| <img src=".github/Detectron2-Logo-Horz.svg" width="300" > | |
| <a href="https://opensource.facebook.com/support-ukraine"> | |
| <img src="https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB" alt="Support Ukraine - Help Provide Humanitarian Aid to Ukraine." /> | |
| </a> | |
| Detectron2 is Facebook AI Research's next generation library | |
| that provides state-of-the-art detection and segmentation algorithms. | |
| It is the successor of | |
| [Detectron](https://github.com/facebookresearch/Detectron/) | |
| and [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/). | |
| It supports a number of computer vision research projects and production applications in Facebook. | |
| <div align="center"> | |
| <img src="https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png"/> | |
| </div> | |
| <br> | |
| ## Learn More about Detectron2 | |
| Explain Like I’m 5: Detectron2 | Using Machine Learning with Detectron2 | |
| :-------------------------:|:-------------------------: | |
| [](https://www.youtube.com/watch?v=1oq1Ye7dFqc) | [](https://www.youtube.com/watch?v=eUSgtfK4ivk) | |
| ## What's New | |
| * Includes new capabilities such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, | |
| DeepLab, ViTDet, MViTv2 etc. | |
| * Used as a library to support building [research projects](projects/) on top of it. | |
| * Models can be exported to TorchScript format or Caffe2 format for deployment. | |
| * It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html). | |
| 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 [installation instructions](https://detectron2.readthedocs.io/tutorials/install.html). | |
| ## Getting Started | |
| 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} | |
| } | |
| ``` | |