py-RFCN-priv

py-RFCN-priv is based on py-R-FCN-multiGPU, thanks for bharatsingh430's job.

Disclaimer

The official R-FCN code (written in MATLAB) is available here.

py-R-FCN is modified from the offcial R-FCN implementation and py-faster-rcnn code, and the usage is quite similar to py-faster-rcnn.

py-R-FCN-multiGPU is a modified version of py-R-FCN, the original code is available here.

py-RFCN-priv also supports soft-nms.

caffe-priv supports convolution_depthwise, roi warping, roi mask pooling, bilinear interpolation, selu.

New features

py-RFCN-priv supports:

• Label shuffling (only single GPU training);
• PIXEL_STD;
• Anchors outside image (described in FPN);
• ceil_mode in pooling layer ;
• Performing bilinear interpolation operator accoording to input blobs size.

Installation

1. Clone the py-RFCN-priv repository

   git clone https://github.com/soeaver/py-RFCN-priv

   We'll call the directory that you cloned py-RFCN-priv into RFCN_ROOT

2. Build the Cython modules

   cd $RFCN_ROOT/lib
   make

3. Build Caffe and pycaffe

   cd $RFCN_ROOT/caffe-priv
   # Now follow the Caffe installation instructions here:
   #   http://caffe.berkeleyvision.org/installation.html
   
   # cp Makefile.config.example Makefile.config
   # If you're experienced with Caffe and have all of the requirements installed
   # and your Makefile.config in place, then simply do:
   make all -j && make pycaffe -j

   Note: Caffe must be built with support for Python layers!

   # In your Makefile.config, make sure to have this line uncommented
   WITH_PYTHON_LAYER := 1
   # Unrelatedly, it's also recommended that you use CUDNN
   USE_CUDNN := 1
   # NCCL (https://github.com/NVIDIA/nccl) is necessary for multi-GPU training with python layer
   USE_NCCL := 1

License

py-RFCN-priv and caffe-priv are released under the MIT License (refer to the LICENSE file for details).

Citing

If you find R-FCN or soft-nms useful in your research, please consider citing:

@article{dai16rfcn,
    Author = {Jifeng Dai, Yi Li, Kaiming He, Jian Sun},
    Title = {{R-FCN}: Object Detection via Region-based Fully Convolutional Networks},
    Journal = {arXiv preprint arXiv:1605.06409},
    Year = {2016}
}

@article{1704.04503,
  Author = {Navaneeth Bodla and Bharat Singh and Rama Chellappa and Larry S. Davis},
  Title = {Improving Object Detection With One Line of Code},
  Journal = {arXiv preprint arXiv:1704.04503},
  Year = {2017}
}