https://github.com/mapiv/detectron2
https://github.com/mapiv/detectron2
Last synced: 7 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/mapiv/detectron2
- Owner: MapIV
- License: apache-2.0
- Created: 2025-03-21T08:42:22.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-27T03:41:55.000Z (about 1 year ago)
- Last Synced: 2025-06-26T02:07:33.850Z (10 months ago)
- Language: Python
- Size: 1.04 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
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README
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.
## 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}
}
```