, 1 chair, 4 books, 8.0ms\n",
      "image 322/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171110.jpg: 640x480 1 cat, 1 suitcase, 6 books, 9.0ms\n",
      "image 323/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171111.jpg: 640x480 1 cat, 1 chair, 3 books, 12.0ms\n",
      "image 324/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_203211.jpg: 640x480 2 cats, 10.0ms\n",
      "image 325/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_203213.jpg: 640x480 2 cats, 1 dog, 9.0ms\n",
      "image 326/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_203217.jpg: 640x480 2 cats, 12.0ms\n",
      "image 327/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_203219.jpg: 640x480 2 cats, 12.2ms\n",
      "image 328/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_164652.jpg: 640x480 1 cat, 1 cake, 11.1ms\n",
      "image 329/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_164655.jpg: 640x480 2 cats, 10.1ms\n",
      "image 330/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_171436.jpg: 640x480 1 cat, 1 cup, 2 bowls, 1 book, 9.0ms\n",
      "image 331/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_171438.jpg: 640x480 1 cat, 2 bowls, 12.0ms\n",
      "image 332/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_171443.jpg: 640x480 1 cat, 2 bowls, 12.0ms\n",
      "image 333/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_173348.jpg: 640x480 1 cat, 12.0ms\n",
      "image 334/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_173355.jpg: 640x480 1 cat, 12.0ms\n",
      "image 335/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_173426.jpg: 480x640 1 cat, 14.0ms\n",
      "image 336/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211309.jpg: 640x480 2 cats, 17.0ms\n",
      "image 337/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211312.jpg: 640x480 1 cat, 14.0ms\n",
      "image 338/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211313.jpg: 640x480 1 cat, 24.0ms\n",
      "image 339/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211320.jpg: 640x480 1 cat, 23.0ms\n",
      "image 340/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211321.jpg: 640x480 1 cat, 29.0ms\n",
      "image 341/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211322.jpg: 640x480 1 cat, 26.0ms\n",
      "image 342/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211330.jpg: 640x480 2 cats, 3 chairs, 30.5ms\n",
      "image 343/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211333.jpg: 640x480 1 cat, 1 chair, 36.1ms\n",
      "image 344/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240820_144924.jpg: 640x480 1 bench, 1 cat, 24.0ms\n",
      "image 345/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240820_145200.jpg: 640x480 1 bench, 1 cat, 25.0ms\n",
      "image 346/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144759.jpg: 640x480 1 cat, 2 chairs, 1 book, 33.5ms\n",
      "image 347/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144801.jpg: 640x480 1 cat, 1 book, 120.4ms\n",
      "image 348/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144802.jpg: 640x480 (no detections), 48.8ms\n",
      "image 349/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144806.jpg: 640x480 1 cat, 1 dog, 1 tie, 19.0ms\n",
      "image 350/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144808.jpg: 640x480 1 cat, 1 dog, 19.7ms\n",
      "image 351/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144856.jpg: 640x480 1 person, 19.0ms\n",
      "image 352/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144935.jpg: 640x480 2 cats, 1 dog, 1 chair, 39.7ms\n",
      "image 353/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145212.jpg: 640x480 2 persons, 3 dogs, 58.2ms\n",
      "image 354/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145214.jpg: 640x480 2 persons, 2 cats, 1 chair, 57.4ms\n",
      "image 355/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145834.jpg: 640x480 2 cats, 1 dog, 37.0ms\n",
      "image 356/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145836.jpg: 640x480 2 cats, 1 chair, 43.5ms\n",
      "image 357/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145837.jpg: 640x480 2 cats, 1 dog, 48.5ms\n",
      "image 358/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145839.jpg: 640x480 2 cats, 1 dog, 63.5ms\n",
      "image 359/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145844.jpg: 640x480 2 cats, 1 dog, 8.0ms\n",
      "image 360/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145846.jpg: 640x480 1 person, 1 cat, 1 dog, 1 suitcase, 10.0ms\n",
      "image 361/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145849.jpg: 640x480 2 cats, 1 chair, 11.0ms\n",
      "image 362/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150802.jpg: 640x480 1 teddy bear, 12.0ms\n",
      "image 363/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150805.jpg: 640x480 1 teddy bear, 10.0ms\n",
      "image 364/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150807.jpg: 480x640 1 cat, 10.0ms\n",
      "image 365/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150810.jpg: 480x640 1 person, 2 cats, 16.0ms\n",
      "image 366/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150813.jpg: 480x640 1 person, 1 cat, 1 couch, 12.0ms\n",
      "image 367/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150852.jpg: 640x480 1 cat, 13.0ms\n",
      "image 368/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_152201.jpg: 640x480 2 cats, 1 dog, 10.0ms\n",
      "image 369/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_152205.jpg: 640x480 2 cats, 12.6ms\n",
      "image 370/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_152211.jpg: 640x480 2 cats, 12.5ms\n",
      "image 371/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_152212.jpg: 640x480 1 cat, 1 dog, 10.2ms\n",
      "image 372/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240827_224300.jpg: 640x480 1 cat, 1 couch, 9.0ms\n",
      "image 373/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240827_224303.jpg: 640x480 1 cat, 31.1ms\n",
      "image 374/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240828_102222.jpg: 640x480 1 cat, 1 chair, 28.0ms\n",
      "image 375/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240828_102229.jpg: 640x480 1 cat, 15.0ms\n",
      "image 376/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240903_211802.jpg: 640x480 1 cat, 40.0ms\n",
      "image 377/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240903_211803.jpg: 640x480 1 cat, 26.0ms\n",
      "image 378/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240903_211811.jpg: 640x480 1 cat, 16.0ms\n",
      "image 379/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240903_211818.jpg: 640x480 1 cat, 1 dog, 1 couch, 1 bed, 16.0ms\n",
      "image 380/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240903_211821.jpg: 640x480 1 person, 1 cat, 1 frisbee, 1 chair, 1 bed, 26.0ms\n",
      "image 381/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240907_115059.jpg: 480x640 1 cat, 1 couch, 1 bed, 17.0ms\n",
      "image 382/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240907_115105.jpg: 480x640 1 cat, 2 beds, 12.0ms\n",
      "image 383/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240907_115118.jpg: 480x640 1 cat, 1 bed, 14.0ms\n",
      "image 384/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_190713.jpg: 640x480 1 cat, 2 beds, 32.0ms\n",
      "image 385/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_190829.jpg: 640x480 1 cat, 1 bed, 42.2ms\n",
      "image 386/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_194734.jpg: 640x480 (no detections), 27.5ms\n",
      "image 387/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_194737.jpg: 640x480 2 cats, 60.6ms\n",
      "image 388/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_194739.jpg: 640x480 2 cats, 34.0ms\n",
      "image 389/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_194752.jpg: 640x480 1 cat, 1 bed, 37.4ms\n",
      "image 390/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_194753.jpg: 640x480 1 handbag, 1 bed, 67.5ms\n",
      "image 391/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240909_000642.jpg: 480x640 1 cat, 1 chair, 1 bed, 55.0ms\n",
      "image 392/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240909_000646.jpg: 480x640 2 cats, 29.8ms\n",
      "image 393/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124611.jpg: 640x480 1 cat, 1 bed, 36.0ms\n",
      "image 394/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124612.jpg: 640x480 1 cat, 1 dog, 1 bed, 84.5ms\n",
      "image 395/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124619.jpg: 640x480 1 person, 1 bed, 1 teddy bear, 46.7ms\n",
      "image 396/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124621.jpg: 640x480 1 person, 1 bed, 1 teddy bear, 59.8ms\n",
      "image 397/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124624.jpg: 640x480 1 dog, 70.0ms\n",
      "image 398/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124720.jpg: 640x480 1 bench, 1 cat, 1 bed, 151.0ms\n",
      "image 399/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124723.jpg: 640x480 1 cat, 50.2ms\n",
      "image 400/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124734.jpg: 640x480 1 cat, 2 beds, 34.0ms\n",
      "image 401/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124738.jpg: 640x480 1 cat, 1 bed, 42.5ms\n",
      "image 402/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124741.jpg: 640x480 1 cat, 1 couch, 1 bed, 50.3ms\n",
      "image 403/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091314.jpg: 640x480 1 cat, 198.3ms\n",
      "image 404/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091319.jpg: 640x480 1 cat, 132.7ms\n",
      "image 405/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091320.jpg: 640x480 1 cat, 55.0ms\n",
      "image 406/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091321.jpg: 640x480 (no detections), 63.7ms\n",
      "image 407/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091322.jpg: 640x480 1 cat, 79.4ms\n",
      "image 408/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091323.jpg: 640x480 1 cat, 62.9ms\n",
      "image 409/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091329.jpg: 480x640 1 cat, 49.0ms\n",
      "image 410/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091845.jpg: 640x480 1 cat, 73.8ms\n",
      "image 411/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091846.jpg: 640x480 1 cat, 59.0ms\n",
      "image 412/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091848.jpg: 640x480 1 bird, 1 cat, 80.0ms\n",
      "image 413/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091850.jpg: 640x480 1 cat, 43.0ms\n",
      "image 414/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091852.jpg: 640x480 1 bird, 1 cat, 61.4ms\n",
      "image 415/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091859.jpg: 640x480 1 cat, 42.9ms\n",
      "image 416/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091902.jpg: 640x480 1 cat, 54.2ms\n",
      "image 417/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091905.jpg: 640x480 1 cat, 52.9ms\n",
      "image 418/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_193740.jpg: 640x480 1 cat, 11.0ms\n",
      "image 419/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_193742.jpg: 640x480 1 cat, 11.0ms\n",
      "image 420/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174138.jpg: 640x480 1 chair, 1 bed, 1 mouse, 10.0ms\n",
      "image 421/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174140.jpg: 640x480 2 surfboards, 11.8ms\n",
      "image 422/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174204.jpg: 640x480 1 cat, 1 toilet, 11.0ms\n",
      "image 423/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174614.jpg: 640x480 2 cats, 1 laptop, 10.0ms\n",
      "image 424/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174616.jpg: 640x480 1 cat, 10.9ms\n",
      "image 425/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174617.jpg: 640x480 1 person, 17.0ms\n",
      "image 426/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174620.jpg: 640x480 1 person, 19.2ms\n",
      "image 427/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174622.jpg: 640x480 1 person, 8.0ms\n",
      "image 428/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174636.jpg: 640x480 1 person, 1 cat, 20.0ms\n",
      "image 429/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_215106.jpg: 640x480 1 cat, 1 bed, 14.0ms\n",
      "image 430/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_215113.jpg: 640x480 1 person, 1 cat, 1 bottle, 1 chair, 1 bed, 1 tv, 8.0ms\n",
      "image 431/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_215119.jpg: 640x480 1 cat, 1 bottle, 1 bed, 1 tv, 13.6ms\n",
      "image 432/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240920_192026.jpg: 640x480 1 cat, 13.0ms\n",
      "image 433/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240924_111824.jpg: 480x640 1 cat, 16.8ms\n",
      "image 434/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240924_111829.jpg: 480x640 1 cat, 15.0ms\n",
      "image 435/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240924_111831.jpg: 480x640 1 cat, 1 chair, 10.0ms\n",
      "image 436/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162434.jpg: 640x480 2 cats, 1 potted plant, 23.5ms\n",
      "image 437/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162437.jpg: 640x480 1 cat, 1 potted plant, 23.0ms\n",
      "image 438/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162442.jpg: 640x480 1 person, 1 cat, 27.0ms\n",
      "image 439/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162443.jpg: 640x480 1 person, 1 cat, 30.0ms\n",
      "image 440/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162445.jpg: 640x480 1 person, 1 cat, 27.0ms\n",
      "image 441/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162452.jpg: 640x480 1 person, 1 cat, 104.3ms\n",
      "image 442/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162458.jpg: 640x480 1 cat, 2 chairs, 2 potted plants, 14.0ms\n",
      "image 443/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162500.jpg: 640x480 1 cat, 10.0ms\n",
      "image 444/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162503.jpg: 640x480 1 cat, 1 chair, 2 potted plants, 15.0ms\n",
      "image 445/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_100748.jpg: 480x640 2 persons, 1 cat, 16.0ms\n",
      "image 446/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_101009.jpg: 640x480 1 cat, 13.0ms\n",
      "image 447/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_101010.jpg: 640x480 1 dog, 16.0ms\n",
      "image 448/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_101012.jpg: 640x480 1 umbrella, 17.0ms\n",
      "image 449/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_101014.jpg: 640x480 1 cat, 13.3ms\n",
      "image 450/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_101016.jpg: 640x480 1 cat, 19.0ms\n",
      "image 451/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_184523.jpg: 480x640 1 cat, 22.0ms\n",
      "image 452/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240930_151252.jpg: 640x480 3 persons, 1 dog, 1 teddy bear, 11.0ms\n",
      "image 453/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240930_151259.jpg: 640x480 1 cat, 11.0ms\n",
      "image 454/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241002_212916.jpg: 640x480 2 cats, 12.0ms\n",
      "image 455/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241002_212925.jpg: 640x480 2 cats, 1 handbag, 19.0ms\n",
      "image 456/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241002_212949.jpg: 640x480 2 cats, 12.0ms\n",
      "image 457/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241004_112619.jpg: 640x480 1 cat, 1 bowl, 1 potted plant, 11.0ms\n",
      "image 458/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241004_112626.jpg: 640x480 1 cat, 1 bowl, 14.0ms\n",
      "image 459/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241008_094705.jpg: 640x480 1 cat, 1 bowl, 30.0ms\n",
      "image 460/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241008_094939.jpg: 640x480 1 cat, 1 dog, 1 couch, 30.5ms\n",
      "image 461/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241008_094940.jpg: 640x480 2 cats, 20.3ms\n",
      "image 462/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241009_121555.jpg: 640x480 2 beds, 37.0ms\n",
      "image 463/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241009_121558.jpg: 640x480 1 cat, 1 suitcase, 62.5ms\n",
      "image 464/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241009_121601.jpg: 640x480 2 persons, 61.0ms\n",
      "image 465/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191519.jpg: 640x480 1 cat, 45.0ms\n",
      "image 466/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191520.jpg: 640x480 1 cat, 33.0ms\n",
      "image 467/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191523.jpg: 640x480 1 cat, 64.2ms\n",
      "image 468/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191525.jpg: 640x480 1 cat, 30.0ms\n",
      "image 469/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191527.jpg: 640x480 1 cat, 1 sink, 28.0ms\n",
      "image 470/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191529.jpg: 640x480 1 cat, 52.8ms\n",
      "image 471/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_132210.jpg: 640x480 1 cat, 102.6ms\n",
      "image 472/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_132216.jpg: 640x480 1 cat, 10.0ms\n",
      "image 473/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_132218.jpg: 640x480 1 dog, 12.0ms\n",
      "image 474/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_184948.jpg: 640x480 1 cat, 12.0ms\n",
      "image 475/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_184949.jpg: 640x480 1 cat, 1 bowl, 12.0ms\n",
      "image 476/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241022_023307.jpg: 640x480 1 cat, 1 bottle, 12.7ms\n",
      "image 477/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241024_134452.jpg: 640x480 1 cat, 1 bed, 11.0ms\n",
      "image 478/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241101_145241.jpg: 640x480 1 cat, 1 bed, 9.0ms\n",
      "image 479/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241115_195434.jpg: 640x480 1 person, 1 cat, 1 chair, 11.0ms\n",
      "image 480/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241115_195439.jpg: 640x480 1 person, 1 cat, 1 chair, 11.0ms\n",
