updates
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train.py
34
train.py
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@ -20,25 +20,25 @@ last = wdir + 'last.pt'
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best = wdir + 'best.pt'
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best = wdir + 'best.pt'
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results_file = 'results.txt'
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results_file = 'results.txt'
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# Hyperparameters (j-series, 50.5 mAP yolov3-320) evolved by @ktian08 https://github.com/ultralytics/yolov3/issues/310
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# Hyperparameters (k-series, 53.3 mAP yolov3-spp-320) https://github.com/ultralytics/yolov3/issues/310
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hyp = {'giou': 1.582, # giou loss gain
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hyp = {'giou': 3.31, # giou loss gain
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'cls': 27.76, # cls loss gain (CE=~1.0, uCE=~20)
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'cls': 42.4, # cls loss gain (CE=~1.0, uCE=~20)
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'cls_pw': 1.446, # cls BCELoss positive_weight
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'cls_pw': 1.0, # cls BCELoss positive_weight
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'obj': 21.35, # obj loss gain (*=80 for uBCE with 80 classes)
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'obj': 50.0, # obj loss gain (*=80 for uBCE with 80 classes)
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'obj_pw': 3.941, # obj BCELoss positive_weight
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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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'iou_t': 0.213, # iou training threshold
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'lr0': 0.002324, # initial learning rate (SGD=1E-3, Adam=9E-5)
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'lr0': 0.00261, # initial learning rate (SGD=1E-3, Adam=9E-5)
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'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
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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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'momentum': 0.949, # SGD momentum
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'weight_decay': 0.0004569, # optimizer weight decay
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'weight_decay': 0.000489, # optimizer weight decay
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'fl_gamma': 0.5, # focal loss gamma
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'fl_gamma': 0.5, # focal loss gamma
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'hsv_h': 0.01, # image HSV-Hue augmentation (fraction)
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'hsv_h': 0.0103, # image HSV-Hue augmentation (fraction)
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'hsv_s': 0.5703, # image HSV-Saturation augmentation (fraction)
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'hsv_s': 0.691, # image HSV-Saturation augmentation (fraction)
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'hsv_v': 0.3174, # image HSV-Value augmentation (fraction)
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'hsv_v': 0.433, # image HSV-Value augmentation (fraction)
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'degrees': 1.113, # image rotation (+/- deg)
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'degrees': 1.43, # image rotation (+/- deg)
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'translate': 0.06797, # image translation (+/- fraction)
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'translate': 0.0663, # image translation (+/- fraction)
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'scale': 0.1059, # image scale (+/- gain)
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'scale': 0.11, # image scale (+/- gain)
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'shear': 0.5768} # image shear (+/- deg)
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'shear': 0.384} # image shear (+/- deg)
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# Overwrite hyp with hyp*.txt (optional)
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# Overwrite hyp with hyp*.txt (optional)
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f = glob.glob('hyp*.txt')
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f = glob.glob('hyp*.txt')
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