updates
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723431d6c3
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5
train.py
5
train.py
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@ -20,7 +20,7 @@ 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 (results68: 59.2 mAP@0.5 yolov3-spp-416) https://github.com/ultralytics/yolov3/issues/310
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# Hyperparameters (results68: 59.9 mAP@0.5 yolov3-spp-416) https://github.com/ultralytics/yolov3/issues/310
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hyp = {'giou': 3.54, # giou loss gain
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hyp = {'giou': 3.54, # giou loss gain
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'cls': 37.4, # cls loss gain
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'cls': 37.4, # cls loss gain
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@ -28,7 +28,7 @@ hyp = {'giou': 3.54, # giou loss gain
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'obj': 49.5, # obj loss gain (*=img_size/320 if img_size != 320)
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'obj': 49.5, # obj loss gain (*=img_size/320 if img_size != 320)
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'obj_pw': 1.0, # obj BCELoss positive_weight
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'obj_pw': 1.0, # obj BCELoss positive_weight
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'iou_t': 0.225, # iou training threshold
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'iou_t': 0.225, # iou training threshold
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'lr0': 0.00579, # initial learning rate (SGD=1E-3, Adam=9E-5)
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'lr0': 0.00579, # initial learning rate (SGD=5E-3, Adam=5E-4)
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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': 0.937, # SGD momentum
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'momentum': 0.937, # SGD momentum
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'weight_decay': 0.000484, # optimizer weight decay
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'weight_decay': 0.000484, # optimizer weight decay
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@ -94,6 +94,7 @@ def train():
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pg0 += [v] # all else
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pg0 += [v] # all else
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if opt.adam:
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if opt.adam:
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hyp['lr0'] *= 0.1 # reduce lr (i.e. SGD=5E-3, Adam=5E-4)
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optimizer = optim.Adam(pg0, lr=hyp['lr0'])
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optimizer = optim.Adam(pg0, lr=hyp['lr0'])
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# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
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# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
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else:
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else:
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