      "image 481/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241115_200624.jpg: 640x480 2 persons, 2 cats, 1 bowl, 1 bed, 13.0ms\n",
      "image 482/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241115_200626.jpg: 640x480 1 person, 1 cat, 12.0ms\n",
      "image 483/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241124_214430.jpg: 640x480 1 bottle, 1 bed, 11.0ms\n",
      "image 484/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241124_220257.jpg: 640x480 1 cat, 1 bowl, 1 bed, 13.0ms\n",
      "image 485/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241124_220259.jpg: 640x480 1 cat, 1 bed, 1 dining table, 15.4ms\n",
      "image 486/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241129_085724.jpg: 640x480 1 bench, 22.0ms\n",
      "image 487/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241206_164037.jpg: 640x480 1 person, 1 cat, 17.0ms\n",
      "image 488/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241206_164040.jpg: 640x480 2 cats, 16.0ms\n",
      "image 489/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241217_102418.jpg: 640x480 1 cat, 1 bowl, 17.1ms\n",
      "image 490/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132045.jpg: 640x480 1 cat, 1 bed, 22.0ms\n",
      "image 491/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132051.jpg: 640x480 2 cats, 20.0ms\n",
      "image 492/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132058.jpg: 640x480 2 cats, 24.0ms\n",
      "image 493/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132101.jpg: 480x640 2 cats, 20.0ms\n",
      "image 494/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132106.jpg: 480x640 1 cat, 27.5ms\n",
      "image 495/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241224_180740.jpg: 640x480 (no detections), 30.0ms\n",
      "image 496/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_130054.jpg: 640x480 1 cat, 1 bowl, 1 couch, 1 bed, 53.0ms\n",
      "image 497/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134500.jpg: 640x480 1 cat, 1 toilet, 45.7ms\n",
      "image 498/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134517.jpg: 640x480 1 bear, 1 toilet, 34.0ms\n",
      "image 499/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134521.jpg: 640x480 1 person, 56.5ms\n",
      "image 500/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134625.jpg: 640x480 (no detections), 61.1ms\n",
      "image 501/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134627.jpg: 640x480 1 cat, 48.0ms\n",
      "image 502/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134634.jpg: 640x480 1 dog, 33.0ms\n",
      "image 503/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134635.jpg: 640x480 2 teddy bears, 32.0ms\n",
      "image 504/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135201.jpg: 640x480 1 cat, 121.9ms\n",
      "image 505/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135214.jpg: 640x480 (no detections), 44.0ms\n",
      "image 506/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135215.jpg: 640x480 1 dog, 1 chair, 24.0ms\n",
      "image 507/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135219.jpg: 640x480 1 person, 1 bicycle, 1 cat, 1 dog, 26.0ms\n",
      "image 508/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135343(0).jpg: 640x480 1 cat, 3 chairs, 29.0ms\n",
      "image 509/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135343.jpg: 640x480 2 cats, 1 chair, 25.0ms\n",
      "image 510/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135345.jpg: 640x480 1 cat, 1 chair, 21.8ms\n",
      "image 511/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135347.jpg: 640x480 2 cats, 1 dog, 20.9ms\n",
      "image 512/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135352.jpg: 640x480 1 cat, 28.0ms\n",
      "image 513/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135354.jpg: 640x480 1 cat, 40.5ms\n",
      "image 514/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135707.jpg: 640x480 3 chairs, 1 bed, 27.0ms\n",
      "image 515/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135708.jpg: 640x480 2 chairs, 29.0ms\n",
      "image 516/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135710.jpg: 640x480 1 cat, 2 chairs, 27.0ms\n",
      "image 517/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135712.jpg: 640x480 1 cat, 1 chair, 29.0ms\n",
      "image 518/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_140923.jpg: 640x480 (no detections), 30.5ms\n",
      "image 519/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_140924.jpg: 640x480 2 persons, 27.0ms\n",
      "image 520/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241227_150819.jpg: 640x480 1 person, 1 cat, 1 tv, 50.2ms\n",
      "image 521/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241227_150826.jpg: 640x480 1 person, 1 cat, 1 chair, 1 tv, 1 laptop, 40.0ms\n",
      "image 522/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250105_143721.jpg: 640x480 2 cats, 2 chairs, 31.0ms\n",
      "image 523/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250105_143723.jpg: 640x480 1 cat, 43.8ms\n",
      "image 524/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250105_143727.jpg: 640x480 2 cats, 30.0ms\n",
      "image 525/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250112_203850.jpg: 640x480 2 cats, 1 sports ball, 39.4ms\n",
      "image 526/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250112_203857.jpg: 640x480 2 persons, 1 cat, 27.0ms\n",
      "image 527/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250112_203858.jpg: 640x480 1 cat, 1 teddy bear, 27.1ms\n",
      "image 528/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250112_203900.jpg: 640x480 1 dog, 1 book, 34.0ms\n",
      "image 529/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250112_203903.jpg: 640x480 1 sheep, 1 sports ball, 26.0ms\n",
      "image 530/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174816.jpg: 640x480 1 person, 1 cat, 30.0ms\n",
      "image 531/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174818.jpg: 640x480 (no detections), 25.9ms\n",
      "image 532/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174833.jpg: 640x480 1 person, 1 teddy bear, 33.0ms\n",
      "image 533/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174904.jpg: 640x480 1 person, 1 chair, 1 tv, 1 clock, 1 teddy bear, 31.0ms\n",
      "image 534/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174905.jpg: 640x480 1 person, 1 chair, 1 tv, 2 teddy bears, 31.0ms\n",
      "image 535/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174908.jpg: 640x480 1 person, 1 tv, 1 teddy bear, 29.1ms\n",
      "image 536/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174912.jpg: 640x480 1 person, 2 chairs, 1 dining table, 1 teddy bear, 27.0ms\n",
      "image 537/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174913.jpg: 640x480 1 person, 1 chair, 1 teddy bear, 31.0ms\n",
      "image 538/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174915.jpg: 640x480 1 person, 2 chairs, 1 teddy bear, 27.0ms\n",
      "image 539/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174920.jpg: 640x480 1 person, 3 chairs, 1 tv, 26.0ms\n",
      "image 540/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174949(0).jpg: 640x480 1 person, 1 couch, 1 bed, 33.0ms\n",
      "image 541/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174949.jpg: 640x480 1 person, 1 couch, 1 bed, 1 book, 27.0ms\n",
      "image 542/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_175124.jpg: 640x480 (no detections), 28.0ms\n",
      "image 543/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250117_172955.jpg: 640x480 1 person, 1 cat, 1 bed, 29.0ms\n",
      "image 544/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250120_225956.jpg: 640x480 1 cat, 26.9ms\n",
      "image 545/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250121_190039.jpg: 640x480 1 cat, 1 dog, 28.3ms\n",
      "image 546/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250121_190043.jpg: 640x480 2 cats, 1 chair, 32.0ms\n",
      "image 547/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250121_190046.jpg: 640x480 1 dog, 1 chair, 3 books, 30.1ms\n",
      "image 548/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142944.jpg: 640x480 3 cats, 1 bed, 30.0ms\n",
      "image 549/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142946.jpg: 640x480 4 cats, 1 bed, 33.0ms\n",
      "image 550/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142948.jpg: 640x480 3 cats, 2 beds, 25.3ms\n",
      "image 551/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142949.jpg: 640x480 2 cats, 1 bed, 31.0ms\n",
      "image 552/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142953.jpg: 640x480 2 cats, 1 potted plant, 1 bed, 27.2ms\n",
      "image 553/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142958.jpg: 640x480 1 cat, 2 beds, 29.5ms\n",
      "image 554/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_191929.jpg: 640x480 1 dog, 2 books, 44.6ms\n",
      "image 555/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\IMG-20240730-WA0005.jpg: 640x480 2 persons, 1 couch, 32.0ms\n",
      "image 556/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\IMG_20240318_231934_252.webp: 640x384 1 cat, 22.0ms\n",
      "image 557/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\IMG_20241207_211456_238.jpg: 640x480 1 person, 1 cat, 1 bed, 34.0ms\n",
      "image 558/558 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\IMG_20241229_221404_122.jpg: 640x512 1 person, 2 dogs, 29.7ms\n",
      "Speed: 4.4ms preprocess, 31.3ms inference, 4.4ms postprocess per image at shape (1, 3, 640, 512)\n"
     ]
    }
   ],
   "source": [
    "results = model.predict(source=images_folder, save=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved cropped cat image: output/cropped/cat_1.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_306.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_310.jpg\n",
      "Saved cropped cat image: output/cropped/cat_311.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_315.jpg\n",
      "Saved cropped cat image: output/cropped/cat_316.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_321.jpg\n",
      "Saved cropped cat image: output/cropped/cat_322.jpg\n",
      "Saved cropped cat image: output/cropped/cat_323.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_325.jpg\n",
      "Saved cropped cat image: output/cropped/cat_326.jpg\n",
      "Saved cropped cat image: output/cropped/cat_327.jpg\n",
      "Saved cropped cat image: output/cropped/cat_328.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_333.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_336.jpg\n",
      "Saved cropped cat image: output/cropped/cat_337.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_339.jpg\n",
      "Saved cropped cat image: output/cropped/cat_340.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_342.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_344.jpg\n",
      "Saved cropped cat image: output/cropped/cat_345.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_347.jpg\n",
      "Saved cropped cat image: output/cropped/cat_348.jpg\n",
      "Saved cropped cat image: output/cropped/cat_349.jpg\n",
      "Saved cropped cat image: output/cropped/cat_350.jpg\n",
      "Saved cropped cat image: output/cropped/cat_351.jpg\n",
      "Saved cropped cat image: output/cropped/cat_352.jpg\n",
      "Saved cropped cat image: output/cropped/cat_353.jpg\n",
      "Saved cropped cat image: output/cropped/cat_354.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_356.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_359.jpg\n",
      "Saved cropped cat image: output/cropped/cat_360.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_366.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_370.jpg\n",
      "Saved cropped cat image: output/cropped/cat_371.jpg\n",
      "Saved cropped cat image: output/cropped/cat_372.jpg\n",
      "Saved cropped cat image: output/cropped/cat_373.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_379.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_381.jpg\n",
      "Saved cropped cat image: output/cropped/cat_382.jpg\n",
      "Saved cropped cat image: output/cropped/cat_383.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_385.jpg\n",
      "Saved cropped cat image: output/cropped/cat_386.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_388.jpg\n",
      "Saved cropped cat image: output/cropped/cat_389.jpg\n",
      "Saved cropped cat image: output/cropped/cat_390.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_399.jpg\n",
      "Saved cropped cat image: output/cropped/cat_400.jpg\n",
      "Saved cropped cat image: output/cropped/cat_401.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_456.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_460.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_468.jpg\n",
      "Saved cropped cat image: output/cropped/cat_469.jpg\n",
      "Saved cropped cat image: output/cropped/cat_470.jpg\n",
      "Saved cropped cat image: output/cropped/cat_471.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_473.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_476.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_480.jpg\n",
      "Saved cropped cat image: output/cropped/cat_481.jpg\n",
      "Saved cropped cat image: output/cropped/cat_482.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_485.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_489.jpg\n",
      "Saved cropped cat image: output/cropped/cat_490.jpg\n",
      "Saved cropped cat image: output/cropped/cat_491.jpg\n",
      "Saved cropped cat image: output/cropped/cat_492.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_499.jpg\n",
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      "Saved cropped cat image: output/cropped/cat_509.jpg\n",
      "Saved cropped cat image: output/cropped/cat_510.jpg\n",
      "Saved cropped cat image: output/cropped/cat_511.jpg\n",
      "Saved cropped cat image: output/cropped/cat_512.jpg\n",
      "Saved cropped cat image: output/cropped/cat_513.jpg\n",
      "Saved cropped cat image: output/cropped/cat_514.jpg\n",
      "Saved cropped cat image: output/cropped/cat_515.jpg\n",
      "Saved cropped cat image: output/cropped/cat_516.jpg\n",
      "Saved cropped cat image: output/cropped/cat_517.jpg\n",
      "Saved cropped cat image: output/cropped/cat_518.jpg\n",
      "Saved cropped cat image: output/cropped/cat_519.jpg\n",
      "Saved cropped cat image: output/cropped/cat_520.jpg\n",
      "Saved cropped cat image: output/cropped/cat_521.jpg\n",
      "Saved cropped cat image: output/cropped/cat_522.jpg\n",
      "Saved cropped cat image: output/cropped/cat_523.jpg\n",
      "Saved cropped cat image: output/cropped/cat_524.jpg\n",
      "Saved cropped cat image: output/cropped/cat_525.jpg\n",
      "Saved cropped cat image: output/cropped/cat_526.jpg\n",
      "Saved cropped cat image: output/cropped/cat_527.jpg\n",
      "Saved cropped cat image: output/cropped/cat_528.jpg\n",
      "Saved cropped cat image: output/cropped/cat_529.jpg\n",
      "Saved cropped cat image: output/cropped/cat_530.jpg\n",
      "Saved cropped cat image: output/cropped/cat_531.jpg\n",
      "Saved cropped cat image: output/cropped/cat_532.jpg\n",
      "Saved cropped cat image: output/cropped/cat_533.jpg\n",
      "Saved cropped cat image: output/cropped/cat_534.jpg\n",
      "Saved cropped cat image: output/cropped/cat_535.jpg\n",
      "Saved cropped cat image: output/cropped/cat_536.jpg\n",
      "Saved cropped cat image: output/cropped/cat_537.jpg\n",
      "Saved cropped cat image: output/cropped/cat_538.jpg\n",
      "Saved cropped cat image: output/cropped/cat_539.jpg\n",
      "Saved cropped cat image: output/cropped/cat_540.jpg\n",
      "Saved cropped cat image: output/cropped/cat_541.jpg\n",
      "Saved cropped cat image: output/cropped/cat_542.jpg\n",
      "Saved cropped cat image: output/cropped/cat_543.jpg\n",
      "Saved cropped cat image: output/cropped/cat_544.jpg\n",
      "Saved cropped cat image: output/cropped/cat_545.jpg\n",
      "Saved cropped cat image: output/cropped/cat_546.jpg\n",
      "Saved cropped cat image: output/cropped/cat_547.jpg\n",
      "Saved cropped cat image: output/cropped/cat_548.jpg\n",
      "Saved cropped cat image: output/cropped/cat_549.jpg\n",
      "Saved cropped cat image: output/cropped/cat_550.jpg\n",
      "Saved cropped cat image: output/cropped/cat_551.jpg\n",
      "Saved cropped cat image: output/cropped/cat_552.jpg\n"
     ]
    }
   ],
   "source": [
    "os.makedirs(output_folder+'cropped', exist_ok=True)  # Ensure the directory exists\n",
    "cat_count = 0  # Counter for saving cat images\n",
    "for result in results:\n",
    "    boxes = result.boxes\n",
    "\n",
    "    for box in boxes:\n",
    "        # Get class ID and confidence\n",
    "        class_id = int(box.cls[0])\n",
    "        confidence = box.conf[0]\n",
    "\n",
    "        # Process only \"cat\" items (cls id = 15)\n",
    "        if class_id == 15:\n",
    "            # Get bounding box coordinates\n",
    "            x_min, y_min, x_max, y_max = map(int, box.xyxy[0])\n",
    "\n",
    "            # Crop the image\n",
    "            cropped_cat = result.orig_img[y_min:y_max, x_min:x_max]\n",
    "\n",
    "            # Save the cropped image\n",
    "            cat_count += 1\n",
    "            output_path = os.path.join(output_folder,f\"cropped/cat_{result}.jpg\")\n",
    "            cv2.imwrite(output_path, cropped_cat)\n",
    "\n",
    "            print(f\"Saved cropped cat image: {output_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\danie\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\danie\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "# Feature Extraction with ResNet18\n",
    "# Load ResNet18 Model\n",
    "resnet = models.resnet18(pretrained=True)\n",
    "resnet = torch.nn.Sequential(*(list(resnet.children())[:-1]))  # Remove classification layer\n",
    "resnet.eval()\n",
    "\n",
    "# Define Preprocessing\n",
    "preprocess = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract Features\n",
    "def extract_features(image_path):\n",
    "    img = Image.open(image_path).convert(\"RGB\")\n",
    "    img_tensor = preprocess(img).unsqueeze(0)\n",
    "    with torch.no_grad():\n",
    "        features = resnet(img_tensor)\n",
    "    return features.squeeze().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "crop_folder = 'output/cropped'\n",
