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TensorFlow implementation of RFCN

Paper is available on https://arxiv.org/abs/1605.06409.

Building

The ROI pooling and the MS COCO loader needs to be compiled first. To do so, run make in the root directory of the project. You may need to edit BoxEngine/ROIPooling/Makefile if you need special linker/compiler options.

Testing

You can run trained models with test.py. Model path should be given without file extension (without .data* and .index). An example:

preview

Pretrained model

You can download a pretrained model from here:

http://xdever.engineerjs.com/rfcn-tensorflow-export.tar.bz2

Extract it to your project directory. Then you can run the network with the following command:

./test.py -n export/model -i <input image> -o <output image>

License

The software is under Apache 2.0 license. See http://www.apache.org/licenses/LICENSE-2.0 for further details.

Notes

This code requires TensorFlow 1.0.

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RFCN implementation in TensorFlow

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  • Python 78.0%
  • C++ 15.7%
  • C 5.5%
  • Makefile 0.8%