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<table style="width:100%"> <a href="https://apps.apple.com/app/id1452689527" target="_blank">
<tr> <img src="https://user-images.githubusercontent.com/26833433/85940594-2d3f7d80-b8d2-11ea-809a-87b3bf6d968b.jpg" width="1000"></a>
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<td align="center">
<a href="https://www.ultralytics.com" target="_blank">
<img src="https://storage.googleapis.com/ultralytics/logo/logoname1000.png" width="160"></a>
<img src="https://user-images.githubusercontent.com/26833433/61591093-2b4d4480-abc2-11e9-8b46-d88eb1dabba1.jpg">
<a href="https://itunes.apple.com/app/id1452689527" target="_blank">
<img src="https://user-images.githubusercontent.com/26833433/50044365-9b22ac00-0082-11e9-862f-e77aee7aa7b0.png" width="180"></a>
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<img src="https://user-images.githubusercontent.com/26833433/61591100-55066b80-abc2-11e9-9647-52c0e045b288.jpg">
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This repo contains Ultralytics inference and training code for YOLOv3 in PyTorch. The code works on Linux, MacOS and Windows. Credit to Joseph Redmon for YOLO https://pjreddie.com/darknet/yolo/.
## Introduction
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/.
## Requirements ## Requirements