updates
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4
train.py
4
train.py
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@ -37,7 +37,7 @@ hyp = {'giou': 1.153, # giou loss gain
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'lr0': 0.001357, # initial learning rate
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'lr0': 0.001357, # initial learning rate
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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': 0.916, # SGD momentum
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'momentum': 0.916, # SGD momentum
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'weight_decay': 0.000572, # optimizer weight decay
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'weight_decay': 0.0000572, # optimizer weight decay
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'hsv_s': 0.5, # image HSV-Saturation augmentation (fraction)
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'hsv_s': 0.5, # image HSV-Saturation augmentation (fraction)
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'hsv_v': 0.5, # image HSV-Value augmentation (fraction)
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'hsv_v': 0.5, # image HSV-Value augmentation (fraction)
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'degrees': 5, # image rotation (+/- deg)
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'degrees': 5, # image rotation (+/- deg)
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@ -328,7 +328,7 @@ def print_mutation(hyp, results):
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with open('evolve.txt', 'a') as f: # append result
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with open('evolve.txt', 'a') as f: # append result
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f.write(c + b + '\n')
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f.write(c + b + '\n')
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x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows
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x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows
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np.savetxt('evolve2.txt', x[np.argsort(-fitness(x))] , '%11.3g') # save sort by fitness
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np.savetxt('evolve.txt', x[np.argsort(-fitness(x))] , '%11.3g') # save sort by fitness
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os.system('gsutil cp evolve.txt gs://%s' % opt.bucket) # upload evolve.txt
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os.system('gsutil cp evolve.txt gs://%s' % opt.bucket) # upload evolve.txt
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else:
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else:
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with open('evolve.txt', 'a') as f:
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with open('evolve.txt', 'a') as f:
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