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‎.gitignore‎

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@@ -2,4 +2,5 @@ runs/
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*/__pycache__/
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__pycache__
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weights/
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traced_model.pt
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traced_model.pt
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.vscode/

‎data/coco.yaml‎

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download: bash ./scripts/get_coco.sh
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: ./coco/train2017.txt # 118287 images
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val: ./coco/val2017.txt # 5000 images
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test: ./coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
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train: ./coco/train2017.txt # 118287 images
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val: ./coco/val2017.txt # 5000 images
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test: ./coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
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# number of classes
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nc: 80
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# class names
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names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
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'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
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'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
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'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
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'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
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'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
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'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
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'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
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'hair drier', 'toothbrush' ]
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names:
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[
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"person",
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"bicycle",
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"car",
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"motorcycle",
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"airplane",
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"bus",
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"train",
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"truck",
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"boat",
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"traffic light",
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"fire hydrant",
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"stop sign",
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"parking meter",
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"bench",
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"bird",
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"cat",
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"dog",
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"horse",
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"sheep",
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"cow",
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"elephant",
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"bear",
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"zebra",
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"giraffe",
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"backpack",
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"umbrella",
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"handbag",
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"tie",
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"suitcase",
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"frisbee",
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"skis",
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"snowboard",
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"sports ball",
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"kite",
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"baseball bat",
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"baseball glove",
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"skateboard",
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"surfboard",
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"tennis racket",
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"bottle",
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"wine glass",
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"cup",
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"fork",
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"knife",
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"spoon",
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"bowl",
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"banana",
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"apple",
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"sandwich",
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"orange",
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"broccoli",
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"carrot",
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"hot dog",
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"pizza",
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"donut",
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"cake",
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"chair",
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"couch",
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"potted plant",
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"bed",
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"dining table",
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"toilet",
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"tv",
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"laptop",
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"mouse",
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"remote",
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"keyboard",
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"cell phone",
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"microwave",
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"oven",
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"toaster",
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"sink",
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"refrigerator",
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"book",
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"clock",
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"vase",
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"scissors",
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"teddy bear",
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"hair drier",
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"toothbrush",
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]

‎data/hyp.scratch.custom.yaml‎

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lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
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lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
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momentum: 0.937 # SGD momentum/Adam beta1
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weight_decay: 0.0005 # optimizer weight decay 5e-4
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warmup_epochs: 3.0 # warmup epochs (fractions ok)
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warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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box: 0.05 # box loss gain
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cls: 0.3 # cls loss gain
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cls_pw: 1.0 # cls BCELoss positive_weight
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obj: 0.7 # obj loss gain (scale with pixels)
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obj_pw: 1.0 # obj BCELoss positive_weight
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iou_t: 0.20 # IoU training threshold
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anchor_t: 4.0 # anchor-multiple threshold
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lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
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lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
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momentum: 0.937 # SGD momentum/Adam beta1
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weight_decay: 0.0005 # optimizer weight decay 5e-4
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warmup_epochs: 3.0 # warmup epochs (fractions ok)
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warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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box: 0.05 # box loss gain
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cls: 0.3 # cls loss gain
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cls_pw: 1.0 # cls BCELoss positive_weight
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obj: 0.7 # obj loss gain (scale with pixels)
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obj_pw: 1.0 # obj BCELoss positive_weight
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iou_t: 0.20 # IoU training threshold
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anchor_t: 4.0 # anchor-multiple threshold
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# anchors: 3 # anchors per output layer (0 to ignore)
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fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
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hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
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hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
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hsv_v: 0.4 # image HSV-Value augmentation (fraction)
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degrees: 0.0 # image rotation (+/- deg)
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translate: 0.2 # image translation (+/- fraction)
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scale: 0.5 # image scale (+/- gain)
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shear: 0.0 # image shear (+/- deg)
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perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
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flipud: 0.0 # image flip up-down (probability)
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fliplr: 0.5 # image flip left-right (probability)
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mosaic: 1.0 # image mosaic (probability)
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mixup: 0.0 # image mixup (probability)
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copy_paste: 0.0 # image copy paste (probability)
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paste_in: 0.0 # image copy paste (probability), use 0 for faster training
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loss_ota: 1 # use ComputeLossOTA, use 0 for faster training
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fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
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hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
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hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
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hsv_v: 0.4 # image HSV-Value augmentation (fraction)
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degrees: 0.0 # image rotation (+/- deg)
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translate: 0.2 # image translation (+/- fraction)
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scale: 0.5 # image scale (+/- gain)
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shear: 0.0 # image shear (+/- deg)
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perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
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flipud: 0.0 # image flip up-down (probability)
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fliplr: 0.5 # image flip left-right (probability)
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mosaic: 1.0 # image mosaic (probability)
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mixup: 0.0 # image mixup (probability)
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copy_paste: 0.0 # image copy paste (probability)
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paste_in: 0.0 # image copy paste (probability), use 0 for faster training
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loss_ota: 1 # use ComputeLossOTA, use 0 for faster training

