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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
Last synced: 2 months ago
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FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
- Host: GitHub
- URL: https://github.com/facebookresearch/Detectron
- Owner: facebookresearch
- License: apache-2.0
- Archived: true
- Created: 2017-10-05T17:32:00.000Z (over 7 years ago)
- Default Branch: main
- Last Pushed: 2023-11-20T09:13:34.000Z (about 1 year ago)
- Last Synced: 2024-05-23T04:37:00.727Z (8 months ago)
- Language: Python
- Homepage:
- Size: 4.32 MB
- Stars: 26,154
- Watchers: 942
- Forks: 5,451
- Open Issues: 332
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-deep-reinforcement-learning - facebookresearch/Detectron
- StarryDivineSky - facebookresearch/Detectron - CNN和RetinaNet等流行算法。 (对象检测、分割 / 网络服务_其他)
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.