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@ -17,7 +17,7 @@ This directory contains python software and an iOS App developed by Ultralytics
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# Description
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# Description
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The https://github.com/ultralytics/yolov3 repo contains inference and training code for YOLOv3 in PyTorch. The code works on Linux, MacOS and Windows. Training is done on the COCO dataset by default: https://cocodataset.org/#home. **Credit to Joseph Redmon for YOLO: ** https://pjreddie.com/darknet/yolo/.
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The https://github.com/ultralytics/yolov3 repo contains inference and training code for YOLOv3 in PyTorch. The code works on Linux, MacOS and Windows. Training is done on the COCO dataset by default: https://cocodataset.org/#home. **Credit to Joseph Redmon for YOLO:** https://pjreddie.com/darknet/yolo/.
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# Requirements
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# Requirements
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@ -100,7 +100,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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assert len(self.img_files) > 0, 'No images found in %s' % path
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assert len(self.img_files) > 0, 'No images found in %s' % path
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self.img_size = img_size
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self.img_size = img_size
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self.augment = augment
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self.augment = augment
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self.label_files = [x.replace('images', 'labels').replace('.bmp', '.txt').replace('.jpg', '.txt')
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self.label_files = [x.replace('images', 'labels').replace('.bmp', '.txt').replace('.jpg', '.txt').replace('.png', '.txt')
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for x in self.img_files]
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for x in self.img_files]
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def __len__(self):
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def __len__(self):
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