    "feature_list = []\n",
    "crop_images = [os.path.join(crop_folder, img) for img in os.listdir(crop_folder) if img.endswith(\".jpg\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "for crop_path in crop_images:\n",
    "    features = extract_features(crop_path)\n",
    "    feature_list.append(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the list of features into a NumPy array\n",
    "features_array = np.array(feature_list)\n",
    "# Perform K-Means clustering (2 clusters for your case)\n",
    "kmeans = KMeans(n_clusters=2, random_state=42)\n",
    "labels = kmeans.fit_predict(features_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat_1.jpg belongs to cluster 0\n",
      "cat_10.jpg belongs to cluster 0\n",
      "cat_100.jpg belongs to cluster 0\n",
      "cat_101.jpg belongs to cluster 1\n",
      "cat_102.jpg belongs to cluster 0\n",
      "cat_103.jpg belongs to cluster 0\n",
      "cat_104.jpg belongs to cluster 0\n",
      "cat_105.jpg belongs to cluster 0\n",
      "cat_106.jpg belongs to cluster 1\n",
      "cat_107.jpg belongs to cluster 1\n",
      "cat_108.jpg belongs to cluster 0\n",
      "cat_109.jpg belongs to cluster 1\n",
      "cat_11.jpg belongs to cluster 0\n",
      "cat_110.jpg belongs to cluster 1\n",
      "cat_111.jpg belongs to cluster 1\n",
      "cat_112.jpg belongs to cluster 1\n",
      "cat_113.jpg belongs to cluster 1\n",
      "cat_114.jpg belongs to cluster 1\n",
      "cat_115.jpg belongs to cluster 1\n",
      "cat_116.jpg belongs to cluster 1\n",
      "cat_117.jpg belongs to cluster 1\n",
      "cat_118.jpg belongs to cluster 1\n",
      "cat_119.jpg belongs to cluster 1\n",
      "cat_12.jpg belongs to cluster 0\n",
      "cat_120.jpg belongs to cluster 1\n",
      "cat_121.jpg belongs to cluster 1\n",
      "cat_122.jpg belongs to cluster 1\n",
      "cat_123.jpg belongs to cluster 0\n",
      "cat_124.jpg belongs to cluster 0\n",
      "cat_125.jpg belongs to cluster 0\n",
      "cat_126.jpg belongs to cluster 0\n",
      "cat_127.jpg belongs to cluster 1\n",
      "cat_128.jpg belongs to cluster 1\n",
      "cat_129.jpg belongs to cluster 1\n",
      "cat_13.jpg belongs to cluster 0\n",
      "cat_130.jpg belongs to cluster 1\n",
      "cat_131.jpg belongs to cluster 1\n",
      "cat_132.jpg belongs to cluster 1\n",
      "cat_133.jpg belongs to cluster 1\n",
      "cat_134.jpg belongs to cluster 1\n",
      "cat_135.jpg belongs to cluster 1\n",
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      "cat_137.jpg belongs to cluster 0\n",
      "cat_138.jpg belongs to cluster 0\n",
      "cat_139.jpg belongs to cluster 0\n",
      "cat_14.jpg belongs to cluster 0\n",
      "cat_140.jpg belongs to cluster 0\n",
      "cat_141.jpg belongs to cluster 0\n",
      "cat_142.jpg belongs to cluster 1\n",
      "cat_143.jpg belongs to cluster 1\n",
      "cat_144.jpg belongs to cluster 0\n",
      "cat_145.jpg belongs to cluster 0\n",
      "cat_146.jpg belongs to cluster 0\n",
      "cat_147.jpg belongs to cluster 1\n",
      "cat_148.jpg belongs to cluster 1\n",
      "cat_149.jpg belongs to cluster 1\n",
      "cat_15.jpg belongs to cluster 0\n",
      "cat_150.jpg belongs to cluster 0\n",
      "cat_151.jpg belongs to cluster 0\n",
      "cat_152.jpg belongs to cluster 0\n",
      "cat_153.jpg belongs to cluster 0\n",
      "cat_154.jpg belongs to cluster 1\n",
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      "cat_156.jpg belongs to cluster 0\n",
      "cat_157.jpg belongs to cluster 0\n",
      "cat_158.jpg belongs to cluster 0\n",
      "cat_159.jpg belongs to cluster 1\n",
      "cat_16.jpg belongs to cluster 0\n",
      "cat_160.jpg belongs to cluster 1\n",
      "cat_161.jpg belongs to cluster 1\n",
      "cat_162.jpg belongs to cluster 1\n",
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      "cat_164.jpg belongs to cluster 0\n",
      "cat_165.jpg belongs to cluster 1\n",
      "cat_166.jpg belongs to cluster 0\n",
      "cat_167.jpg belongs to cluster 1\n",
      "cat_168.jpg belongs to cluster 1\n",
      "cat_169.jpg belongs to cluster 1\n",
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      "cat_170.jpg belongs to cluster 0\n",
      "cat_171.jpg belongs to cluster 1\n",
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      "cat_173.jpg belongs to cluster 0\n",
      "cat_174.jpg belongs to cluster 0\n",
      "cat_175.jpg belongs to cluster 0\n",
      "cat_176.jpg belongs to cluster 0\n",
      "cat_177.jpg belongs to cluster 0\n",
      "cat_178.jpg belongs to cluster 0\n",
      "cat_179.jpg belongs to cluster 1\n",
      "cat_18.jpg belongs to cluster 0\n",
      "cat_180.jpg belongs to cluster 0\n",
      "cat_181.jpg belongs to cluster 0\n",
      "cat_182.jpg belongs to cluster 0\n",
      "cat_183.jpg belongs to cluster 0\n",
      "cat_184.jpg belongs to cluster 0\n",
      "cat_185.jpg belongs to cluster 1\n",
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      "cat_187.jpg belongs to cluster 0\n",
      "cat_188.jpg belongs to cluster 1\n",
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      "cat_19.jpg belongs to cluster 0\n",
      "cat_190.jpg belongs to cluster 0\n",
      "cat_191.jpg belongs to cluster 1\n",
      "cat_192.jpg belongs to cluster 1\n",
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      "cat_194.jpg belongs to cluster 1\n",
      "cat_195.jpg belongs to cluster 1\n",
      "cat_196.jpg belongs to cluster 1\n",
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      "cat_198.jpg belongs to cluster 0\n",
      "cat_199.jpg belongs to cluster 0\n",
      "cat_2.jpg belongs to cluster 0\n",
      "cat_20.jpg belongs to cluster 0\n",
      "cat_200.jpg belongs to cluster 0\n",
      "cat_201.jpg belongs to cluster 1\n",
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      "cat_203.jpg belongs to cluster 0\n",
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      "cat_206.jpg belongs to cluster 1\n",
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      "cat_208.jpg belongs to cluster 0\n",
      "cat_209.jpg belongs to cluster 0\n",
      "cat_21.jpg belongs to cluster 0\n",
      "cat_210.jpg belongs to cluster 0\n",
      "cat_211.jpg belongs to cluster 0\n",
      "cat_212.jpg belongs to cluster 0\n",
      "cat_213.jpg belongs to cluster 1\n",
      "cat_214.jpg belongs to cluster 1\n",
      "cat_215.jpg belongs to cluster 1\n",
      "cat_216.jpg belongs to cluster 1\n",
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      "cat_219.jpg belongs to cluster 1\n",
      "cat_22.jpg belongs to cluster 1\n",
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      "cat_223.jpg belongs to cluster 0\n",
      "cat_224.jpg belongs to cluster 0\n",
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      "cat_226.jpg belongs to cluster 1\n",
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      "cat_229.jpg belongs to cluster 0\n",
      "cat_23.jpg belongs to cluster 1\n",
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      "cat_231.jpg belongs to cluster 0\n",
      "cat_232.jpg belongs to cluster 0\n",
      "cat_233.jpg belongs to cluster 0\n",
      "cat_234.jpg belongs to cluster 0\n",
      "cat_235.jpg belongs to cluster 0\n",
      "cat_236.jpg belongs to cluster 0\n",
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      "cat_238.jpg belongs to cluster 0\n",
      "cat_239.jpg belongs to cluster 0\n",
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      "cat_241.jpg belongs to cluster 1\n",
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      "cat_246.jpg belongs to cluster 0\n",
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      "cat_248.jpg belongs to cluster 0\n",
      "cat_249.jpg belongs to cluster 0\n",
      "cat_25.jpg belongs to cluster 1\n",
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      "cat_255.jpg belongs to cluster 0\n",
      "cat_256.jpg belongs to cluster 0\n",
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      "cat_259.jpg belongs to cluster 0\n",
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      "cat_262.jpg belongs to cluster 0\n",
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      "cat_277.jpg belongs to cluster 1\n",
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      "cat_279.jpg belongs to cluster 1\n",
      "cat_28.jpg belongs to cluster 1\n",
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      "cat_289.jpg belongs to cluster 0\n",
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      "cat_292.jpg belongs to cluster 0\n",
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      "cat_297.jpg belongs to cluster 0\n",
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      "cat_299.jpg belongs to cluster 1\n",
      "cat_3.jpg belongs to cluster 1\n",
      "cat_30.jpg belongs to cluster 1\n",
      "cat_300.jpg belongs to cluster 1\n",
      "cat_301.jpg belongs to cluster 1\n",
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      "cat_309.jpg belongs to cluster 1\n",
      "cat_31.jpg belongs to cluster 1\n",
      "cat_310.jpg belongs to cluster 1\n",
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      "cat_319.jpg belongs to cluster 0\n",
      "cat_32.jpg belongs to cluster 0\n",
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      "cat_4.jpg belongs to cluster 0\n",
      "cat_40.jpg belongs to cluster 0\n",
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      "cat_93.jpg belongs to cluster 1\n",
      "cat_94.jpg belongs to cluster 1\n",
      "cat_95.jpg belongs to cluster 1\n",
      "cat_96.jpg belongs to cluster 1\n",
      "cat_97.jpg belongs to cluster 1\n",
      "cat_98.jpg belongs to cluster 0\n",
      "cat_99.jpg belongs to cluster 1\n"
     ]
    }
   ],
   "source": [
    "# Print which image belongs to which cluster\n",
    "for i, crop_path in enumerate(crop_images):\n",
    "    cluster = labels[i]\n",
    "    print(f\"{os.path.basename(crop_path)} belongs to cluster {cluster}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_folder = 'dataset/annotations'   # Path to save YOLO annotation files\n",
    "os.makedirs(labels_folder, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_yolo_annotations(cropped_image_path, label, box, labels_folder):\n",
    "    \"\"\"\n",
    "    Save YOLO annotation for the given image.\n",
    "    :param cropped_image_path: Path to the cropped image\n",
    "    :param label: Cluster label (0 or 1)\n",
    "    :param box: The bounding box (x1, y1, x2, y2) coordinates\n",
    "    :param labels_folder: Folder to save the annotations\n",
    "    \"\"\"\n",
    "    # Extract image name without extension\n",
    "    image_name = os.path.basename(cropped_image_path).split('.')[0]\n",
    "    \n",
    "    # Normalize the bounding box coordinates\n",
    "    image = cv2.imread(cropped_image_path)\n",
    "    h, w, _ = image.shape\n",
    "\n",
    "    # YOLO requires normalized coordinates: (x_center, y_center, width, height)\n",
    "    x_min, y_min, x_max, y_max = box\n",
    "    x_center = (x_min + x_max) / 2 / w\n",
    "    y_center = (y_min + y_max) / 2 / h\n",
    "    width = (x_max - x_min) / w\n",
    "    height = (y_max - y_min) / h\n",
    "\n",
    "    # Create the annotation file path\n",
    "    annotation_path = os.path.join(labels_folder, f\"{image_name}.txt\")\n",
    "    \n",
    "    # Write the YOLO format annotation\n",
    "    with open(annotation_path, 'w') as f:\n",
    "        # Write the class id and the normalized bounding box info\n",
    "        f.write(f\"{label} {x_center} {y_center} {width} {height}\\n\")\n",
    "    \n",
    "    print(f\"Saved annotation for {image_name}: {annotation_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "cropped_data = []  # List to store cropped image data\n",
    "\n",
    "for result in results:\n",
    "    boxes = result.boxes\n",
    "    img_path = result.path\n",
    "    img = cv2.imread(img_path)\n",
    "\n",
    "    for i, box in enumerate(boxes):\n",
    "        class_id = int(box.cls[0])\n",
    "        if class_id == 15:  # Process only cats (class ID = 15 in COCO dataset)\n",
    "            x_min, y_min, x_max, y_max = map(int, box.xyxy[0])\n",
    "\n",
    "            # Crop and save the image\n",
    "            cropped_cat = img[y_min:y_max, x_min:x_max]\n",
    "            crop_path = os.path.join(output_folder, f\"{os.path.basename(img_path).split('.')[0]}_crop{i}.jpg\")\n",
    "            cv2.imwrite(crop_path, cropped_cat)\n",
    "\n",
    "            # Store metadata for this crop\n",
    "            cropped_data.append({\n",
    "                \"cropped_image\": crop_path,\n",
    "                \"original_image\": img_path,\n",
    "                \"bounding_box\": (x_min, y_min, x_max, y_max),\n",
    "                \"box_index\": i,  # Index of the box in the original image\n",
    "            })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list = []\n",
    "for data in cropped_data:\n",
    "    features = extract_features(data[\"cropped_image\"])\n",
    "    feature_list.append(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "552\n",
      "552\n"
     ]
    }
   ],
   "source": [
    "print(len(cropped_data))\n",
    "print(len(feature_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 5: Clustering with K-Means\n",
    "# Perform K-Means clustering on the extracted features\n",
    "features_array = np.array(feature_list)\n",
    "kmeans = KMeans(n_clusters=2, random_state=42)  # 2 clusters for 2 cats\n",
    "labels = kmeans.fit_predict(features_array)\n",
    "\n",
    "# Add cluster labels to `cropped_data`\n",
    "for i, label in enumerate(labels):\n",
    "    cropped_data[i][\"cluster_label\"] = label "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 6: Generate YOLO Annotations\n",
    "# Group bounding boxes by original image and write YOLO format annotations\n",
    "annotations = {}\n",
    "\n",
    "for data in cropped_data:\n",
    "    original_image = data[\"original_image\"]\n",
    "    box = data[\"bounding_box\"]\n",
    "    class_id = data[\"cluster_label\"]  # Use cluster label as YOLO class ID\n",
    "\n",
    "    # Calculate YOLO format (x_center, y_center, width, height)\n",
    "    x_min, y_min, x_max, y_max = box\n",
    "    img = cv2.imread(original_image)\n",
    "    h, w, _ = img.shape\n",
    "\n",
    "    x_center = (x_min + x_max) / (2 * w)\n",
    "    y_center = (y_min + y_max) / (2 * h)\n",
    "    width = (x_max - x_min) / w\n",
    "    height = (y_max - y_min) / h\n",
    "\n",
    "    # Add to annotations\n",
    "    if original_image not in annotations:\n",
    "        annotations[original_image] = []\n",
    "    annotations[original_image].append((class_id, x_center, y_center, width, height))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\1818949000-IMG-20240118-WA0001.jpg to dataset/annotations/1818949000-IMG-20240118-WA0001.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_103335.jpg to dataset/annotations/20240308_103335.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_141622.jpg to dataset/annotations/20240308_141622.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_184600.jpg to dataset/annotations/20240308_184600.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_195833.jpg to dataset/annotations/20240308_195833.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_200052.jpg to dataset/annotations/20240308_200052.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_200057.jpg to dataset/annotations/20240308_200057.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_200102.jpg to dataset/annotations/20240308_200102.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201439.jpg to dataset/annotations/20240308_201439.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201442.jpg to dataset/annotations/20240308_201442.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201443.jpg to dataset/annotations/20240308_201443.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201446.jpg to dataset/annotations/20240308_201446.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201447.jpg to dataset/annotations/20240308_201447.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201448.jpg to dataset/annotations/20240308_201448.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201452.jpg to dataset/annotations/20240308_201452.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201454.jpg to dataset/annotations/20240308_201454.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201458.jpg to dataset/annotations/20240308_201458.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_201510.jpg to dataset/annotations/20240308_201510.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240308_232857.jpg to dataset/annotations/20240308_232857.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240310_140207.jpg to dataset/annotations/20240310_140207.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240311_195350.jpg to dataset/annotations/20240311_195350.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240311_195352.jpg to dataset/annotations/20240311_195352.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240311_195357.jpg to dataset/annotations/20240311_195357.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240311_195403.jpg to dataset/annotations/20240311_195403.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_180219.jpg to dataset/annotations/20240312_180219.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185655.jpg to dataset/annotations/20240312_185655.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185658.jpg to dataset/annotations/20240312_185658.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185700.jpg to dataset/annotations/20240312_185700.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185702.jpg to dataset/annotations/20240312_185702.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185718(0).jpg to dataset/annotations/20240312_185718(0).txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185718.jpg to dataset/annotations/20240312_185718.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185719.jpg to dataset/annotations/20240312_185719.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185729.jpg to dataset/annotations/20240312_185729.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185730.jpg to dataset/annotations/20240312_185730.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185733.jpg to dataset/annotations/20240312_185733.