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AshLynxu 2025-01-23 09:07:58 +02:00
parent 95d57a7289
commit a3ebb6ebb8
42 changed files with 7200 additions and 622 deletions

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.gitignore vendored
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/dataset/cats
/dataset/cats.zip
/dataset
/output
/yolov8n.pt
/runs/detect/*
!/runs/detect/cat_detection
!/runs/detect/val

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cats_dataset.yaml Normal file
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path: E:/Facultate/Master/Anul 1/CV/Project/dataset # Root directory of your dataset
train: images/train # Train image directory
val: images/val # Validation image directory
nc: 2 # Number of classes (2 cats in your case)
names: ["Tom", "Garfield"] # Class names corresponding to cluster labels

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import tensorflow as tf
import tensorflow_datasets as tfds

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task: detect
mode: train
model: yolov8n.pt
data: cats_dataset.yaml
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: cuda:0
workers: 4
project: null
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: null
multi_scale: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
save_hybrid: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
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: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
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: null
tracker: botsort.yaml
save_dir: runs\detect\cat_detection

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epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
1,41.7603,2.54994,3.18243,2.51221,0.00543,0.60861,0.06066,0.01572,2.88929,3.47453,3.39482,0.00038341,0.00038341,0.00038341
2,74.996,1.5829,2.20317,1.86498,0.35884,0.31878,0.215,0.07008,2.83816,2.78914,3.14487,0.000767977,0.000767977,0.000767977
3,108.249,1.38431,1.86215,1.70799,0.62526,0.55407,0.58633,0.31948,1.5763,2.2232,1.98879,0.0011367,0.0011367,0.0011367
4,138.046,1.24139,1.6498,1.58007,0.7061,0.69734,0.76866,0.51716,1.19157,1.55139,1.55139,0.00148958,0.00148958,0.00148958
5,177.51,1.12757,1.55872,1.51474,0.8258,0.68903,0.83567,0.49045,1.28558,1.48416,1.67659,0.00153497,0.00153497,0.00153497
6,213.163,1.05271,1.361,1.43795,0.81593,0.69091,0.824,0.55616,1.09991,1.34204,1.52969,0.00150197,0.00150197,0.00150197
7,242.458,1.05077,1.38409,1.44457,0.79956,0.69286,0.79212,0.49044,1.22665,1.41692,1.60125,0.00146896,0.00146896,0.00146896
8,270.991,1.01271,1.35862,1.41917,0.82757,0.7106,0.86499,0.62663,0.94366,1.22926,1.39691,0.00143595,0.00143595,0.00143595
9,296.441,0.91474,1.24452,1.34834,0.85085,0.77679,0.87678,0.61598,1.03542,1.1298,1.49551,0.00140295,0.00140295,0.00140295
10,323.314,0.86803,1.1113,1.30245,0.7953,0.84163,0.87429,0.64231,0.87863,1.09015,1.28995,0.00136994,0.00136994,0.00136994
