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src=\".github/Detectron2-Logo-Horz.svg\" width=\"300\" \u003e\n\nDetectron2 is Facebook AI Research's next generation library\nthat provides state-of-the-art detection and segmentation algorithms.\nIt is the successor of\n[Detectron](https://github.com/facebookresearch/Detectron/)\nand [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).\nIt supports a number of computer vision research projects and production applications in Facebook.\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png\"/\u003e\n\u003c/div\u003e\n\u003cbr\u003e\n\n## Learn More about Detectron2\n\n* Includes new capabilities such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend,\n  DeepLab, ViTDet, MViTv2 etc.\n* Used as a library to support building [research projects](projects/) on top of it.\n* Models can be exported to TorchScript format or Caffe2 format for deployment.\n* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html).\n\nSee our [blog post](https://ai.meta.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)\nto see more demos.\nSee this [interview](https://ai.meta.com/blog/detectron-everingham-prize/) to learn more about the stories behind detectron2.\n\n## Installation\n\nSee [installation instructions](https://detectron2.readthedocs.io/tutorials/install.html).\n\n## Getting Started\n\nSee [Getting Started with Detectron2](https://detectron2.readthedocs.io/tutorials/getting_started.html),\nand the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)\nto learn about basic usage.\n\nLearn more at our [documentation](https://detectron2.readthedocs.org).\nAnd see [projects/](projects/) for some projects that are built on top of detectron2.\n\n## Model Zoo and Baselines\n\nWe provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md).\n\n## License\n\nDetectron2 is released under the [Apache 2.0 license](LICENSE).\n\n## Citing Detectron2\n\nIf 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.\n\n```BibTeX\n@misc{wu2019detectron2,\n  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and\n                  Wan-Yen Lo and Ross Girshick},\n  title =        {Detectron2},\n  howpublished = {\\url{https://github.com/facebookresearch/detectron2}},\n  year =         {2019}\n}\n```\n","funding_links":[],"categories":["Python","Frameworks \u0026 libraries","Computer Vision","🛠️ Tools \u0026 Libraries","🔍 Instance Segmentation","🛠️ Tools \u0026 Frameworks","Sensor Processing","Pytorch \u0026 related libraries｜Pytorch \u0026 相关库","Other","Pytorch \u0026 related libraries","对象检测、分割","计算机视觉 (CV)","Frameworks for segmentation","PyTorch 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