updates
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5
train.py
5
train.py
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@ -263,6 +263,10 @@ if __name__ == '__main__':
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opt = parser.parse_args()
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print(opt, end='\n\n')
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if opt.evolve:
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opt.notest = True # save time by only testing final epoch
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opt.nosave = True # do not save checkpoints
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# Train
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results = train(
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opt.cfg,
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@ -279,7 +283,6 @@ if __name__ == '__main__':
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# Evolve hyperparameters (optional)
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if opt.evolve:
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opt.notest = True # save time by only testing final epoch
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best_fitness = results[2] # use mAP for fitness
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gen = 30 # generations to evolve
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