11,348.993,0.87972,1.05537,1.33899,0.87419,0.77573,0.85699,0.65747,0.85251,1.04036,1.27638,0.00133693,0.00133693,0.00133693
12,374.694,0.92134,1.08425,1.34283,0.90352,0.80431,0.89774,0.69599,0.81858,0.8944,1.23589,0.00130393,0.00130393,0.00130393
13,400.797,0.88226,1.04856,1.31014,0.88992,0.84843,0.91106,0.68552,0.82365,0.83574,1.26521,0.00127092,0.00127092,0.00127092
14,427.968,0.81201,0.95926,1.29909,0.91449,0.85523,0.92765,0.71476,0.80232,0.81871,1.23408,0.00123791,0.00123791,0.00123791
15,454.189,0.79519,0.96168,1.26909,0.84575,0.88301,0.90129,0.68346,0.79181,0.78937,1.24608,0.00120491,0.00120491,0.00120491
16,479.954,0.86308,0.96065,1.31282,0.88634,0.82399,0.89899,0.70555,0.76829,0.81577,1.23144,0.0011719,0.0011719,0.0011719
17,506.279,0.85551,0.92718,1.3078,0.87993,0.78278,0.89245,0.6706,0.84912,0.82797,1.25843,0.00113889,0.00113889,0.00113889
18,534.252,0.84472,0.91935,1.28251,0.84172,0.80875,0.86914,0.64171,0.90884,0.83304,1.34577,0.00110589,0.00110589,0.00110589
19,561.001,0.82708,0.87588,1.2796,0.7629,0.77727,0.85955,0.67344,0.83244,0.96993,1.28749,0.00107288,0.00107288,0.00107288
20,598.281,0.77351,0.87018,1.23253,0.92505,0.87549,0.91505,0.73492,0.77467,0.66846,1.24465,0.00103987,0.00103987,0.00103987
21,624.738,0.7427,0.83641,1.23337,0.89574,0.86978,0.91214,0.71788,0.77596,0.69304,1.2433,0.00100687,0.00100687,0.00100687
22,653.387,0.75428,0.8537,1.23923,0.90535,0.75404,0.87979,0.71103,0.70279,0.82322,1.19686,0.000973861,0.000973861,0.000973861
23,677.798,0.73519,0.79982,1.20099,0.93995,0.79148,0.88972,0.69598,0.74002,0.73455,1.21654,0.000940855,0.000940855,0.000940855
24,703.367,0.71942,0.79011,1.20355,0.91963,0.79657,0.90314,0.70615,0.78578,0.6963,1.24998,0.000907848,0.000907848,0.000907848
25,728.85,0.75508,0.79804,1.24131,0.9421,0.81162,0.91895,0.72437,0.74649,0.71008,1.22421,0.000874842,0.000874842,0.000874842
26,755.27,0.68981,0.75245,1.17233,0.90671,0.86121,0.91712,0.73366,0.73208,0.63567,1.22266,0.000841835,0.000841835,0.000841835
27,778.886,0.70801,0.74326,1.20027,0.95102,0.82554,0.90723,0.6996,0.79841,0.69897,1.22938,0.000808828,0.000808828,0.000808828
28,804.012,0.69438,0.71202,1.17782,0.94963,0.86504,0.93251,0.75816,0.71698,0.65917,1.21462,0.000775822,0.000775822,0.000775822
29,827.596,0.68358,0.72711,1.17863,0.94478,0.82232,0.92438,0.74928,0.71748,0.64451,1.18104,0.000742815,0.000742815,0.000742815
30,850.945,0.69173,0.72918,1.17348,0.91018,0.87057,0.93745,0.76242,0.67573,0.61438,1.15435,0.000709809,0.000709809,0.000709809
31,875.669,0.66821,0.66905,1.17224,0.95255,0.8251,0.93451,0.77284,0.63139,0.60749,1.11677,0.000676802,0.000676802,0.000676802
32,901.456,0.65135,0.68664,1.16114,0.9364,0.79458,0.92021,0.74713,0.66189,0.64562,1.14592,0.000643795,0.000643795,0.000643795
33,926.333,0.63677,0.64844,1.14138,0.92334,0.89741,0.94348,0.78891,0.65211,0.57955,1.13967,0.000610789,0.000610789,0.000610789
