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    README.md

    Detectron is deprecated. Please see 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. 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.

    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:

    using the following backbone network architectures:

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

    Update

    License

    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.

    @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

    项目简介

    🚀 Github 镜像仓库 🚀

    源项目地址

    https://github.com/facebookresearch/Detectron

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    开发语言

    • Python 94.3 %
    • CMake 4.7 %
    • C++ 0.4 %
    • MATLAB 0.3 %
    • Cuda 0.2 %