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train.py
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train.py
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@ -199,8 +199,7 @@ def train(
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print('multi_scale img_size = %g' % dataset.img_size)
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# Calculate mAP
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opt.notest = opt.notest or (opt.nosave and epoch < 10) # skip testing first 10 epochs if opt.nosave
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if not opt.notest or epoch == epochs - 1: # always test final epoch
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if not (opt.notest or (opt.nosave and epoch < 2)) or epoch == epochs - 1: # always test final epoch
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with torch.no_grad():
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results = test.test(cfg, data_cfg, batch_size=batch_size, img_size=img_size, model=model,
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conf_thres=0.1)
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