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240312_185734.jpg to dataset/annotations/20240312_185734.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240313_203246.jpg to dataset/annotations/20240313_203246.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240313_203248.jpg to dataset/annotations/20240313_203248.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_170048.jpg to dataset/annotations/20240315_170048.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_170048_remastered.jpg to dataset/annotations/20240315_170048_remastered.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_171004.jpg to dataset/annotations/20240315_171004.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191222.jpg to dataset/annotations/20240315_191222.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191224(0).jpg to dataset/annotations/20240315_191224(0).txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191224.jpg to dataset/annotations/20240315_191224.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191225.jpg to dataset/annotations/20240315_191225.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191226.jpg to dataset/annotations/20240315_191226.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240315_191227.jpg to dataset/annotations/20240315_191227.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104313.jpg to dataset/annotations/20240316_104313.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104328.jpg to dataset/annotations/20240316_104328.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104330.jpg to dataset/annotations/20240316_104330.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_104331.jpg to dataset/annotations/20240316_104331.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_220153.jpg to dataset/annotations/20240316_220153.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240316_220210.jpg to dataset/annotations/20240316_220210.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002355.jpg to dataset/annotations/20240317_002355.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002404.jpg to dataset/annotations/20240317_002404.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002407.jpg to dataset/annotations/20240317_002407.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002416.jpg to dataset/annotations/20240317_002416.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_002419.jpg to dataset/annotations/20240317_002419.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204422.jpg to dataset/annotations/20240317_204422.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204426.jpg to dataset/annotations/20240317_204426.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204429.jpg to dataset/annotations/20240317_204429.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204431.jpg to dataset/annotations/20240317_204431.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204434.jpg to dataset/annotations/20240317_204434.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204436.jpg to dataset/annotations/20240317_204436.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240317_204503.jpg to dataset/annotations/20240317_204503.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240318_194443.jpg to dataset/annotations/20240318_194443.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240320_171347.jpg to dataset/annotations/20240320_171347.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185912.jpg to dataset/annotations/20240323_185912.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185914.jpg to dataset/annotations/20240323_185914.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185916.jpg to dataset/annotations/20240323_185916.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185917.jpg to dataset/annotations/20240323_185917.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185921.jpg to dataset/annotations/20240323_185921.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185922.jpg to dataset/annotations/20240323_185922.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185924.jpg to dataset/annotations/20240323_185924.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_185950.jpg to dataset/annotations/20240323_185950.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_190014.jpg to dataset/annotations/20240323_190014.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193424.jpg to dataset/annotations/20240323_193424.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193431.jpg to dataset/annotations/20240323_193431.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240323_193433.jpg to dataset/annotations/20240323_193433.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175352.jpg to dataset/annotations/20240324_175352.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175354.jpg to dataset/annotations/20240324_175354.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175358.jpg to dataset/annotations/20240324_175358.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175402.jpg to dataset/annotations/20240324_175402.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175405.jpg to dataset/annotations/20240324_175405.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175406.jpg to dataset/annotations/20240324_175406.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240324_175407.jpg to dataset/annotations/20240324_175407.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240325_163728.jpg to dataset/annotations/20240325_163728.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_131427.jpg to dataset/annotations/20240326_131427.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_131431.jpg to dataset/annotations/20240326_131431.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183749.jpg to dataset/annotations/20240326_183749.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183757.jpg to dataset/annotations/20240326_183757.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183759.jpg to dataset/annotations/20240326_183759.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183803.jpg to dataset/annotations/20240326_183803.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240326_183810.jpg to dataset/annotations/20240326_183810.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135547.jpg to dataset/annotations/20240327_135547.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135550.jpg to dataset/annotations/20240327_135550.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135553.jpg to dataset/annotations/20240327_135553.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135555.jpg to dataset/annotations/20240327_135555.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240327_135643.jpg to dataset/annotations/20240327_135643.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240329_223942(0).jpg to dataset/annotations/20240329_223942(0).txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240329_223942.jpg to dataset/annotations/20240329_223942.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240329_223945.jpg to dataset/annotations/20240329_223945.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224228.jpg to dataset/annotations/20240404_224228.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224230.jpg to dataset/annotations/20240404_224230.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224232.jpg to dataset/annotations/20240404_224232.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224233.jpg to dataset/annotations/20240404_224233.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224236.jpg to dataset/annotations/20240404_224236.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224237.jpg to dataset/annotations/20240404_224237.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240404_224242.jpg to dataset/annotations/20240404_224242.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152307.jpg to dataset/annotations/20240405_152307.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152308.jpg to dataset/annotations/20240405_152308.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152310.jpg to dataset/annotations/20240405_152310.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152316.jpg to dataset/annotations/20240405_152316.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152338.jpg to dataset/annotations/20240405_152338.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240405_152341.jpg to dataset/annotations/20240405_152341.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233500.jpg to dataset/annotations/20240413_233500.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233501.jpg to dataset/annotations/20240413_233501.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233502.jpg to dataset/annotations/20240413_233502.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233503.jpg to dataset/annotations/20240413_233503.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233504.jpg to dataset/annotations/20240413_233504.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233505.jpg to dataset/annotations/20240413_233505.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233506.jpg to dataset/annotations/20240413_233506.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_233600.jpg to dataset/annotations/20240413_233600.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234112.jpg to dataset/annotations/20240413_234112.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234115.jpg to dataset/annotations/20240413_234115.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234507.jpg to dataset/annotations/20240413_234507.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234510.jpg to dataset/annotations/20240413_234510.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234512.jpg to dataset/annotations/20240413_234512.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234514.jpg to dataset/annotations/20240413_234514.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234520.jpg to dataset/annotations/20240413_234520.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234523.jpg to dataset/annotations/20240413_234523.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234524.jpg to dataset/annotations/20240413_234524.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234630.jpg to dataset/annotations/20240413_234630.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_234749.jpg to dataset/annotations/20240413_234749.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240413_235449.jpg to dataset/annotations/20240413_235449.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240414_204832.jpg to dataset/annotations/20240414_204832.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240415_191936.jpg to dataset/annotations/20240415_191936.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240416_131400.jpg to dataset/annotations/20240416_131400.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240423_103248.jpg to dataset/annotations/20240423_103248.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240424_204037.jpg to dataset/annotations/20240424_204037.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240425_224816.jpg to dataset/annotations/20240425_224816.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240425_224817(0).jpg to dataset/annotations/20240425_224817(0).txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240425_224817.jpg to dataset/annotations/20240425_224817.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240425_224819.jpg to dataset/annotations/20240425_224819.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240426_133718.jpg to dataset/annotations/20240426_133718.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240426_133720.jpg to dataset/annotations/20240426_133720.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_113516.jpg to dataset/annotations/20240427_113516.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_113519.jpg to dataset/annotations/20240427_113519.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_130031.jpg to dataset/annotations/20240427_130031.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_130032.jpg to dataset/annotations/20240427_130032.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240427_130041.jpg to dataset/annotations/20240427_130041.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240503_173937.jpg to dataset/annotations/20240503_173937.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240503_173948.jpg to dataset/annotations/20240503_173948.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185557.jpg to dataset/annotations/20240509_185557.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185559.jpg to dataset/annotations/20240509_185559.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185601.jpg to dataset/annotations/20240509_185601.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185604.jpg to dataset/annotations/20240509_185604.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185606.jpg to dataset/annotations/20240509_185606.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185608.jpg to dataset/annotations/20240509_185608.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240509_185612.jpg to dataset/annotations/20240509_185612.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_185650.jpg to dataset/annotations/20240511_185650.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_185651.jpg to dataset/annotations/20240511_185651.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_185652.jpg to dataset/annotations/20240511_185652.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_194053.jpg to dataset/annotations/20240511_194053.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240511_210228.jpg to dataset/annotations/20240511_210228.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_071046.jpg to dataset/annotations/20240512_071046.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_071050.jpg to dataset/annotations/20240512_071050.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_071052.jpg to dataset/annotations/20240512_071052.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_071053.jpg to dataset/annotations/20240512_071053.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_072915.jpg to dataset/annotations/20240512_072915.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_231919.jpg to dataset/annotations/20240512_231919.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_231920.jpg to dataset/annotations/20240512_231920.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_235311.jpg to dataset/annotations/20240512_235311.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_235315.jpg to dataset/annotations/20240512_235315.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240512_235317.jpg to dataset/annotations/20240512_235317.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090846.jpg to dataset/annotations/20240513_090846.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090900.jpg to dataset/annotations/20240513_090900.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090907.jpg to dataset/annotations/20240513_090907.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_090911.jpg to dataset/annotations/20240513_090911.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180307.jpg to dataset/annotations/20240513_180307.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180310.jpg to dataset/annotations/20240513_180310.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180316.jpg to dataset/annotations/20240513_180316.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180318.jpg to dataset/annotations/20240513_180318.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180331.jpg to dataset/annotations/20240513_180331.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180334.jpg to dataset/annotations/20240513_180334.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180532.jpg to dataset/annotations/20240513_180532.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180847.jpg to dataset/annotations/20240513_180847.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180849.jpg to dataset/annotations/20240513_180849.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180853.jpg to dataset/annotations/20240513_180853.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240513_180937.jpg to dataset/annotations/20240513_180937.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_193135.jpg to dataset/annotations/20240514_193135.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_193136.jpg to dataset/annotations/20240514_193136.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_213919(0).jpg to dataset/annotations/20240514_213919(0).txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_213919.jpg to dataset/annotations/20240514_213919.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_223849.jpg to dataset/annotations/20240514_223849.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240514_223851.jpg to dataset/annotations/20240514_223851.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240603_225020(0).jpg to dataset/annotations/20240603_225020(0).txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240603_225020.jpg to dataset/annotations/20240603_225020.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192457.jpg to dataset/annotations/20240611_192457.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192458(0).jpg to dataset/annotations/20240611_192458(0).txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192458.jpg to dataset/annotations/20240611_192458.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192459.jpg to dataset/annotations/20240611_192459.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192501(0).jpg to dataset/annotations/20240611_192501(0).txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192501.jpg to dataset/annotations/20240611_192501.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192502.jpg to dataset/annotations/20240611_192502.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192504.jpg to dataset/annotations/20240611_192504.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192506.jpg to dataset/annotations/20240611_192506.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192512(0).jpg to dataset/annotations/20240611_192512(0).txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192513.jpg to dataset/annotations/20240611_192513.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192514.jpg to dataset/annotations/20240611_192514.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192515(0).jpg to dataset/annotations/20240611_192515(0).txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192515.jpg to dataset/annotations/20240611_192515.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192534.jpg to dataset/annotations/20240611_192534.