34,949.914,0.66215,0.68526,1.16026,0.96318,0.85524,0.95244,0.78401,0.61719,0.57355,1.12666,0.000577782,0.000577782,0.000577782
35,973.515,0.64566,0.65378,1.14951,0.97695,0.85901,0.95319,0.78117,0.65163,0.56723,1.15153,0.000544776,0.000544776,0.000544776
36,997.299,0.60033,0.63096,1.12215,0.90017,0.881,0.9435,0.76913,0.65355,0.57331,1.15214,0.000511769,0.000511769,0.000511769
37,1022.39,0.6187,0.61173,1.12676,0.95067,0.85356,0.94685,0.77863,0.64569,0.58735,1.14863,0.000478762,0.000478762,0.000478762
38,1046.51,0.62408,0.64977,1.13398,0.9744,0.83322,0.93891,0.77845,0.63145,0.59304,1.14327,0.000445756,0.000445756,0.000445756
39,1069.52,0.59965,0.62592,1.12855,0.9327,0.87231,0.92721,0.77809,0.63875,0.59236,1.14363,0.000412749,0.000412749,0.000412749
40,1093.88,0.60368,0.61713,1.11117,0.96572,0.85427,0.93924,0.78961,0.6273,0.55367,1.11419,0.000379743,0.000379743,0.000379743
41,1148.05,0.50324,0.62738,1.07458,0.97744,0.80879,0.90839,0.73295,0.72751,0.73311,1.20287,0.000346736,0.000346736,0.000346736
42,1172.58,0.46432,0.5305,1.0149,0.94972,0.86703,0.94626,0.77272,0.62908,0.57454,1.13127,0.000313729,0.000313729,0.000313729
43,1196.92,0.45396,0.5091,1.0216,0.95514,0.88087,0.94434,0.79231,0.61548,0.5314,1.11574,0.000280723,0.000280723,0.000280723
44,1219.38,0.44166,0.46775,0.97879,0.91471,0.89346,0.9397,0.79357,0.60267,0.53682,1.10288,0.000247716,0.000247716,0.000247716
45,1243.16,0.42041,0.46066,0.9959,0.91382,0.89782,0.94741,0.79596,0.60978,0.52438,1.13124,0.00021471,0.00021471,0.00021471
46,1265.52,0.40657,0.4481,0.96549,0.93209,0.87258,0.95065,0.80079,0.59726,0.53471,1.11823,0.000181703,0.000181703,0.000181703
47,1294.23,0.40389,0.43643,0.96607,0.94116,0.88465,0.94829,0.79693,0.585,0.54994,1.09351,0.000148696,0.000148696,0.000148696
48,1324.01,0.38368,0.40904,0.96912,0.93682,0.89071,0.94641,0.79852,0.5988,0.53865,1.10804,0.00011569,0.00011569,0.00011569
49,1350.25,0.40215,0.45338,0.99384,0.93855,0.89749,0.94677,0.80032,0.60052,0.53816,1.11297,8.26832e-05,8.26832e-05,8.26832e-05
50,1373.09,0.40307,0.42498,0.98954,0.94335,0.89709,0.95056,0.80202,0.59766,0.53763,1.11256,4.96766e-05,4.96766e-05,4.96766e-05
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 41.7603 2.54994 3.18243 2.51221 0.00543 0.60861 0.06066 0.01572 2.88929 3.47453 3.39482 0.00038341 0.00038341 0.00038341
3 2 74.996 1.5829 2.20317 1.86498 0.35884 0.31878 0.215 0.07008 2.83816 2.78914 3.14487 0.000767977 0.000767977 0.000767977
4 3 108.249 1.38431 1.86215 1.70799 0.62526 0.55407 0.58633 0.31948 1.5763 2.2232 1.98879 0.0011367 0.0011367 0.0011367
5 4 138.046 1.24139 1.6498 1.58007 0.7061 0.69734 0.76866 0.51716 1.19157 1.55139 1.55139 0.00148958 0.00148958 0.00148958
6 5 177.51 1.12757 1.55872 1.51474 0.8258 0.68903 0.83567 0.49045 1.28558 1.48416 1.67659 0.00153497 0.00153497 0.00153497
