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Initial commit: Recycling object detection project

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- 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
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boladeE
2025-04-23 19:18:05 +01:00
commit a6e52f8ad0
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datasets/runs/detect/train8/results.csv
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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
1,54.1052,2.09071,3.34134,1.92262,0.01333,1,0.44572,0.27186,1.23051,2.59193,1.24848,0.00018,0.00018,0.00018
2,108.428,1.50925,1.64368,1.30782,0.026,0.79167,0.48938,0.22884,1.44,2.58214,1.33161,0.00034238,0.00034238,0.00034238
3,169.2,1.34332,1.3215,1.15863,0.01333,1,0.58594,0.25415,1.57323,2.57662,1.42461,0.00046516,0.00046516,0.00046516
4,234.459,1.31312,1.2712,1.15172,1,0.47543,0.79618,0.35276,1.61652,2.20651,1.3799,0.00054834,0.00054834,0.00054834
5,294.564,1.26273,1.19739,1.13664,1,0.28525,0.74794,0.36812,1.68514,2.29653,1.41218,0.00059192,0.00059192,0.00059192
6,350.243,1.21539,1.14377,1.12547,0.91649,0.45734,0.70839,0.31517,1.94937,1.85537,1.67103,0.0005959,0.0005959,0.0005959
7,404.798,1.23307,1.12771,1.12239,0.84802,0.45833,0.67201,0.32455,1.8349,1.87145,1.56846,0.00056028,0.00056028,0.00056028
8,461.361,1.11852,1.03717,1.09084,0.92189,0.49228,0.66535,0.37511,1.62371,1.64545,1.42783,0.00048506,0.00048506,0.00048506
9,518.598,1.08484,0.99435,1.08017,0.92847,0.54088,0.70312,0.41016,1.44851,1.34793,1.32552,0.00037024,0.00037024,0.00037024
10,579.541,1.03245,0.95569,1.0672,0.88861,0.66487,0.7698,0.44125,1.74982,1.29988,1.50408,0.00021582,0.00021582,0.00021582
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 54.1052 2.09071 3.34134 1.92262 0.01333 1 0.44572 0.27186 1.23051 2.59193 1.24848 0.00018 0.00018 0.00018
3 2 108.428 1.50925 1.64368 1.30782 0.026 0.79167 0.48938 0.22884 1.44 2.58214 1.33161 0.00034238 0.00034238 0.00034238
4 3 169.2 1.34332 1.3215 1.15863 0.01333 1 0.58594 0.25415 1.57323 2.57662 1.42461 0.00046516 0.00046516 0.00046516
5 4 234.459 1.31312 1.2712 1.15172 1 0.47543 0.79618 0.35276 1.61652 2.20651 1.3799 0.00054834 0.00054834 0.00054834
6 5 294.564 1.26273 1.19739 1.13664 1 0.28525 0.74794 0.36812 1.68514 2.29653 1.41218 0.00059192 0.00059192 0.00059192
7 6 350.243 1.21539 1.14377 1.12547 0.91649 0.45734 0.70839 0.31517 1.94937 1.85537 1.67103 0.0005959 0.0005959 0.0005959
8 7 404.798 1.23307 1.12771 1.12239 0.84802 0.45833 0.67201 0.32455 1.8349 1.87145 1.56846 0.00056028 0.00056028 0.00056028
9 8 461.361 1.11852 1.03717 1.09084 0.92189 0.49228 0.66535 0.37511 1.62371 1.64545 1.42783 0.00048506 0.00048506 0.00048506
10 9 518.598 1.08484 0.99435 1.08017 0.92847 0.54088 0.70312 0.41016 1.44851 1.34793 1.32552 0.00037024 0.00037024 0.00037024
11 10 579.541 1.03245 0.95569 1.0672 0.88861 0.66487 0.7698 0.44125 1.74982 1.29988 1.50408 0.00021582 0.00021582 0.00021582