updates
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@ -179,7 +179,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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# self.imgs = [cv2.imread(img_files[i]) for i in range(n)]
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# self.imgs = [cv2.imread(img_files[i]) for i in range(n)]
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# Preload labels (required for weighted CE training)
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# Preload labels (required for weighted CE training)
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self.labels = [np.array([])] * n
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self.labels = [np.zeros((0, 5))] * n
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iter = tqdm(self.label_files, desc='Reading labels') if n > 1000 else self.label_files
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iter = tqdm(self.label_files, desc='Reading labels') if n > 1000 else self.label_files
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for i, file in enumerate(iter):
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for i, file in enumerate(iter):
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try:
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try:
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