7 6 213.163 1.05271 1.361 1.43795 0.81593 0.69091 0.824 0.55616 1.09991 1.34204 1.52969 0.00150197 0.00150197 0.00150197
8 7 242.458 1.05077 1.38409 1.44457 0.79956 0.69286 0.79212 0.49044 1.22665 1.41692 1.60125 0.00146896 0.00146896 0.00146896
9 8 270.991 1.01271 1.35862 1.41917 0.82757 0.7106 0.86499 0.62663 0.94366 1.22926 1.39691 0.00143595 0.00143595 0.00143595
10 9 296.441 0.91474 1.24452 1.34834 0.85085 0.77679 0.87678 0.61598 1.03542 1.1298 1.49551 0.00140295 0.00140295 0.00140295
11 10 323.314 0.86803 1.1113 1.30245 0.7953 0.84163 0.87429 0.64231 0.87863 1.09015 1.28995 0.00136994 0.00136994 0.00136994
12 11 348.993 0.87972 1.05537 1.33899 0.87419 0.77573 0.85699 0.65747 0.85251 1.04036 1.27638 0.00133693 0.00133693 0.00133693
13 12 374.694 0.92134 1.08425 1.34283 0.90352 0.80431 0.89774 0.69599 0.81858 0.8944 1.23589 0.00130393 0.00130393 0.00130393
14 13 400.797 0.88226 1.04856 1.31014 0.88992 0.84843 0.91106 0.68552 0.82365 0.83574 1.26521 0.00127092 0.00127092 0.00127092
15 14 427.968 0.81201 0.95926 1.29909 0.91449 0.85523 0.92765 0.71476 0.80232 0.81871 1.23408 0.00123791 0.00123791 0.00123791
16 15 454.189 0.79519 0.96168 1.26909 0.84575 0.88301 0.90129 0.68346 0.79181 0.78937 1.24608 0.00120491 0.00120491 0.00120491
17 16 479.954 0.86308 0.96065 1.31282 0.88634 0.82399 0.89899 0.70555 0.76829 0.81577 1.23144 0.0011719 0.0011719 0.0011719
18 17 506.279 0.85551 0.92718 1.3078 0.87993 0.78278 0.89245 0.6706 0.84912 0.82797 1.25843 0.00113889 0.00113889 0.00113889
19 18 534.252 0.84472 0.91935 1.28251 0.84172 0.80875 0.86914 0.64171 0.90884 0.83304 1.34577 0.00110589 0.00110589 0.00110589
20 19 561.001 0.82708 0.87588 1.2796 0.7629 0.77727 0.85955 0.67344 0.83244 0.96993 1.28749 0.00107288 0.00107288 0.00107288
21 20 598.281 0.77351 0.87018 1.23253 0.92505 0.87549 0.91505 0.73492 0.77467 0.66846 1.24465 0.00103987 0.00103987 0.00103987
22 21 624.738 0.7427 0.83641 1.23337 0.89574 0.86978 0.91214 0.71788 0.77596 0.69304 1.2433 0.00100687 0.00100687 0.00100687
23 22 653.387 0.75428 0.8537 1.23923 0.90535 0.75404 0.87979 0.71103 0.70279 0.82322 1.19686 0.000973861 0.000973861 0.000973861
24 23 677.798 0.73519 0.79982 1.20099 0.93995 0.79148 0.88972 0.69598 0.74002 0.73455 1.21654 0.000940855 0.000940855 0.000940855
25 24 703.367 0.71942 0.79011 1.20355 0.91963 0.79657 0.90314 0.70615 0.78578 0.6963 1.24998 0.000907848 0.000907848 0.000907848
26 25 728.85 0.75508 0.79804 1.24131 0.9421 0.81162 0.91895 0.72437 0.74649 0.71008 1.22421 0.000874842 0.000874842 0.000874842
27 26 755.27 0.68981 0.75245 1.17233 0.90671 0.86121 0.91712 0.73366 0.73208 0.63567 1.22266 0.000841835 0.000841835 0.000841835
28 27 778.886 0.70801 0.74326 1.20027 0.95102 0.82554 0.90723 0.6996 0.79841 0.69897 1.22938 0.000808828 0.000808828 0.000808828
29 28 804.012 0.69438 0.71202 1.17782 0.94963 0.86504 0.93251 0.75816 0.71698 0.65917 1.21462 0.000775822 0.000775822 0.000775822
