Initial commit: Recycling object detection project

- Added Fast api web application for recycling object detection
- Included YOLOv8 model training notebook
- Set up project structure with datasets and training directories
- Added requirements.txt for dependencies
- Configured .gitignore for Python virtual environment and cache files
This commit is contained in:
boladeE
2025-04-23 19:18:05 +01:00
commit a6e52f8ad0
454 changed files with 2649 additions and 0 deletions
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task: detect
mode: train
model: yolov8n.pt
data: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\data.yaml
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: null
workers: 8
project: null
name: train4
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
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
cfg: null
tracker: botsort.yaml
save_dir: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\runs\detect\train4
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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
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11,76.6196,1.51641,2.49421,1.47669,0.01333,1,0.26881,0.13278,1.27645,2.85958,1.32994,0.0001604,0.0001604,0.0001604
12,83.1946,1.51184,2.30713,1.41096,0.01333,1,0.31061,0.1674,1.25121,2.8077,1.28717,0.000172084,0.000172084,0.000172084
13,90.515,1.51602,2.00888,1.40129,0.01333,1,0.4757,0.24692,1.2561,2.66198,1.25275,0.000182976,0.000182976,0.000182976
14,98.1615,1.37113,1.66197,1.25801,0.01333,1,0.41803,0.22915,1.33355,2.60968,1.30747,0.000193076,0.000193076,0.000193076
15,104.684,1.45391,1.59786,1.37029,0.03362,0.875,0.40816,0.21489,1.36121,2.51034,1.30125,0.000202384,0.000202384,0.000202384
16,111.213,1.39235,1.5949,1.29785,0.33903,0.5,0.4044,0.23843,1.30273,2.48562,1.29281,0.0002109,0.0002109,0.0002109
17,118.594,1.31418,1.49078,1.21216,0.38404,0.375,0.40322,0.23121,1.30583,2.48418,1.32533,0.000218624,0.000218624,0.000218624
18,126.405,1.5303,1.67169,1.35183,0.38404,0.375,0.40322,0.23121,1.30583,2.48418,1.32533,0.000225556,0.000225556,0.000225556
19,133.907,1.39993,1.74236,1.39831,0.0363,0.83333,0.50162,0.2658,1.29758,2.55769,1.43345,0.000231696,0.000231696,0.000231696
20,141.029,1.39252,1.54489,1.34648,0.0363,0.83333,0.50162,0.2658,1.29758,2.55769,1.43345,0.000237044,0.000237044,0.000237044
21,147.427,1.30629,1.43423,1.31285,0.01333,1,0.41838,0.21308,1.26632,2.73525,1.48731,0.0002416,0.0002416,0.0002416
22,154.374,1.38586,1.38663,1.28934,0.01333,1,0.41838,0.21308,1.26632,2.73525,1.48731,0.000245364,0.000245364,0.000245364
23,160.791,1.46137,1.52123,1.29602,0.01333,1,0.33787,0.17736,1.27755,2.86772,1.51195,0.000248336,0.000248336,0.000248336
24,167.375,1.40314,1.38524,1.25211,0.01333,1,0.33787,0.17736,1.27755,2.86772,1.51195,0.000250516,0.000250516,0.000250516
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37,257.244,1.13138,1.10565,1.16992,0.01333,1,0.31294,0.14219,1.42966,2.99147,1.65039,0.000206784,0.000206784,0.000206784
38,263.772,1.18014,1.23762,1.17946,0.01333,1,0.31294,0.14219,1.42966,2.99147,1.65039,0.000197876,0.000197876,0.000197876
39,271.22,1.15402,1.25401,1.1634,0.01278,0.95833,0.29555,0.15313,1.38078,2.9779,1.64063,0.000188176,0.000188176,0.000188176
40,278.293,1.14982,1.1206,1.17784,0.01278,0.95833,0.29555,0.15313,1.38078,2.9779,1.64063,0.000177684,0.000177684,0.000177684
41,285.814,1.11075,1.43055,1.11889,0.01333,1,0.30395,0.17321,1.3201,2.86638,1.56272,0.0001664,0.0001664,0.0001664
