updates
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@ -320,7 +320,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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extract_bounding_boxes = False
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create_datasubset = False
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pbar = tqdm(self.label_files, desc='Caching labels')
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nm, nf, ne, ns = 0, 0, 0, 0 # number missing, number found, number empty, number datasubset
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nm, nf, ne, ns, nd = 0, 0, 0, 0, 0 # number missing, found, empty, datasubset, duplicate
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for i, file in enumerate(pbar):
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try:
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with open(file, 'r') as f:
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@ -333,6 +333,9 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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assert l.shape[1] == 5, '> 5 label columns: %s' % file
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assert (l >= 0).all(), 'negative labels: %s' % file
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assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file
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if np.unique(l, axis=0).shape[0] < l.shape[0]: # duplicate rows
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nd += 1 # print('WARNING: duplicate rows in %s' % self.label_files[i]) # duplicate rows
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self.labels[i] = l
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nf += 1 # file found
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@ -370,7 +373,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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ne += 1 # print('empty labels for image %s' % self.img_files[i]) # file empty
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# os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i])) # remove
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pbar.desc = 'Caching labels (%g found, %g missing, %g empty for %g images)' % (nf, nm, ne, n)
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pbar.desc = 'Caching labels (%g found, %g missing, %g empty, %g duplicate, for %g images)' % (
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nf, nm, ne, nd, n)
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assert nf > 0, 'No labels found. Recommend correcting image and label paths.'
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# Cache images into memory for faster training (WARNING: Large datasets may exceed system RAM)
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