Skip to content
No description, website, or topics provided.
Branch: master
Clone or download
BIT-silence and facebook-github-bot Move GenreateProposalsOp on GPU for detectron (#857)
Pull Request resolved: facebookresearch/Detectron#857

Move GenreateProposalsOp on GPU for detectron

Reviewed By: rbgirshick

Differential Revision: D14779322

fbshipit-source-id: 3034e33ac06c227a33946063233f8626c0a442be
Latest commit 8c1e2af Apr 5, 2019
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github Add template for github issues Jan 30, 2018
cmake Include missing copyright headers Oct 18, 2018
demo Initial commit Jan 22, 2018
docker Use a requirements file to specify python dependencies May 24, 2018
CMakeLists.txt Provide a code of conduct file Oct 18, 2018 Update caffe2 repo links to pytorch repo links Dec 26, 2018
LICENSE Migrate URLs to Dec 26, 2018
NOTICE typo Apr 24, 2018
requirements.txt Encapsulate detectron code in a package May 7, 2018


Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.

At FAIR, Detectron has enabled numerous research projects, including: Feature Pyramid Networks for Object Detection, Mask R-CNN, Detecting and Recognizing Human-Object Interactions, Focal Loss for Dense Object Detection, Non-local Neural Networks, Learning to Segment Every Thing, Data Distillation: Towards Omni-Supervised Learning, DensePose: Dense Human Pose Estimation In The Wild, and Group Normalization.

Example Mask R-CNN output.


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:

using the following backbone network architectures:

Additional backbone architectures may be easily implemented. For more details about these models, please see References below.



Detectron is released under the Apache 2.0 license. See the 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, please use the following BibTeX entry.

  author =       {Ross Girshick and Ilija Radosavovic and Georgia Gkioxari and
                  Piotr Doll\'{a}r and Kaiming He},
  title =        {Detectron},
  howpublished = {\url{}},
  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.


Please find installation instructions for Caffe2 and Detectron in

Quick Start: Using Detectron

After installation, please see 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 and improving our installation instructions and troubleshooting documents). Please see for more information about contributing to Detectron.


You can’t perform that action at this time.