42,292.792,1.01372,1.31437,1.10605,0.01333,1,0.30395,0.17321,1.3201,2.86638,1.56272,0.000154324,0.000154324,0.000154324
43,299.236,1.04451,1.35932,1.13138,1,0.36287,0.48233,0.25656,1.25244,2.78164,1.51545,0.000141456,0.000141456,0.000141456
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45,312.829,0.98534,1.24686,1.10526,1,0.22952,0.49823,0.28839,1.20318,2.69778,1.47186,0.000113344,0.000113344,0.000113344
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47,326.22,1.01347,1.19968,1.07937,1,0.16199,0.55856,0.31857,1.15047,2.61299,1.42365,8.2064e-05,8.2064e-05,8.2064e-05
48,332.77,1.02152,1.2012,1.07213,1,0.16199,0.55856,0.31857,1.15047,2.61299,1.42365,6.5236e-05,6.5236e-05,6.5236e-05
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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 5.77103 2.39502 3.74233 2.28906 0.01111 0.83333 0.01388 0.00519 1.8298 3.20789 1.89118 0 0 0
3 2 11.2977 2.55022 3.75033 2.33099 0.01167 0.875 0.01903 0.00579 1.73516 3.21125 1.83719 1.9604e-05 1.9604e-05 1.9604e-05
4 3 20.2221 2.38719 3.79239 2.28421 0.01167 0.875 0.01923 0.00684 1.62975 3.19063 1.76571 3.8416e-05 3.8416e-05 3.8416e-05
5 4 27.4798 2.09943 3.48603 2.07603 0.01222 0.91667 0.02303 0.00844 1.57125 3.15184 1.69627 5.6436e-05 5.6436e-05 5.6436e-05
6 5 34.249 2.12103 3.59398 2.07602 0.01278 0.95833 0.02936 0.01108 1.42444 3.1487 1.60115 7.3664e-05 7.3664e-05 7.3664e-05
7 6 41.5492 1.95239 3.41935 1.81496 0.01278 0.95833 0.04007 0.01562 1.38235 3.06553 1.50544 9.01e-05 9.01e-05 9.01e-05
8 7 48.3184 1.67583 3.28295 1.73853 0.01333 1 0.05015 0.02544 1.30467 3.02673 1.44765 0.000105744 0.000105744 0.000105744
9 8 55.5926 1.62765 3.02834 1.56366 0.01333 1 0.10045 0.04397 1.26542 2.94541 1.40131 0.000120596 0.000120596 0.000120596
10 9 62.7108 1.81498 3.01087 1.61769 0.01333 1 0.17666 0.07956 1.2281 2.94119 1.38457 0.000134656 0.000134656 0.000134656
11 10 69.6239 1.55792 2.75754 1.52268 0.01333 1 0.21511 0.11338 1.29935 2.89992 1.3765 0.000147924 0.000147924 0.000147924
12 11 76.6196 1.51641 2.49421 1.47669 0.01333 1 0.26881 0.13278 1.27645 2.85958 1.32994 0.0001604 0.0001604 0.0001604
13 12 83.1946 1.51184 2.30713 1.41096 0.01333 1 0.31061 0.1674 1.25121 2.8077 1.28717 0.000172084 0.000172084 0.000172084
14 13 90.515 1.51602 2.00888 1.40129 0.01333 1 0.4757 0.24692 1.2561 2.66198 1.25275 0.000182976 0.000182976 0.000182976
15 14 98.1615 1.37113 1.66197 1.25801 0.01333 1 0.41803 0.22915 1.33355 2.60968 1.30747 0.000193076 0.000193076 0.000193076
16 15 104.684 1.45391 1.59786 1.37029 0.03362 0.875 0.40816 0.21489 1.36121 2.51034 1.30125 0.000202384 0.000202384 0.000202384
17 16 111.213 1.39235 1.5949 1.29785 0.33903 0.5 0.4044 0.23843 1.30273 2.48562 1.29281 0.0002109 0.0002109 0.0002109
18 17 118.594 1.31418 1.49078 1.21216 0.38404 0.375 0.40322 0.23121 1.30583 2.48418 1.32533 0.000218624 0.000218624 0.000218624
19 18 126.405 1.5303 1.67169 1.35183 0.38404 0.375 0.40322 0.23121 1.30583 2.48418 1.32533 0.000225556 0.000225556 0.000225556
20 19 133.907 1.39993 1.74236 1.39831 0.0363 0.83333 0.50162 0.2658 1.29758 2.55769 1.43345 0.000231696 0.000231696 0.000231696
21 20 141.029 1.39252 1.54489 1.34648 0.0363 0.83333 0.50162 0.2658 1.29758 2.55769 1.43345 0.000237044 0.000237044 0.000237044
22 21 147.427 1.30629 1.43423 1.31285 0.01333 1 0.41838 0.21308 1.26632 2.73525 1.48731 0.0002416 0.0002416 0.0002416
23 22 154.374 1.38586 1.38663 1.28934 0.01333 1 0.41838 0.21308 1.26632 2.73525 1.48731 0.000245364 0.000245364 0.000245364