‎data/hyp.scratch.p5.yaml‎

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lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
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lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
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momentum: 0.937 # SGD momentum/Adam beta1
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weight_decay: 0.0005 # optimizer weight decay 5e-4
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warmup_epochs: 3.0 # warmup epochs (fractions ok)
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warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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box: 0.05 # box loss gain
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cls: 0.3 # cls loss gain
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cls_pw: 1.0 # cls BCELoss positive_weight
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obj: 0.7 # obj loss gain (scale with pixels)
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obj_pw: 1.0 # obj BCELoss positive_weight
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iou_t: 0.20 # IoU training threshold
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anchor_t: 4.0 # anchor-multiple threshold
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lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
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lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
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momentum: 0.937 # SGD momentum/Adam beta1
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weight_decay: 0.0005 # optimizer weight decay 5e-4
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warmup_epochs: 3.0 # warmup epochs (fractions ok)
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warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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box: 0.05 # box loss gain
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cls: 0.3 # cls loss gain
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cls_pw: 1.0 # cls BCELoss positive_weight
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obj: 0.7 # obj loss gain (scale with pixels)
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obj_pw: 1.0 # obj BCELoss positive_weight
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iou_t: 0.20 # IoU training threshold
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anchor_t: 4.0 # anchor-multiple threshold
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# anchors: 3 # anchors per output layer (0 to ignore)
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fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
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hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
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hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
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hsv_v: 0.4 # image HSV-Value augmentation (fraction)
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degrees: 0.0 # image rotation (+/- deg)
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translate: 0.2 # image translation (+/- fraction)
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scale: 0.9 # image scale (+/- gain)
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shear: 0.0 # image shear (+/- deg)
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perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
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flipud: 0.0 # image flip up-down (probability)
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fliplr: 0.5 # image flip left-right (probability)
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mosaic: 1.0 # image mosaic (probability)
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mixup: 0.15 # image mixup (probability)
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copy_paste: 0.0 # image copy paste (probability)
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paste_in: 0.15 # image copy paste (probability), use 0 for faster training
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loss_ota: 1 # use ComputeLossOTA, use 0 for faster training
16+
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
17+
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
18+
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
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hsv_v: 0.4 # image HSV-Value augmentation (fraction)
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degrees: 0.0 # image rotation (+/- deg)
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translate: 0.2 # image translation (+/- fraction)
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scale: 0.9 # image scale (+/- gain)
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shear: 0.0 # image shear (+/- deg)
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perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
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flipud: 0.0 # image flip up-down (probability)
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fliplr: 0.5 # image flip left-right (probability)
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mosaic: 1.0 # image mosaic (probability)
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mixup: 0.15 # image mixup (probability)
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copy_paste: 0.0 # image copy paste (probability)
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paste_in: 0.15 # image copy paste (probability), use 0 for faster training
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loss_ota: 1 # use ComputeLossOTA, use 0 for faster training

