Detectron


Detectron is a high-performance codebase for object detection, covering both bounding box and object instance segmentation outputs. It's designed to be flexible for rapid implementation and evaluation. Detectron is used by the Facebook AI Research (FAIR) team on numerous state-of-the-art research projects.

Rapid, flexible research

Detectron was built by Facebook AI Research (FAIR) to support rapid implementation and evaluation of novel computer vision research. It includes implementations for the following object detection algorithms:

  • Mask R-CNN

  • RetinaNet

  • Faster R-CNN

  • RPN

  • Fast R-CNN

  • R-FCN

Detectron can be used out-of-the-box for general object detection or modified to train and run inference on your own datasets. It's written in Python and will be powered by the PyTorch 1.0 deep learning framework.


Detectron example

Get Started

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  • Install Caffe2 with CUDA support. If you already have Caffe2 installed, make sure to update it to a version that includes the Detectron module.

  • 2


  • Install Python dependences and the COCO API.

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    pip install numpy>=1.13 pyyaml>=3.12 matplotlib opencv-python>=3.2 setuptools Cython mock scipy
    
    # COCOAPI=/path/to/clone/cocoapi
    git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
    cd $COCOAPI/PythonAPI
    # Install into global site-packages
    make install
    # Alternatively, if you do not have permissions or prefer
    # not to install the COCO API into global site-packages
    python2 setup.py install --user
              

  • 3


  • Clone the Detectron repository and set up Python modules.

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    # DETECTRON=/path/to/clone/detectron
    git clone https://github.com/facebookresearch/detectron $DETECTRON
    cd $DETECTRON/lib && make
              

  • 4

  • Run inference using pretrained Detectron models, or install datasets and follow advanced directions to train on your own data.

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    PyTorch

    PyTorch is an open source deep learning framework built to be flexible and modular for research, with the stability and support needed for production deployment. It enables fast, flexible experimentation through a tape-based autograd system designed for immediate and python-like execution.

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