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https://github.com/facebookresearch/Detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
https://github.com/facebookresearch/Detectron

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FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

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README 

        
**Detectron is deprecated. Please see [detectron2](https://github.com/facebookresearch/detectron2), a ground-up rewrite of Detectron in PyTorch.**



# Detectron



Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including [Mask R-CNN](https://arxiv.org/abs/1703.06870). It is written in Python and powered by the [Caffe2](https://github.com/caffe2/caffe2) deep learning framework.



At FAIR, Detectron has enabled numerous research projects, including: [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144), [Mask R-CNN](https://arxiv.org/abs/1703.06870), [Detecting and Recognizing Human-Object Interactions](https://arxiv.org/abs/1704.07333), [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002), [Non-local Neural Networks](https://arxiv.org/abs/1711.07971), [Learning to Segment Every Thing](https://arxiv.org/abs/1711.10370), [Data Distillation: Towards Omni-Supervised Learning](https://arxiv.org/abs/1712.04440), [DensePose: Dense Human Pose Estimation In The Wild](https://arxiv.org/abs/1802.00434), and [Group Normalization](https://arxiv.org/abs/1803.08494).





  

  
Example Mask R-CNN output.





## Introduction



The goal of Detectron is to provide a high-quality, high-performance

codebase for object detection *research*. It is designed to be flexible in order

to support rapid implementation and evaluation of novel research. Detectron

includes implementations of the following object detection algorithms:



- [Mask R-CNN](https://arxiv.org/abs/1703.06870) -- *Marr Prize at ICCV 2017*

- [RetinaNet](https://arxiv.org/abs/1708.02002) -- *Best Student Paper Award at ICCV 2017*

- [Faster R-CNN](https://arxiv.org/abs/1506.01497)

- [RPN](https://arxiv.org/abs/1506.01497)

- [Fast R-CNN](https://arxiv.org/abs/1504.08083)

- [R-FCN](https://arxiv.org/abs/1605.06409)



using the following backbone network architectures:



- [ResNeXt{50,101,152}](https://arxiv.org/abs/1611.05431)

- [ResNet{50,101,152}](https://arxiv.org/abs/1512.03385)

- [Feature Pyramid Networks](https://arxiv.org/abs/1612.03144) (with ResNet/ResNeXt)

- [VGG16](https://arxiv.org/abs/1409.1556)



Additional backbone architectures may be easily implemented. For more details about these models, please see [References](#references) below.



## Update



- 4/2018: Support Group Normalization - see [`GN/README.md`](./projects/GN/README.md)



## License



Detectron is released under the [Apache 2.0 license](https://github.com/facebookresearch/detectron/blob/master/LICENSE). See the [NOTICE](https://github.com/facebookresearch/detectron/blob/master/NOTICE) file for additional details.



## Citing Detectron



If you use Detectron 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.



```

@misc{Detectron2018,

  author =       {Ross Girshick and Ilija Radosavovic and Georgia Gkioxari and

                  Piotr Doll\'{a}r and Kaiming He},

  title =        {Detectron},

  howpublished = {\url{https://github.com/facebookresearch/detectron}},

  year =         {2018}

}

```



## Model Zoo and Baselines



We provide a large set of baseline results and trained models available for download in the [Detectron Model Zoo](MODEL_ZOO.md).



## Installation



Please find installation instructions for Caffe2 and Detectron in [`INSTALL.md`](INSTALL.md).



## Quick Start: Using Detectron



After installation, please see [`GETTING_STARTED.md`](GETTING_STARTED.md) for brief tutorials covering inference and training with Detectron.



## Getting Help



To start, please check the [troubleshooting](INSTALL.md#troubleshooting) section of our installation instructions as well as our [FAQ](FAQ.md). If you couldn't find help there, try searching our GitHub issues. We intend the issues page to be a forum in which the community collectively troubleshoots problems.



If bugs are found, **we appreciate pull requests** (including adding Q&A's to `FAQ.md` and improving our installation instructions and troubleshooting documents). Please see [CONTRIBUTING.md](CONTRIBUTING.md) for more information about contributing to Detectron.



## References



- [Data Distillation: Towards Omni-Supervised Learning](https://arxiv.org/abs/1712.04440).

  Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, and Kaiming He.

  Tech report, arXiv, Dec. 2017.

- [Learning to Segment Every Thing](https://arxiv.org/abs/1711.10370).

  Ronghang Hu, Piotr Dollár, Kaiming He, Trevor Darrell, and Ross Girshick.

  Tech report, arXiv, Nov. 2017.

- [Non-Local Neural Networks](https://arxiv.org/abs/1711.07971).

  Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He.

  Tech report, arXiv, Nov. 2017.

- [Mask R-CNN](https://arxiv.org/abs/1703.06870).

  Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick.

  IEEE International Conference on Computer Vision (ICCV), 2017.

- [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002).

  Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár.

  IEEE International Conference on Computer Vision (ICCV), 2017.

- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/abs/1706.02677).

  Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He.

  Tech report, arXiv, June 2017.

- [Detecting and Recognizing Human-Object Interactions](https://arxiv.org/abs/1704.07333).

  Georgia Gkioxari, Ross Girshick, Piotr Dollár, and Kaiming He.

  Tech report, arXiv, Apr. 2017.

- [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144).

  Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie.

  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

- [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431).

  Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He.

  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](http://arxiv.org/abs/1605.06409).

  Jifeng Dai, Yi Li, Kaiming He, and Jian Sun.

  Conference on Neural Information Processing Systems (NIPS), 2016.

- [Deep Residual Learning for Image Recognition](http://arxiv.org/abs/1512.03385).

  Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.

  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](http://arxiv.org/abs/1506.01497)

  Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun.

  Conference on Neural Information Processing Systems (NIPS), 2015.

- [Fast R-CNN](http://arxiv.org/abs/1504.08083).

  Ross Girshick.

  IEEE International Conference on Computer Vision (ICCV), 2015.