update coco-tuned hyp['cls'] to current dataset
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train.py
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train.py
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@ -71,6 +71,7 @@ def train():
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train_path = data_dict['train']
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train_path = data_dict['train']
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test_path = data_dict['valid']
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test_path = data_dict['valid']
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nc = 1 if opt.single_cls else int(data_dict['classes']) # number of classes
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nc = 1 if opt.single_cls else int(data_dict['classes']) # number of classes
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hyp['cls'] *= nc / 80 # update coco-tuned hyp['cls'] to current dataset
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# Remove previous results
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# Remove previous results
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for f in glob.glob('*_batch*.png') + glob.glob(results_file):
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for f in glob.glob('*_batch*.png') + glob.glob(results_file):
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