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
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task: detect
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mode: train
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model: yolov8n.pt
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data: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\data.yaml
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epochs: 10
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time: null
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patience: 100
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batch: 16
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imgsz: 640
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save: true
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save_period: -1
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cache: false
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device: null
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workers: 8
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project: null
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name: train8
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exist_ok: false
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pretrained: true
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optimizer: auto
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verbose: true
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seed: 0
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deterministic: true
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single_cls: false
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rect: false
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cos_lr: false
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close_mosaic: 10
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resume: false
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amp: true
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fraction: 1.0
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profile: false
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freeze: null
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multi_scale: false
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overlap_mask: true
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mask_ratio: 4
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dropout: 0.0
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val: true
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split: val
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save_json: false
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conf: null
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iou: 0.7
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max_det: 300
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half: false
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dnn: false
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plots: true
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source: null
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vid_stride: 1
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stream_buffer: false
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visualize: false
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augment: false
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agnostic_nms: false
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classes: null
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retina_masks: false
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embed: null
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show: false
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save_frames: false
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save_txt: false
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save_conf: false
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save_crop: false
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show_labels: true
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show_conf: true
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show_boxes: true
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line_width: null
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format: torchscript
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keras: false
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optimize: false
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int8: false
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dynamic: false
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simplify: true
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opset: null
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workspace: null
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nms: false
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lr0: 0.01
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lrf: 0.01
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momentum: 0.937
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weight_decay: 0.0005
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warmup_epochs: 3.0
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warmup_momentum: 0.8
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warmup_bias_lr: 0.1
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box: 7.5
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cls: 0.5
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dfl: 1.5
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pose: 12.0
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kobj: 1.0
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nbs: 64
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hsv_h: 0.015
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hsv_s: 0.7
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hsv_v: 0.4
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degrees: 0.0
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translate: 0.1
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scale: 0.5
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shear: 0.0
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perspective: 0.0
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flipud: 0.0
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fliplr: 0.5
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bgr: 0.0
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mosaic: 1.0
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mixup: 0.0
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copy_paste: 0.0
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copy_paste_mode: flip
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auto_augment: randaugment
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erasing: 0.4
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cfg: null
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tracker: botsort.yaml
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save_dir: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\runs\detect\train8
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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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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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