updates
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train.py
4
train.py
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@ -244,7 +244,9 @@ def train(
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# Multi-Scale training (320 - 608 pixels) every 10 batches
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if multi_scale and (i + 1) % 10 == 0:
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dataset.img_size = random.choice(range(10, 20)) * 32
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min_size = round(img_size / 32 / 1.5)
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max_size = round(img_size / 32 * 1.5)
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dataset.img_size = random.choice(range(min_size, max_size + 1)) * 32
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print('multi_scale img_size = %g' % dataset.img_size)
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# Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
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