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train.py
4
train.py
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@ -128,7 +128,7 @@ def train(
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# plt.savefig('LR.png', dpi=300)
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# Dataset
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rectangular_training = False
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rectangular_training = True
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dataset = LoadImagesAndLabels(train_path,
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img_size,
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batch_size,
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@ -196,7 +196,7 @@ def train(
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imgs = imgs.to(device)
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targets = targets.to(device)
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# Multi-Scale training
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# Multi-Scale training TODO: short-side to 32-multiple https://github.com/ultralytics/yolov3/issues/358
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if multi_scale:
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if (i + nb * epoch) / accumulate % 10 == 0: # adjust (67% - 150%) every 10 batches
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img_size = random.choice(range(img_size_min, img_size_max + 1)) * 32
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