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](https://github.com/facebookresearch/Detectron/), and it originates from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).
### What's New * It is powered by the [PyTorch](https://pytorch.org) deep learning framework. * Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. * Can be used as a library to support [different projects](projects/) on top of it. We'll open source more research projects in this way. * It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html). * Models can be exported to TorchScript format or Caffe2 format for deployment. See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/) to see more demos and learn about detectron2. ## Installation See [INSTALL.md](INSTALL.md). ## Getting Started Follow the [installation instructions](https://detectron2.readthedocs.io/tutorials/install.html) to install detectron2. See [Getting Started with Detectron2](https://detectron2.readthedocs.io/tutorials/getting_started.html), and the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5) to learn about basic usage. Learn more at our [documentation](https://detectron2.readthedocs.org). And see [projects/](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](MODEL_ZOO.md). ## License Detectron2 is released under the [Apache 2.0 license](LICENSE). ## Citing Detectron2 If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry. ```BibTeX @misc{wu2019detectron2, author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and Wan-Yen Lo and Ross Girshick}, title = {Detectron2}, howpublished = {\url{https://github.com/facebookresearch/detectron2}}, year = {2019} } ```