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@ -79,6 +79,7 @@ def train(hyp):
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# Create model
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model = Model(opt.cfg).to(device)
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assert model.md['nc'] == nc, '%s nc=%g classes but %s nc=%g classes' % (opt.data, nc, opt.cfg, model.md['nc'])
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model.names = data_dict['names']
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# Image sizes
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gs = int(max(model.stride)) # grid size (max stride)
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@ -193,7 +194,6 @@ def train(hyp):
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model.hyp = hyp # attach hyperparameters to model
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model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou)
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model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
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model.names = data_dict['names']
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# Class frequency
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labels = np.concatenate(dataset.labels, 0)
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