updates
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train.py
32
train.py
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@ -53,25 +53,25 @@ hyp = {'giou': 1.582, # giou loss gain
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'scale': 0.1059, # image scale (+/- gain)
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'shear': 0.5768} # image shear (+/- deg)
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# Hyperparameters (k-series, j-series with all *_pw=1)
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# hyp = {'giou': 1.582, # giou loss gain
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# # Hyperparameters (i-series)
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# hyp = {'giou': 1.43, # giou loss gain
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# 'xy': 4.688, # xy loss gain
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# 'wh': 0.1857, # wh loss gain
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# 'cls': 40.14, # cls loss gain
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# 'cls_pw': 1.0, # cls BCELoss positive_weight
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# 'obj': 84.14, # obj loss gain
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# 'obj_pw': 1.0, # obj BCELoss positive_weight
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# 'iou_t': 0.2635, # iou training threshold
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# 'lr0': 0.002324, # initial learning rate
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# 'cls': 11.7, # cls loss gain
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# 'cls_pw': 4.81, # cls BCELoss positive_weight
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# 'obj': 11.5, # obj loss gain
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# 'obj_pw': 1.56, # obj BCELoss positive_weight
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# 'iou_t': 0.281, # iou training threshold
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# 'lr0': 0.0013, # initial learning rate
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# 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
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# 'momentum': 0.97, # SGD momentum
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# 'weight_decay': 0.0004569, # optimizer weight decay
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# 'hsv_s': 0.5703, # image HSV-Saturation augmentation (fraction)
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# 'hsv_v': 0.3174, # image HSV-Value augmentation (fraction)
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# 'degrees': 1.113, # image rotation (+/- deg)
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# 'translate': 0.06797, # image translation (+/- fraction)
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# 'scale': 0.1059, # image scale (+/- gain)
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# 'shear': 0.5768} # image shear (+/- deg)
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# 'momentum': 0.944, # SGD momentum
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# 'weight_decay': 0.000427, # optimizer weight decay
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# 'hsv_s': 0.0599, # image HSV-Saturation augmentation (fraction)
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# 'hsv_v': 0.142, # image HSV-Value augmentation (fraction)
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# 'degrees': 1.03, # image rotation (+/- deg)
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# 'translate': 0.0552, # image translation (+/- fraction)
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# 'scale': 0.0555, # image scale (+/- gain)
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# 'shear': 0.434} # image shear (+/- deg)
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def train(cfg,
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@ -18,7 +18,6 @@ sudo shutdown
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rm -rf yolov3 # Warning: remove existing
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git clone https://github.com/ultralytics/yolov3 # master
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# git clone -b test --depth 1 https://github.com/ultralytics/yolov3 test # branch
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cp -r weights yolov3 && cd yolov3
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# Train
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python3 train.py
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