Introduction

The goal of this project is to implement Light Head R-CNN using Detectron platform.

Already implemented

• Light Head R-CNN & RPN & unshared weights of convolution layer in neural network head
• Light Head R-CNN & FPN(Feature Map Pyramid) & shared weights of convolution layer in neural network head

Platform 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
• Faster R-CNN
• RPN
• Fast R-CNN
• R-FCN

using the following backbone network architectures:

• ResNeXt{50,101,152}
• ResNet{50,101,152}
• Feature Pyramid Networks (with ResNet/ResNeXt)

Additional backbone architectures may be easily implemented.

License

This project is released under theMIT License and Apache 2.0 license.

Citing Detectron

If you use Detectron in your research or wish to refer to the baseline results published in the Model Zoo, 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.

Installation

Please find installation instructions for Caffe2 and Detectron in INSTALL.md.

Quick Start: Using Detectron

After installation, please see GETTING_STARTED.md for brief tutorials covering inference and training with Detectron.

Getting Help

To start, please check the troubleshooting section of our installation instructions as well as our FAQ. 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 for more information about contributing to Detectron.

References

• Data Distillation: Towards Omni-Supervised Learning. Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, and Kaiming He. Tech report, arXiv, Dec. 2017.
• Learning to Segment Every Thing. Ronghang Hu, Piotr Dollár, Kaiming He, Trevor Darrell, and Ross Girshick. Tech report, arXiv, Nov. 2017.
• Non-Local Neural Networks. Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He. Tech report, arXiv, Nov. 2017.
• Mask R-CNN. Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. IEEE International Conference on Computer Vision (ICCV), 2017.
• Focal Loss for Dense Object Detection. 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. 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. Georgia Gkioxari, Ross Girshick, Piotr Dollár, and Kaiming He. Tech report, arXiv, Apr. 2017.
• Feature Pyramid Networks for Object Detection. 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. 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. Jifeng Dai, Yi Li, Kaiming He, and Jian Sun. Conference on Neural Information Processing Systems (NIPS), 2016.
• Deep Residual Learning for Image Recognition. 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 Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Conference on Neural Information Processing Systems (NIPS), 2015.
• Fast R-CNN. Ross Girshick. IEEE International Conference on Computer Vision (ICCV), 2015.