‎data/hyp.scratch.p6.yaml‎

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lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
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lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)
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momentum: 0.937 # SGD momentum/Adam beta1
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weight_decay: 0.0005 # optimizer weight decay 5e-4
5-
warmup_epochs: 3.0 # warmup epochs (fractions ok)
6-
warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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box: 0.05 # box loss gain
9-
cls: 0.3 # cls loss gain
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cls_pw: 1.0 # cls BCELoss positive_weight
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obj: 0.7 # obj loss gain (scale with pixels)
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obj_pw: 1.0 # obj BCELoss positive_weight
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iou_t: 0.20 # IoU training threshold
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anchor_t: 4.0 # anchor-multiple threshold
1+
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
2+
lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)
3+
momentum: 0.937 # SGD momentum/Adam beta1
4+
weight_decay: 0.0005 # optimizer weight decay 5e-4
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warmup_epochs: 3.0 # warmup epochs (fractions ok)
6+
warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
8+
box: 0.05 # box loss gain
9+
cls: 0.3 # cls loss gain
10+
cls_pw: 1.0 # cls BCELoss positive_weight
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obj: 0.7 # obj loss gain (scale with pixels)
12+
obj_pw: 1.0 # obj BCELoss positive_weight
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iou_t: 0.20 # IoU training threshold
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anchor_t: 4.0 # anchor-multiple threshold
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# anchors: 3 # anchors per output layer (0 to ignore)
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fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
17-
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
18-
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
19-
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
20-
degrees: 0.0 # image rotation (+/- deg)
21-
translate: 0.2 # image translation (+/- fraction)
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scale: 0.9 # image scale (+/- gain)
23-
shear: 0.0 # image shear (+/- deg)
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perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
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flipud: 0.0 # image flip up-down (probability)
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fliplr: 0.5 # image flip left-right (probability)
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mosaic: 1.0 # image mosaic (probability)
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mixup: 0.15 # image mixup (probability)
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copy_paste: 0.0 # image copy paste (probability)
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paste_in: 0.15 # image copy paste (probability), use 0 for faster training
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loss_ota: 1 # use ComputeLossOTA, use 0 for faster training
16+
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
17+
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
18+
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
19+
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
20+
degrees: 0.0 # image rotation (+/- deg)
21+
translate: 0.2 # image translation (+/- fraction)
22+
scale: 0.9 # image scale (+/- gain)
23+
shear: 0.0 # image shear (+/- deg)
24+
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
25+
flipud: 0.0 # image flip up-down (probability)
26+
fliplr: 0.5 # image flip left-right (probability)
27+
mosaic: 1.0 # image mosaic (probability)
28+
mixup: 0.15 # image mixup (probability)
29+
copy_paste: 0.0 # image copy paste (probability)
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paste_in: 0.15 # image copy paste (probability), use 0 for faster training
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loss_ota: 1 # use ComputeLossOTA, use 0 for faster training

‎data/hyp.scratch.tiny.yaml‎

Lines changed: 29 additions & 29 deletions
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lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
2-
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
3-
momentum: 0.937 # SGD momentum/Adam beta1
4-
weight_decay: 0.0005 # optimizer weight decay 5e-4
5-
warmup_epochs: 3.0 # warmup epochs (fractions ok)
6-
warmup_momentum: 0.8 # warmup initial momentum
7-
warmup_bias_lr: 0.1 # warmup initial bias lr
8-
box: 0.05 # box loss gain
9-
cls: 0.5 # cls loss gain
10-
cls_pw: 1.0 # cls BCELoss positive_weight
11-
obj: 1.0 # obj loss gain (scale with pixels)
12-
obj_pw: 1.0 # obj BCELoss positive_weight
13-
iou_t: 0.20 # IoU training threshold
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anchor_t: 4.0 # anchor-multiple threshold
1+
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
2+
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
3+
momentum: 0.937 # SGD momentum/Adam beta1
4+
weight_decay: 0.0005 # optimizer weight decay 5e-4
5+
warmup_epochs: 3.0 # warmup epochs (fractions ok)
6+
warmup_momentum: 0.8 # warmup initial momentum
7+
warmup_bias_lr: 0.1 # warmup initial bias lr
8+
box: 0.05 # box loss gain
9+
cls: 0.5 # cls loss gain
10+
cls_pw: 1.0 # cls BCELoss positive_weight
11+
obj: 1.0 # obj loss gain (scale with pixels)
12+
obj_pw: 1.0 # obj BCELoss positive_weight
13+
iou_t: 0.20 # IoU training threshold
14+
anchor_t: 4.0 # anchor-multiple threshold
1515
# anchors: 3 # anchors per output layer (0 to ignore)
16-
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
17-
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
18-
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
19-
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
20-
degrees: 0.0 # image rotation (+/- deg)
21-
translate: 0.1 # image translation (+/- fraction)
22-
scale: 0.5 # image scale (+/- gain)
23-
shear: 0.0 # image shear (+/- deg)
24-
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
25-
flipud: 0.0 # image flip up-down (probability)
26-
fliplr: 0.5 # image flip left-right (probability)
27-
mosaic: 1.0 # image mosaic (probability)
28-
mixup: 0.05 # image mixup (probability)
29-
copy_paste: 0.0 # image copy paste (probability)
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paste_in: 0.05 # image copy paste (probability), use 0 for faster training
16+
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
17+
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
18+
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
19+
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
20+
degrees: 0.0 # image rotation (+/- deg)
21+
translate: 0.1 # image translation (+/- fraction)
22+
scale: 0.5 # image scale (+/- gain)
23+
shear: 0.0 # image shear (+/- deg)
24+
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
25+
flipud: 0.0 # image flip up-down (probability)
26+
fliplr: 0.5 # image flip left-right (probability)
27+
mosaic: 1.0 # image mosaic (probability)
28+
mixup: 0.05 # image mixup (probability)
29+
copy_paste: 0.0 # image copy paste (probability)
30+
paste_in: 0.05 # image copy paste (probability), use 0 for faster training
3131
loss_ota: 1 # use ComputeLossOTA, use 0 for faster training

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