24 23 160.791 1.46137 1.52123 1.29602 0.01333 1 0.33787 0.17736 1.27755 2.86772 1.51195 0.000248336 0.000248336 0.000248336
25 24 167.375 1.40314 1.38524 1.25211 0.01333 1 0.33787 0.17736 1.27755 2.86772 1.51195 0.000250516 0.000250516 0.000250516
26 25 173.764 1.52506 1.46222 1.28968 0.01333 1 0.26607 0.17632 1.26241 2.92442 1.52792 0.000251904 0.000251904 0.000251904
27 26 181.025 1.33243 1.39899 1.15744 0.01333 1 0.26607 0.17632 1.26241 2.92442 1.52792 0.0002525 0.0002525 0.0002525
28 27 187.713 1.2929 1.33615 1.17835 0.01333 1 0.36076 0.23671 1.21156 2.89447 1.50917 0.000252304 0.000252304 0.000252304
29 28 193.907 1.38689 1.49024 1.3356 0.01333 1 0.36076 0.23671 1.21156 2.89447 1.50917 0.000251316 0.000251316 0.000251316
30 29 200.496 1.37361 1.46786 1.18527 0.01333 1 0.39021 0.23898 1.23804 2.90968 1.55469 0.000249536 0.000249536 0.000249536
31 30 208.513 1.27135 1.33555 1.16232 0.01333 1 0.39021 0.23898 1.23804 2.90968 1.55469 0.000246964 0.000246964 0.000246964
32 31 215.615 1.29587 1.38724 1.15838 0.01333 1 0.2754 0.18092 1.34405 2.90785 1.58436 0.0002436 0.0002436 0.0002436
33 32 223.531 1.2612 1.30351 1.13205 0.01333 1 0.2754 0.18092 1.34405 2.90785 1.58436 0.000239444 0.000239444 0.000239444
34 33 230.452 1.31623 1.30861 1.13295 0.01278 0.95833 0.32768 0.17477 1.41444 2.90286 1.59092 0.000234496 0.000234496 0.000234496
35 34 238.213 1.28537 1.25581 1.22603 0.01278 0.95833 0.32768 0.17477 1.41444 2.90286 1.59092 0.000228756 0.000228756 0.000228756
36 35 244.394 1.27605 1.22113 1.18731 0.01278 0.95833 0.30914 0.15393 1.44597 2.95759 1.62449 0.000222224 0.000222224 0.000222224
37 36 250.874 1.14524 1.16995 1.09065 0.01278 0.95833 0.30914 0.15393 1.44597 2.95759 1.62449 0.0002149 0.0002149 0.0002149
38 37 257.244 1.13138 1.10565 1.16992 0.01333 1 0.31294 0.14219 1.42966 2.99147 1.65039 0.000206784 0.000206784 0.000206784
39 38 263.772 1.18014 1.23762 1.17946 0.01333 1 0.31294 0.14219 1.42966 2.99147 1.65039 0.000197876 0.000197876 0.000197876
40 39 271.22 1.15402 1.25401 1.1634 0.01278 0.95833 0.29555 0.15313 1.38078 2.9779 1.64063 0.000188176 0.000188176 0.000188176
41 40 278.293 1.14982 1.1206 1.17784 0.01278 0.95833 0.29555 0.15313 1.38078 2.9779 1.64063 0.000177684 0.000177684 0.000177684
42 41 285.814 1.11075 1.43055 1.11889 0.01333 1 0.30395 0.17321 1.3201 2.86638 1.56272 0.0001664 0.0001664 0.0001664
43 42 292.792 1.01372 1.31437 1.10605 0.01333 1 0.30395 0.17321 1.3201 2.86638 1.56272 0.000154324 0.000154324 0.000154324
44 43 299.236 1.04451 1.35932 1.13138 1 0.36287 0.48233 0.25656 1.25244 2.78164 1.51545 0.000141456 0.000141456 0.000141456
45 44 306.258 1.01778 1.20135 1.09701 1 0.36287 0.48233 0.25656 1.25244 2.78164 1.51545 0.000127796 0.000127796 0.000127796
46 45 312.829 0.98534 1.24686 1.10526 1 0.22952 0.49823 0.28839 1.20318 2.69778 1.47186 0.000113344 0.000113344 0.000113344
47 46 319.193 1.09942 1.26626 1.06372 1 0.22952 0.49823 0.28839 1.20318 2.69778 1.47186 9.81e-05 9.81e-05 9.81e-05
48 47 326.22 1.01347 1.19968 1.07937 1 0.16199 0.55856 0.31857 1.15047 2.61299 1.42365 8.2064e-05 8.2064e-05 8.2064e-05
49 48 332.77 1.02152 1.2012 1.07213 1 0.16199 0.55856 0.31857 1.15047 2.61299 1.42365 6.5236e-05 6.5236e-05 6.5236e-05
50 49 339.411 0.92922 1.16594 1.03896 1 0.16688 0.65621 0.38721 1.1464 2.54556 1.38359 4.7616e-05 4.7616e-05 4.7616e-05
51 50 345.931 0.97048 1.14546 1.05849 1 0.16688 0.65621 0.38721 1.1464 2.54556 1.38359 2.9204e-05 2.9204e-05 2.9204e-05
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