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Detectron2 is FAIR's next-generation research platform for object detection and segmentation.
Python Cuda C++ Other
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RaymondKirk and facebook-github-bot LastLevelP6P7 should allow custom in_feature for custom backbones (#905)
LastLevelP6P7 had a hardcoded "res5" in feature key, PR adds parameter to overload this so RetinaNet FPN can be used with other backbones.
Pull Request resolved: #905

Differential Revision: D19990142

Pulled By: ppwwyyxx

fbshipit-source-id: 5dff259e13529b311036fccc7768dfa2b9f44793
Latest commit 664b58e Feb 20, 2020

Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark.

What's New

  • It is powered by the PyTorch deep learning framework.
  • Includes more features such as panoptic segmentation, densepose, Cascade R-CNN, rotated bounding boxes, etc.
  • Can be used as a library to support different projects on top of it. We'll open source more research projects in this way.
  • It trains much faster.

See our blog post to see more demos and learn about detectron2.



Quick Start

See, or the Colab Notebook.

Learn more at our documentation. And see projects/ for some projects that are built on top of detectron2.

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the Detectron2 Model Zoo.


Detectron2 is released under the Apache 2.0 license.

Citing Detectron

If you use Detectron2 in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.

  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and
                  Wan-Yen Lo and Ross Girshick},
  title =        {Detectron2},
  howpublished = {\url{}},
  year =         {2019}
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