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240611_192538.jpg to dataset/annotations/20240611_192538.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205057.jpg to dataset/annotations/20240618_205057.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205103.jpg to dataset/annotations/20240618_205103.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205105.jpg to dataset/annotations/20240618_205105.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205110.jpg to dataset/annotations/20240618_205110.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205113.jpg to dataset/annotations/20240618_205113.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205119.jpg to dataset/annotations/20240618_205119.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240618_205121.jpg to dataset/annotations/20240618_205121.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240623_192721.jpg to dataset/annotations/20240623_192721.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240624_200545.jpg to dataset/annotations/20240624_200545.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240624_200548.jpg to dataset/annotations/20240624_200548.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240624_200549.jpg to dataset/annotations/20240624_200549.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012349.jpg to dataset/annotations/20240628_012349.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012352.jpg to dataset/annotations/20240628_012352.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012400.jpg to dataset/annotations/20240628_012400.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012402.jpg to dataset/annotations/20240628_012402.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012407.jpg to dataset/annotations/20240628_012407.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_012408.jpg to dataset/annotations/20240628_012408.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_231410.jpg to dataset/annotations/20240628_231410.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_231412.jpg to dataset/annotations/20240628_231412.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240628_231413.jpg to dataset/annotations/20240628_231413.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240704_202652.jpg to dataset/annotations/20240704_202652.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240711_221427.jpg to dataset/annotations/20240711_221427.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240711_221435.jpg to dataset/annotations/20240711_221435.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165019.jpg to dataset/annotations/20240720_165019.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165021.jpg to dataset/annotations/20240720_165021.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165025.jpg to dataset/annotations/20240720_165025.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165028.jpg to dataset/annotations/20240720_165028.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165029.jpg to dataset/annotations/20240720_165029.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165031.jpg to dataset/annotations/20240720_165031.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165033.jpg to dataset/annotations/20240720_165033.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_165040.jpg to dataset/annotations/20240720_165040.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_224858.jpg to dataset/annotations/20240720_224858.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_224902.jpg to dataset/annotations/20240720_224902.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_224903.jpg to dataset/annotations/20240720_224903.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240720_224905.jpg to dataset/annotations/20240720_224905.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240726_225449.jpg to dataset/annotations/20240726_225449.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240727_085826.jpg to dataset/annotations/20240727_085826.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240802_203206.jpg to dataset/annotations/20240802_203206.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102342.jpg to dataset/annotations/20240816_102342.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102347.jpg to dataset/annotations/20240816_102347.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102351.jpg to dataset/annotations/20240816_102351.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102354.jpg to dataset/annotations/20240816_102354.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102402.jpg to dataset/annotations/20240816_102402.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102407.jpg to dataset/annotations/20240816_102407.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102411.jpg to dataset/annotations/20240816_102411.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_102415.jpg to dataset/annotations/20240816_102415.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240816_113806.jpg to dataset/annotations/20240816_113806.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_145117.jpg to dataset/annotations/20240817_145117.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_145118.jpg to dataset/annotations/20240817_145118.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171056.jpg to dataset/annotations/20240817_171056.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171058.jpg to dataset/annotations/20240817_171058.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171104.jpg to dataset/annotations/20240817_171104.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171110.jpg to dataset/annotations/20240817_171110.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_171111.jpg to dataset/annotations/20240817_171111.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_203211.jpg to dataset/annotations/20240817_203211.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_203213.jpg to dataset/annotations/20240817_203213.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_203217.jpg to dataset/annotations/20240817_203217.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240817_203219.jpg to dataset/annotations/20240817_203219.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_164652.jpg to dataset/annotations/20240819_164652.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_164655.jpg to dataset/annotations/20240819_164655.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_171436.jpg to dataset/annotations/20240819_171436.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_171438.jpg to dataset/annotations/20240819_171438.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_171443.jpg to dataset/annotations/20240819_171443.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_173348.jpg to dataset/annotations/20240819_173348.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_173355.jpg to dataset/annotations/20240819_173355.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_173426.jpg to dataset/annotations/20240819_173426.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211309.jpg to dataset/annotations/20240819_211309.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211312.jpg to dataset/annotations/20240819_211312.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211313.jpg to dataset/annotations/20240819_211313.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211320.jpg to dataset/annotations/20240819_211320.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211321.jpg to dataset/annotations/20240819_211321.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211322.jpg to dataset/annotations/20240819_211322.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211330.jpg to dataset/annotations/20240819_211330.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240819_211333.jpg to dataset/annotations/20240819_211333.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240820_144924.jpg to dataset/annotations/20240820_144924.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240820_145200.jpg to dataset/annotations/20240820_145200.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144759.jpg to dataset/annotations/20240826_144759.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144801.jpg to dataset/annotations/20240826_144801.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144806.jpg to dataset/annotations/20240826_144806.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144808.jpg to dataset/annotations/20240826_144808.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_144935.jpg to dataset/annotations/20240826_144935.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145214.jpg to dataset/annotations/20240826_145214.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145834.jpg to dataset/annotations/20240826_145834.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145846.jpg to dataset/annotations/20240826_145846.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_145849.jpg to dataset/annotations/20240826_145849.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150807.jpg to dataset/annotations/20240826_150807.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150810.jpg to dataset/annotations/20240826_150810.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150813.jpg to dataset/annotations/20240826_150813.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_150852.jpg to dataset/annotations/20240826_150852.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_152201.jpg to dataset/annotations/20240826_152201.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_152205.jpg to dataset/annotations/20240826_152205.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_152211.jpg to dataset/annotations/20240826_152211.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240826_152212.jpg to dataset/annotations/20240826_152212.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240827_224300.jpg to dataset/annotations/20240827_224300.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240827_224303.jpg to dataset/annotations/20240827_224303.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240828_102222.jpg to dataset/annotations/20240828_102222.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240828_102229.jpg to dataset/annotations/20240828_102229.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240903_211802.jpg to dataset/annotations/20240903_211802.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240903_211803.jpg to dataset/annotations/20240903_211803.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240907_115105.jpg to dataset/annotations/20240907_115105.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240907_115118.jpg to dataset/annotations/20240907_115118.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_190713.jpg to dataset/annotations/20240908_190713.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_190829.jpg to dataset/annotations/20240908_190829.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_194737.jpg to dataset/annotations/20240908_194737.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_194739.jpg to dataset/annotations/20240908_194739.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240908_194752.jpg to dataset/annotations/20240908_194752.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240909_000642.jpg to dataset/annotations/20240909_000642.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240909_000646.jpg to dataset/annotations/20240909_000646.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124611.jpg to dataset/annotations/20240910_124611.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124612.jpg to dataset/annotations/20240910_124612.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124720.jpg to dataset/annotations/20240910_124720.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124723.jpg to dataset/annotations/20240910_124723.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124734.jpg to dataset/annotations/20240910_124734.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240910_124741.jpg to dataset/annotations/20240910_124741.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091314.jpg to dataset/annotations/20240911_091314.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091319.jpg to dataset/annotations/20240911_091319.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091320.jpg to dataset/annotations/20240911_091320.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091329.jpg to dataset/annotations/20240911_091329.txt\n",
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      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_091905.jpg to dataset/annotations/20240911_091905.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_193740.jpg to dataset/annotations/20240911_193740.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240911_193742.jpg to dataset/annotations/20240911_193742.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174204.jpg to dataset/annotations/20240912_174204.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174614.jpg to dataset/annotations/20240912_174614.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174616.jpg to dataset/annotations/20240912_174616.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_174636.jpg to dataset/annotations/20240912_174636.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_215106.jpg to dataset/annotations/20240912_215106.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_215113.jpg to dataset/annotations/20240912_215113.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240912_215119.jpg to dataset/annotations/20240912_215119.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240920_192026.jpg to dataset/annotations/20240920_192026.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240924_111824.jpg to dataset/annotations/20240924_111824.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240924_111829.jpg to dataset/annotations/20240924_111829.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240924_111831.jpg to dataset/annotations/20240924_111831.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162434.jpg to dataset/annotations/20240926_162434.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162437.jpg to dataset/annotations/20240926_162437.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162442.jpg to dataset/annotations/20240926_162442.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162443.jpg to dataset/annotations/20240926_162443.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162445.jpg to dataset/annotations/20240926_162445.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162452.jpg to dataset/annotations/20240926_162452.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162458.jpg to dataset/annotations/20240926_162458.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162500.jpg to dataset/annotations/20240926_162500.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240926_162503.jpg to dataset/annotations/20240926_162503.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_100748.jpg to dataset/annotations/20240929_100748.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_101009.jpg to dataset/annotations/20240929_101009.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_101014.jpg to dataset/annotations/20240929_101014.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_101016.jpg to dataset/annotations/20240929_101016.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240929_184523.jpg to dataset/annotations/20240929_184523.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20240930_151259.jpg to dataset/annotations/20240930_151259.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241002_212916.jpg to dataset/annotations/20241002_212916.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241002_212925.jpg to dataset/annotations/20241002_212925.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241002_212949.jpg to dataset/annotations/20241002_212949.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241004_112619.jpg to dataset/annotations/20241004_112619.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241004_112626.jpg to dataset/annotations/20241004_112626.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241008_094705.jpg to dataset/annotations/20241008_094705.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241008_094939.jpg to dataset/annotations/20241008_094939.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241008_094940.jpg to dataset/annotations/20241008_094940.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241009_121558.jpg to dataset/annotations/20241009_121558.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191519.jpg to dataset/annotations/20241013_191519.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191520.jpg to dataset/annotations/20241013_191520.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191523.jpg to dataset/annotations/20241013_191523.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191525.jpg to dataset/annotations/20241013_191525.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191527.jpg to dataset/annotations/20241013_191527.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241013_191529.jpg to dataset/annotations/20241013_191529.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_132210.jpg to dataset/annotations/20241017_132210.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_132216.jpg to dataset/annotations/20241017_132216.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_184948.jpg to dataset/annotations/20241017_184948.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241017_184949.jpg to dataset/annotations/20241017_184949.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241022_023307.jpg to dataset/annotations/20241022_023307.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241024_134452.jpg to dataset/annotations/20241024_134452.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241101_145241.jpg to dataset/annotations/20241101_145241.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241115_195434.jpg to dataset/annotations/20241115_195434.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241115_195439.jpg to dataset/annotations/20241115_195439.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241115_200624.jpg to dataset/annotations/20241115_200624.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241115_200626.jpg to dataset/annotations/20241115_200626.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241124_220257.jpg to dataset/annotations/20241124_220257.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241124_220259.jpg to dataset/annotations/20241124_220259.