30 29 827.596 0.68358 0.72711 1.17863 0.94478 0.82232 0.92438 0.74928 0.71748 0.64451 1.18104 0.000742815 0.000742815 0.000742815
31 30 850.945 0.69173 0.72918 1.17348 0.91018 0.87057 0.93745 0.76242 0.67573 0.61438 1.15435 0.000709809 0.000709809 0.000709809
32 31 875.669 0.66821 0.66905 1.17224 0.95255 0.8251 0.93451 0.77284 0.63139 0.60749 1.11677 0.000676802 0.000676802 0.000676802
33 32 901.456 0.65135 0.68664 1.16114 0.9364 0.79458 0.92021 0.74713 0.66189 0.64562 1.14592 0.000643795 0.000643795 0.000643795
34 33 926.333 0.63677 0.64844 1.14138 0.92334 0.89741 0.94348 0.78891 0.65211 0.57955 1.13967 0.000610789 0.000610789 0.000610789
35 34 949.914 0.66215 0.68526 1.16026 0.96318 0.85524 0.95244 0.78401 0.61719 0.57355 1.12666 0.000577782 0.000577782 0.000577782
36 35 973.515 0.64566 0.65378 1.14951 0.97695 0.85901 0.95319 0.78117 0.65163 0.56723 1.15153 0.000544776 0.000544776 0.000544776
37 36 997.299 0.60033 0.63096 1.12215 0.90017 0.881 0.9435 0.76913 0.65355 0.57331 1.15214 0.000511769 0.000511769 0.000511769
38 37 1022.39 0.6187 0.61173 1.12676 0.95067 0.85356 0.94685 0.77863 0.64569 0.58735 1.14863 0.000478762 0.000478762 0.000478762
39 38 1046.51 0.62408 0.64977 1.13398 0.9744 0.83322 0.93891 0.77845 0.63145 0.59304 1.14327 0.000445756 0.000445756 0.000445756
40 39 1069.52 0.59965 0.62592 1.12855 0.9327 0.87231 0.92721 0.77809 0.63875 0.59236 1.14363 0.000412749 0.000412749 0.000412749
41 40 1093.88 0.60368 0.61713 1.11117 0.96572 0.85427 0.93924 0.78961 0.6273 0.55367 1.11419 0.000379743 0.000379743 0.000379743
42 41 1148.05 0.50324 0.62738 1.07458 0.97744 0.80879 0.90839 0.73295 0.72751 0.73311 1.20287 0.000346736 0.000346736 0.000346736
43 42 1172.58 0.46432 0.5305 1.0149 0.94972 0.86703 0.94626 0.77272 0.62908 0.57454 1.13127 0.000313729 0.000313729 0.000313729
44 43 1196.92 0.45396 0.5091 1.0216 0.95514 0.88087 0.94434 0.79231 0.61548 0.5314 1.11574 0.000280723 0.000280723 0.000280723
45 44 1219.38 0.44166 0.46775 0.97879 0.91471 0.89346 0.9397 0.79357 0.60267 0.53682 1.10288 0.000247716 0.000247716 0.000247716
46 45 1243.16 0.42041 0.46066 0.9959 0.91382 0.89782 0.94741 0.79596 0.60978 0.52438 1.13124 0.00021471 0.00021471 0.00021471
47 46 1265.52 0.40657 0.4481 0.96549 0.93209 0.87258 0.95065 0.80079 0.59726 0.53471 1.11823 0.000181703 0.000181703 0.000181703
48 47 1294.23 0.40389 0.43643 0.96607 0.94116 0.88465 0.94829 0.79693 0.585 0.54994 1.09351 0.000148696 0.000148696 0.000148696
49 48 1324.01 0.38368 0.40904 0.96912 0.93682 0.89071 0.94641 0.79852 0.5988 0.53865 1.10804 0.00011569 0.00011569 0.00011569
50 49 1350.25 0.40215 0.45338 0.99384 0.93855 0.89749 0.94677 0.80032 0.60052 0.53816 1.11297 8.26832e-05 8.26832e-05 8.26832e-05
51 50 1373.09 0.40307 0.42498 0.98954 0.94335 0.89709 0.95056 0.80202 0.59766 0.53763 1.11256 4.96766e-05 4.96766e-05 4.96766e-05

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