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<img src="https://storage.googleapis.com/ultralytics/logo/logoname1000.png" width="200">
<table style="width:100%">
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<th><img src="https://user-images.githubusercontent.com/26833433/52743528-e6096300-2fe2-11e9-970c-5fee45769fab.jpg" width="400"></th>
<th><img src="https://storage.googleapis.com/ultralytics/logo/logoname1000.png" width="200">
<br><br/>
<p> <a href="https://itunes.apple.com/app/id1452689527">
<img href="https://itunes.apple.com/app/id1452689527" src="https://user-images.githubusercontent.com/26833433/50044365-9b22ac00-0082-11e9-862f-e77aee7aa7b0.png" width="180">
</a> </p>
<img src="https://user-images.githubusercontent.com/26833433/52743528-e6096300-2fe2-11e9-970c-5fee45769fab.jpg" width="400">
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# Introduction
This directory contains software developed by Ultralytics LLC, and **is freely available for redistribution under the GPL-3.0 license**. For more information on Ultralytics projects please visit:
This directory contains python software and an iOS App developed by Ultralytics LLC, and **is freely available for redistribution under the GPL-3.0 license**. For more information on Ultralytics projects please visit:
https://www.ultralytics.com.
# Description
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/) and to **Erik Lindernoren for the PyTorch implementation** this work is based on (https://github.com/eriklindernoren/PyTorch-YOLOv3).
# Requirements
Python 3.7 or later with the following `pip3 install -U -r requirements.txt` packages: