69 lines
3.0 KiB
Markdown
69 lines
3.0 KiB
Markdown
<img src=".github/Detectron2-Logo-Horz.svg" width="300" >
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<a href="https://opensource.facebook.com/support-ukraine">
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<img src="https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB" alt="Support Ukraine - Help Provide Humanitarian Aid to Ukraine." />
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</a>
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Detectron2 is Facebook AI Research's next generation library
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that provides state-of-the-art detection and segmentation algorithms.
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It is the successor of
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[Detectron](https://github.com/facebookresearch/Detectron/)
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and [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).
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It supports a number of computer vision research projects and production applications in Facebook.
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<div align="center">
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<img src="https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png"/>
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</div>
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<br>
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## Learn More about Detectron2
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Explain Like I’m 5: Detectron2 | Using Machine Learning with Detectron2
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:-------------------------:|:-------------------------:
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[](https://www.youtube.com/watch?v=1oq1Ye7dFqc) | [](https://www.youtube.com/watch?v=eUSgtfK4ivk)
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## What's New
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* Includes new capabilities such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend,
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DeepLab, ViTDet, MViTv2 etc.
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* Used as a library to support building [research projects](projects/) on top of it.
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* Models can be exported to TorchScript format or Caffe2 format for deployment.
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* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html).
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See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)
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to see more demos and learn about detectron2.
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## Installation
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See [installation instructions](https://detectron2.readthedocs.io/tutorials/install.html).
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## Getting Started
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See [Getting Started with Detectron2](https://detectron2.readthedocs.io/tutorials/getting_started.html),
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and the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
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to learn about basic usage.
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Learn more at our [documentation](https://detectron2.readthedocs.org).
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And see [projects/](projects/) for some projects that are built on top of detectron2.
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## Model Zoo and Baselines
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We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md).
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## License
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Detectron2 is released under the [Apache 2.0 license](LICENSE).
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## Citing Detectron2
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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.
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```BibTeX
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@misc{wu2019detectron2,
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author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and
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Wan-Yen Lo and Ross Girshick},
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title = {Detectron2},
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howpublished = {\url{https://github.com/facebookresearch/detectron2}},
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year = {2019}
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}
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```
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