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241206_164037.jpg to dataset/annotations/20241206_164037.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241206_164040.jpg to dataset/annotations/20241206_164040.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241217_102418.jpg to dataset/annotations/20241217_102418.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132045.jpg to dataset/annotations/20241222_132045.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132051.jpg to dataset/annotations/20241222_132051.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132058.jpg to dataset/annotations/20241222_132058.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132101.jpg to dataset/annotations/20241222_132101.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241222_132106.jpg to dataset/annotations/20241222_132106.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_130054.jpg to dataset/annotations/20241225_130054.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134500.jpg to dataset/annotations/20241225_134500.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_134627.jpg to dataset/annotations/20241225_134627.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135201.jpg to dataset/annotations/20241225_135201.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135219.jpg to dataset/annotations/20241225_135219.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135343(0).jpg to dataset/annotations/20241225_135343(0).txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135343.jpg to dataset/annotations/20241225_135343.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135345.jpg to dataset/annotations/20241225_135345.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135347.jpg to dataset/annotations/20241225_135347.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135352.jpg to dataset/annotations/20241225_135352.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135354.jpg to dataset/annotations/20241225_135354.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135710.jpg to dataset/annotations/20241225_135710.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241225_135712.jpg to dataset/annotations/20241225_135712.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241227_150819.jpg to dataset/annotations/20241227_150819.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20241227_150826.jpg to dataset/annotations/20241227_150826.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250105_143721.jpg to dataset/annotations/20250105_143721.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250105_143723.jpg to dataset/annotations/20250105_143723.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250105_143727.jpg to dataset/annotations/20250105_143727.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250112_203850.jpg to dataset/annotations/20250112_203850.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250112_203857.jpg to dataset/annotations/20250112_203857.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250112_203858.jpg to dataset/annotations/20250112_203858.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250116_174816.jpg to dataset/annotations/20250116_174816.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250117_172955.jpg to dataset/annotations/20250117_172955.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250120_225956.jpg to dataset/annotations/20250120_225956.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250121_190039.jpg to dataset/annotations/20250121_190039.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250121_190043.jpg to dataset/annotations/20250121_190043.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142944.jpg to dataset/annotations/20250122_142944.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142946.jpg to dataset/annotations/20250122_142946.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142948.jpg to dataset/annotations/20250122_142948.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142949.jpg to dataset/annotations/20250122_142949.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142953.jpg to dataset/annotations/20250122_142953.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\20250122_142958.jpg to dataset/annotations/20250122_142958.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\IMG_20240318_231934_252.webp to dataset/annotations/IMG_20240318_231934_252.txt\n",
      "Saved YOLO annotation for e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\cats\\IMG_20241207_211456_238.jpg to dataset/annotations/IMG_20241207_211456_238.txt\n"
     ]
    }
   ],
   "source": [
    "annotations_folder = 'dataset/annotations/' \n",
    "# Save YOLO annotations to files\n",
    "for original_image, boxes in annotations.items():\n",
    "    annotation_file = os.path.join(annotations_folder, f\"{os.path.basename(original_image).split('.')[0]}.txt\")\n",
    "    with open(annotation_file, \"w\") as f:\n",
    "        for class_id, x_center, y_center, width, height in boxes:\n",
    "            f.write(f\"{class_id} {x_center} {y_center} {width} {height}\\n\")\n",
    "    print(f\"Saved YOLO annotation for {original_image} to {annotation_file}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the paths\n",
    "original_images_path = \"dataset/Cats\"\n",
    "original_annotations_path = \"dataset/annotations\"\n",
    "\n",
    "# Define the YOLO structure paths\n",
    "output_images_train = \"dataset/images/train\"\n",
    "output_images_val = \"dataset/images/val\"\n",
    "output_labels_train = \"dataset/labels/train\"\n",
    "output_labels_val = \"dataset/labels/val\"\n",
    "\n",
    "# Create YOLO directory structure\n",
    "os.makedirs(output_images_train, exist_ok=True)\n",
    "os.makedirs(output_images_val, exist_ok=True)\n",
    "os.makedirs(output_labels_train, exist_ok=True)\n",
    "os.makedirs(output_labels_val, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get all image and annotation files\n",
    "images = {os.path.splitext(f)[0]: f for f in os.listdir(original_images_path)}\n",
    "annotations = {os.path.splitext(f)[0]: f for f in os.listdir(original_annotations_path) if f.endswith(\".txt\")}\n",
    "\n",
    "# Match images to their annotations (by base name)\n",
    "matched_files = [(images[key], annotations[key]) for key in images if key in annotations]\n",
    "\n",
    "if not matched_files:\n",
    "    raise ValueError(\"No matching images and annotations found!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "558\n",
      "465\n",
      "465\n"
     ]
    }
   ],
   "source": [
    "print(len(images))\n",
    "print(len(annotations))\n",
    "print(len(matched_files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into train and validation sets\n",
    "train_files, val_files = train_test_split(matched_files, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to copy files\n",
    "def copy_files(file_pairs, src_image_folder, src_label_folder, dest_image_folder, dest_label_folder):\n",
    "    for img_file, ann_file in file_pairs:\n",
    "        # Copy image\n",
    "        shutil.copy(os.path.join(src_image_folder, img_file), os.path.join(dest_image_folder, img_file))\n",
    "        # Copy annotation\n",
    "        shutil.copy(os.path.join(src_label_folder, ann_file), os.path.join(dest_label_folder, ann_file))\n",
    "\n",
    "# Copy training files\n",
    "copy_files(train_files, original_images_path, original_annotations_path, output_images_train, output_labels_train)\n",
    "\n",
    "# Copy validation files\n",
    "copy_files(val_files, original_images_path, original_annotations_path, output_images_val, output_labels_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mengine\\trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=cats_dataset.yaml, epochs=50, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=cuda:0, workers=4, project=None, name=cat_detection, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs\\detect\\cat_detection\n",
      "Downloading https://ultralytics.com/assets/Arial.ttf to 'C:\\Users\\danie\\AppData\\Roaming\\Ultralytics\\Arial.ttf'...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 755k/755k [00:00<00:00, 4.81MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overriding model.yaml nc=80 with nc=2\n",
      "\n",
      "                   from  n    params  module                                       arguments                     \n",
      "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
      "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
      "  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n",
      "  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
      "  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n",
      "  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
      "  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
      "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
      "  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n",
      "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
      " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n",
      " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  \n",
      " 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
      " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 \n",
      " 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
      " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n",
      " 22        [15, 18, 21]  1    751702  ultralytics.nn.modules.head.Detect           [2, [64, 128, 256]]           \n",
      "Model summary: 225 layers, 3,011,238 parameters, 3,011,222 gradients, 8.2 GFLOPs\n",
      "\n",
      "Transferred 58/355 items from pretrained weights\n",
      "Freezing layer 'model.22.dfl.conv.weight'\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
      "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5.35M/5.35M [00:00<00:00, 10.1MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning E:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\labels\\train... 372 images, 0 backgrounds, 0 corrupt: 100%|██████████| 372/372 [00:01<00:00, 228.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: E:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\labels\\train.cache\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning E:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\labels\\val... 93 images, 0 backgrounds, 0 corrupt: 100%|██████████| 93/93 [00:00<00:00, 254.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: E:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\labels\\val.cache\n",
      "Plotting labels to runs\\detect\\cat_detection\\labels.jpg... \n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001667, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n",
      "Image sizes 640 train, 640 val\n",
      "Using 4 dataloader workers\n",
      "Logging results to \u001b[1mruns\\detect\\cat_detection\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       1/50      2.12G       2.55      3.182      2.512         11        640: 100%|██████████| 24/24 [00:26<00:00,  1.12s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:14<00:00,  4.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115    0.00543      0.609     0.0607     0.0157\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       2/50      2.12G      1.583      2.203      1.865         15        640: 100%|██████████| 24/24 [00:27<00:00,  1.15s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  1.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.359      0.319      0.215     0.0701\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       3/50      2.11G      1.384      1.862      1.708          6        640: 100%|██████████| 24/24 [00:24<00:00,  1.02s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:02<00:00,  1.38it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.625      0.554      0.586      0.319\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       4/50      2.12G      1.241       1.65       1.58         13        640: 100%|██████████| 24/24 [00:21<00:00,  1.13it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:02<00:00,  1.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.706      0.697      0.769      0.517\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       5/50      2.12G      1.128      1.559      1.515         15        640: 100%|██████████| 24/24 [00:24<00:00,  1.01s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:03<00:00,  1.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.826      0.689      0.836       0.49\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       6/50      2.12G      1.053      1.361      1.438         10        640: 100%|██████████| 24/24 [00:24<00:00,  1.01s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.816      0.691      0.824      0.556\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       7/50      2.12G      1.051      1.384      1.445         12        640: 100%|██████████| 24/24 [00:20<00:00,  1.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  1.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115        0.8      0.693      0.792       0.49\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       8/50      2.12G      1.013      1.359      1.419          9        640: 100%|██████████| 24/24 [00:19<00:00,  1.23it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.828      0.711      0.865      0.627\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       9/50      2.12G     0.9147      1.245      1.348          6        640: 100%|██████████| 24/24 [00:17<00:00,  1.36it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.851      0.777      0.877      0.616\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      10/50      2.14G      0.868      1.111      1.302         12        640: 100%|██████████| 24/24 [00:19<00:00,  1.23it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.795      0.842      0.874      0.642\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      11/50      2.12G     0.8797      1.055      1.339          8        640: 100%|██████████| 24/24 [00:17<00:00,  1.40it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.38it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.874      0.776      0.857      0.657\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      12/50      2.12G     0.9213      1.084      1.343          9        640: 100%|██████████| 24/24 [00:16<00:00,  1.41it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.904      0.804      0.898      0.696\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      13/50      2.13G     0.8823      1.049       1.31         15        640: 100%|██████████| 24/24 [00:17<00:00,  1.38it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115       0.89      0.848      0.911      0.686\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      14/50      2.12G      0.812     0.9593      1.299         10        640: 100%|██████████| 24/24 [00:18<00:00,  1.33it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.53it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.914      0.855      0.928      0.715\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      15/50      2.12G     0.7952     0.9617      1.269         14        640: 100%|██████████| 24/24 [00:17<00:00,  1.34it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.846      0.883      0.901      0.683\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      16/50      2.12G     0.8631     0.9607      1.313          8        640: 100%|██████████| 24/24 [00:18<00:00,  1.32it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.51it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.886      0.824      0.899      0.706\n"
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    },
    {
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     "output_type": "stream",
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      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "      17/50      2.12G     0.8555     0.9272      1.308         12        640: 100%|██████████| 24/24 [00:18<00:00,  1.29it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115       0.88      0.783      0.892      0.671\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "      18/50      2.12G     0.8447     0.9193      1.283          8        640: 100%|██████████| 24/24 [00:20<00:00,  1.18it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.13it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.842      0.809      0.869      0.642\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      19/50      2.12G     0.8271     0.8759       1.28         13        640: 100%|██████████| 24/24 [00:17<00:00,  1.35it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.763      0.777       0.86      0.673\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      20/50      2.12G     0.7735     0.8702      1.233         15        640: 100%|██████████| 24/24 [00:24<00:00,  1.01s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.925      0.875      0.915      0.735\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      21/50      2.12G     0.7427     0.8364      1.233          9        640: 100%|██████████| 24/24 [00:17<00:00,  1.35it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.896       0.87      0.912      0.718\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      22/50      2.12G     0.7543     0.8537      1.239          5        640: 100%|██████████| 24/24 [00:20<00:00,  1.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.905      0.754       0.88      0.711\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      23/50      2.12G     0.7352     0.7998      1.201         11        640: 100%|██████████| 24/24 [00:17<00:00,  1.37it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.35it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115       0.94      0.791       0.89      0.696\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      24/50      2.12G     0.7194     0.7901      1.204         12        640: 100%|██████████| 24/24 [00:18<00:00,  1.28it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115       0.92      0.797      0.903      0.706\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      25/50      2.14G     0.7551      0.798      1.241          8        640: 100%|██████████| 24/24 [00:16<00:00,  1.46it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.942      0.812      0.919      0.724\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      26/50      2.12G     0.6898     0.7524      1.172         10        640: 100%|██████████| 24/24 [00:18<00:00,  1.33it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.907      0.861      0.917      0.734\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      27/50      2.14G      0.708     0.7433        1.2         10        640: 100%|██████████| 24/24 [00:16<00:00,  1.47it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.60it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.951      0.826      0.907        0.7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      28/50      2.12G     0.6944      0.712      1.178         15        640: 100%|██████████| 24/24 [00:16<00:00,  1.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115       0.95      0.865      0.933      0.758\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      29/50      2.12G     0.6836     0.7271      1.179         10        640: 100%|██████████| 24/24 [00:16<00:00,  1.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.945      0.822      0.924      0.749\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      30/50      2.12G     0.6917     0.7292      1.173         19        640: 100%|██████████| 24/24 [00:16<00:00,  1.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115       0.91      0.871      0.937      0.762\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      31/50      2.12G     0.6682     0.6691      1.172         12        640: 100%|██████████| 24/24 [00:16<00:00,  1.42it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.51it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.953      0.825      0.935      0.773\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      32/50      2.12G     0.6513     0.6866      1.161          7        640: 100%|██████████| 24/24 [00:18<00:00,  1.29it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.58it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.936      0.795       0.92      0.747\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      33/50      2.12G     0.6368     0.6484      1.141         10        640: 100%|██████████| 24/24 [00:18<00:00,  1.26it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.62it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.923      0.897      0.943      0.789\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      34/50      2.12G     0.6621     0.6853       1.16          9        640: 100%|██████████| 24/24 [00:17<00:00,  1.37it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.963      0.855      0.952      0.784\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      35/50      2.12G     0.6457     0.6538       1.15         12        640: 100%|██████████| 24/24 [00:17<00:00,  1.41it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.977      0.859      0.953      0.781\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      36/50      2.12G     0.6003      0.631      1.122          6        640: 100%|██████████| 24/24 [00:15<00:00,  1.54it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.57it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115        0.9      0.881      0.943      0.769\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      37/50      2.14G     0.6187     0.6117      1.127         12        640: 100%|██████████| 24/24 [00:17<00:00,  1.36it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.951      0.854      0.947      0.779\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      38/50      2.12G     0.6241     0.6498      1.134         12        640: 100%|██████████| 24/24 [00:17<00:00,  1.36it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.63it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.974      0.833      0.939      0.778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      39/50      2.12G     0.5996     0.6259      1.129          7        640: 100%|██████████| 24/24 [00:17<00:00,  1.40it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.45it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.933      0.872      0.927      0.778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      40/50      2.13G     0.6037     0.6171      1.111         18        640: 100%|██████████| 24/24 [00:16<00:00,  1.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.966      0.854      0.939       0.79\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Closing dataloader mosaic\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      41/50      2.29G     0.5032     0.6274      1.075          4        640: 100%|██████████| 24/24 [00:23<00:00,  1.01it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.977      0.809      0.908      0.733\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      42/50      2.12G     0.4643     0.5305      1.015          5        640: 100%|██████████| 24/24 [00:16<00:00,  1.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.57it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115       0.95      0.867      0.946      0.773\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      43/50      2.12G      0.454     0.5091      1.022          4        640: 100%|██████████| 24/24 [00:16<00:00,  1.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.955      0.881      0.944      0.792\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      44/50      2.12G     0.4417     0.4678     0.9788          7        640: 100%|██████████| 24/24 [00:16<00:00,  1.49it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.915      0.893       0.94      0.794\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      45/50      2.12G     0.4204     0.4607     0.9959          4        640: 100%|██████████| 24/24 [00:16<00:00,  1.47it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.914      0.898      0.947      0.796\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      46/50      2.12G     0.4066     0.4481     0.9655          6        640: 100%|██████████| 24/24 [00:15<00:00,  1.55it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.60it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.932      0.873      0.951      0.801\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      47/50      2.12G     0.4039     0.4364     0.9661          4        640: 100%|██████████| 24/24 [00:19<00:00,  1.24it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:02<00:00,  1.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.941      0.885      0.948      0.797\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      48/50      2.12G     0.3837      0.409     0.9691          5        640: 100%|██████████| 24/24 [00:20<00:00,  1.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.29it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.937      0.891      0.946      0.799\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      49/50      2.12G     0.4021     0.4534     0.9938          4        640: 100%|██████████| 24/24 [00:16<00:00,  1.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.939      0.897      0.947        0.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      50/50      2.12G     0.4031      0.425     0.9895          6        640: 100%|██████████| 24/24 [00:14<00:00,  1.61it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  2.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.943      0.897      0.951      0.802\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "50 epochs completed in 0.383 hours.\n",
      "Optimizer stripped from runs\\detect\\cat_detection\\weights\\last.pt, 6.2MB\n",
      "Optimizer stripped from runs\\detect\\cat_detection\\weights\\best.pt, 6.2MB\n",
      "\n",
      "Validating runs\\detect\\cat_detection\\weights\\best.pt...\n",
      "Ultralytics 8.3.65  Python-3.10.9 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4060 Ti, 8187MiB)\n",
      "Model summary (fused): 168 layers, 3,006,038 parameters, 0 gradients, 8.1 GFLOPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:01<00:00,  1.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.943      0.897      0.951      0.804\n",
      "                   Tom         56         64      0.965      0.853      0.935      0.801\n",
      "              Garfield         48         51      0.922      0.941      0.966      0.806\n",
      "Speed: 2.1ms preprocess, 1.8ms inference, 0.0ms loss, 2.5ms postprocess per image\n",
      "Results saved to \u001b[1mruns\\detect\\cat_detection\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ultralytics.utils.metrics.DetMetrics object with attributes:\n",
       "\n",
       "ap_class_index: array([0, 1])\n",
       "box: ultralytics.utils.metrics.Metric object\n",
       "confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x00000219175CD420>\n",
       "curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']\n",
       "curves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
       "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
       "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
       "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
       "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
       "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
       "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
       "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
       "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
       "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
       "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
       "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
       "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
       "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
       "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
       "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
       "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
       "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
       "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
       "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
       "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
       "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
       "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
       "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
       "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
       "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
       "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
       "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
       "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
       "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
       "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
       "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
       "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
       "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
       "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
       "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
       "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
       "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
       "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
       "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
       "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
       "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[          1,           1,           1, ...,   0.0084524,   0.0042262,           0],\n",
       "       [          1,           1,           1, ...,     0.31677,     0.31677,           0]]), 'Recall', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
       "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
       "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
       "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
       "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
       "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
       "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
       "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
       "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
       "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
       "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
       "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
       "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
       "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
       "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
       "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
       "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
       "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
       "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
       "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
       "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
       "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
       "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
       "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
       "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
       "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
       "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
       "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
       "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
       "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
       "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
       "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
       "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
       "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
       "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
       "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
       "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
       "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
       "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
       "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
       "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
       "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.12365,     0.12365,     0.39479, ...,           0,           0,           0],\n",
       "       [     0.1351,      0.1351,      0.3119, ...,           0,           0,           0]]), 'Confidence', 'F1'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
       "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
       "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
       "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
       "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
       "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
       "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
       "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
       "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
       "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
       "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
       "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
       "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
       "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
       "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
       "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
       "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
       "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
       "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
       "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
       "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
       "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
       "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
       "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
       "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
       "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
       "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
       "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
       "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
       "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
       "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
       "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
       "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
       "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
       "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
       "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
       "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
       "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
       "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
       "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
       "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
       "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[   0.065969,    0.065969,     0.24791, ...,           1,           1,           1],\n",
       "       [   0.072443,    0.072443,     0.18476, ...,           1,           1,           1]]), 'Confidence', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
       "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
       "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
       "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
       "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
       "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
       "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
       "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
       "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
       "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
       "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
       "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
       "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
       "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
       "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
       "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
       "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
       "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
       "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
       "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
       "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
       "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
       "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
       "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
       "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
       "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
       "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
       "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
       "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
       "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
       "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
       "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
       "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
       "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
       "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
       "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
       "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
       "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
       "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
       "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
       "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
       "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.98438,     0.98438,     0.96875, ...,           0,           0,           0],\n",
       "       [          1,           1,           1, ...,           0,           0,           0]]), 'Confidence', 'Recall']]\n",
       "fitness: 0.8184726558170026\n",
       "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n",
       "maps: array([    0.80124,     0.80629])\n",
       "names: {0: 'Tom', 1: 'Garfield'}\n",
       "plot: True\n",
       "results_dict: {'metrics/precision(B)': 0.943492773503834, 'metrics/recall(B)': 0.8970135031545707, 'metrics/mAP50(B)': 0.950867345126137, 'metrics/mAP50-95(B)': 0.8037621347826542, 'fitness': 0.8184726558170026}\n",
       "save_dir: WindowsPath('runs/detect/cat_detection')\n",
       "speed: {'preprocess': 2.1078073850242043, 'inference': 1.8489976083078692, 'loss': 0.0, 'postprocess': 2.4962194504276396}\n",
       "task: 'detect'"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.train(\n",
    "    data=\"cats_dataset.yaml\",  # Path to the dataset YAML file\n",
    "    epochs=50,            # Number of training epochs\n",
    "    imgsz=640,            # Image size (default is 640x640)\n",
    "    batch=16,             # Batch size (adjust based on your GPU memory)\n",
    "    name=\"cat_detection\", # Name of the training run\n",
    "    workers=4             # Number of data-loading workers (adjust as needed)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics 8.3.65  Python-3.10.9 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4060 Ti, 8187MiB)\n",
      "Model summary (fused): 168 layers, 3,006,038 parameters, 0 gradients, 8.1 GFLOPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mScanning E:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\labels\\val.cache... 93 images, 0 backgrounds, 0 corrupt: 100%|██████████| 93/93 [00:00<?, ?it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 6/6 [00:17<00:00,  2.85s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         93        115      0.943      0.898      0.948      0.798\n",
      "                   Tom         56         64      0.965      0.854      0.932      0.789\n",
      "              Garfield         48         51      0.921      0.941      0.965      0.806\n",
      "Speed: 1.7ms preprocess, 4.4ms inference, 0.0ms loss, 3.1ms postprocess per image\n",
      "Results saved to \u001b[1mruns\\detect\\val\u001b[0m\n",
      "ultralytics.utils.metrics.DetMetrics object with attributes:\n",
      "\n",
      "ap_class_index: array([0, 1])\n",
      "box: ultralytics.utils.metrics.Metric object\n",
      "confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x00000218AEB70400>\n",
      "curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']\n",
      "curves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
      "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
      "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
      "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
      "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
      "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
      "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
      "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
      "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
      "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
      "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
      "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
      "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
      "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
      "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
      "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
      "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
      "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
      "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
      "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
      "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
      "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
      "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
      "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
      "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
      "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
      "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
      "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
      "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
      "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
      "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
      "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
      "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
      "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
      "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
      "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
      "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
      "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
      "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
      "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
      "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
      "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[          1,           1,           1, ...,   0.0089789,   0.0044895,           0],\n",
      "       [          1,           1,           1, ...,     0.22667,     0.22667,           0]]), 'Recall', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
      "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
      "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
      "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
      "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
      "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
      "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
      "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
      "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
      "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
      "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
      "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
      "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
      "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
      "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
      "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
      "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
      "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
      "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
      "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
      "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
      "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
      "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
      "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
      "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
      "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
      "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
      "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
      "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
      "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
      "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
      "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
      "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
      "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
      "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
      "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
      "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
      "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
      "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
      "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
      "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
      "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.13084,     0.13103,     0.40692, ...,           0,           0,           0],\n",
      "       [    0.13178,     0.13206,     0.29857, ...,           0,           0,           0]]), 'Confidence', 'F1'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
      "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
      "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
      "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
      "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
      "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
      "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
      "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
      "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
      "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
      "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
      "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
      "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
      "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
      "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
      "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
      "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
      "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
      "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
      "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
      "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
      "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
      "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
      "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
      "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
      "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
      "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
      "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
      "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
      "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
      "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
      "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
      "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
      "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
      "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
      "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
      "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
      "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
      "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
      "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
      "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
      "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[   0.070078,    0.070185,     0.25755, ...,           1,           1,           1],\n",
      "       [   0.070539,    0.070697,     0.17548, ...,           1,           1,           1]]), 'Confidence', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
      "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
      "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
      "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
      "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
      "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
      "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
      "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
      "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
      "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
      "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
      "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
      "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
      "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
      "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
      "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
      "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
      "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
      "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
      "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
      "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
      "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
      "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
      "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
      "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
      "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
      "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
      "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
      "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
      "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
      "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
      "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
      "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
      "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
      "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
      "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
      "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
      "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
      "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
      "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
      "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
      "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.98438,     0.98438,     0.96875, ...,           0,           0,           0],\n",
      "       [          1,           1,           1, ...,           0,           0,           0]]), 'Confidence', 'Recall']]\n",
      "fitness: 0.8126186178355472\n",
      "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n",
      "maps: array([    0.78935,     0.80573])\n",
      "names: {0: 'Tom', 1: 'Garfield'}\n",
      "plot: True\n",
      "results_dict: {'metrics/precision(B)': 0.9430115651391962, 'metrics/recall(B)': 0.8975594798061068, 'metrics/mAP50(B)': 0.9483266511721926, 'metrics/mAP50-95(B)': 0.7975399474648087, 'fitness': 0.8126186178355472}\n",
      "save_dir: WindowsPath('runs/detect/val')\n",
      "speed: {'preprocess': 1.6566168877386276, 'inference': 4.408738946401944, 'loss': 0.0, 'postprocess': 3.1229296038227696}\n",
      "task: 'detect'\n"
     ]
    }
   ],
   "source": [
    "model = YOLO(\"runs/detect/cat_detection/weights/best.pt\")\n",
    "# Validate on the validation dataset\n",
    "metrics = model.val(data=\"cats_dataset.yaml\")\n",
    "print(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/1 e:\\Facultate\\Master\\Anul 1\\CV\\Project\\dataset\\Cats\\20250122_142949.jpg: 640x480 1 Tom, 1 Garfield, 25.0ms\n",
      "Speed: 4.0ms preprocess, 25.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 480)\n",
      "Results saved to \u001b[1mruns\\detect\\predict3\u001b[0m\n",
      "ultralytics.engine.results.Results object with attributes:\n",
      "\n",
      "boxes: ultralytics.engine.results.Boxes object\n",
      "keypoints: None\n",
      "masks: None\n",
      "names: {0: 'Tom', 1: 'Garfield'}\n",
      "obb: None\n",
      "orig_img: array([[[ 64,  50,  38],\n",
      "        [ 67,  53,  41],\n",
      "        [ 69,  55,  43],\n",
      "        ...,\n",
      "        [ 90, 122, 133],\n",
      "        [ 87, 119, 130],\n",
      "        [ 85, 117, 128]],\n",
      "\n",
      "       [[ 63,  49,  37],\n",
      "        [ 66,  52,  40],\n",
      "        [ 70,  56,  44],\n",
      "        ...,\n",
      "        [ 70, 102, 113],\n",
      "        [ 67,  99, 110],\n",
      "        [ 72, 104, 115]],\n",
      "\n",
      "       [[ 66,  52,  40],\n",
      "        [ 67,  53,  41],\n",
      "        [ 71,  57,  45],\n",
      "        ...,\n",
      "        [ 53,  85,  96],\n",
      "        [ 51,  83,  94],\n",
      "        [ 58,  90, 101]],\n",
      "\n",
      "       ...,\n",
      "\n",
      "       [[144, 146, 146],\n",
      "        [147, 149, 149],\n",
      "        [150, 152, 152],\n",
      "        ...,\n",
      "        [108, 112, 113],\n",
      "        [108, 112, 113],\n",
      "        [111, 115, 116]],\n",
      "\n",
      "       [[144, 146, 146],\n",
      "        [146, 148, 148],\n",
      "        [148, 150, 150],\n",
      "        ...,\n",
      "        [104, 108, 109],\n",
      "        [107, 111, 112],\n",
      "        [109, 113, 114]],\n",
      "\n",
      "       [[148, 150, 150],\n",
      "        [148, 150, 150],\n",
      "        [149, 151, 151],\n",
      "        ...,\n",
      "        [103, 107, 108],\n",
      "        [105, 109, 110],\n",
      "        [107, 111, 112]]], dtype=uint8)\n",
      "orig_shape: (4000, 3000)\n",
      "path: 'e:\\\\Facultate\\\\Master\\\\Anul 1\\\\CV\\\\Project\\\\dataset\\\\Cats\\\\20250122_142949.jpg'\n",
      "probs: None\n",
      "save_dir: 'runs\\\\detect\\\\predict3'\n",
      "speed: {'preprocess': 4.025697708129883, 'inference': 25.003910064697266, 'postprocess': 5.973577499389648}\n"
     ]
    }
   ],
   "source": [
    "results = model.predict(source=\"dataset/Cats/20250122_142949.jpg\", save=True, conf=0.5)\n",
    "\n",
    "# View the results\n",
    "for result in results:\n",
    "    print(result)"
   ]
  }
 ],
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