Faster R-CNN

This is an experimental implementation of Faster R-CNN using Chainer based on Ross Girshick's py-faster-rcnn codes.

Requirement

Using anaconda is strongly recommended.

• Python 2.7.6+, 3.4.3+, 3.5.1+

  • Chainer 1.9.1+
  • NumPy 1.9, 1.10, 1.11
  • Cython 0.23+
  • OpenCV 2.9+, 3.1+

Installation of dependencies

pip install numpy
pip install cython
pip install chainer
# for python3
conda install -c https://conda.binstar.org/menpo opencv3
# for python2
conda install opencv

For Windows users

There's a known problem in cpu_nms.pyx. But a workaround has been posted here (and see also the issue posted to the original py-faster-rcnn).

Inference

1. Download pre-trained model

wget https://www.dropbox.com/s/2fadbs9q50igar8/VGG16_faster_rcnn_final.model?dl=0
mv VGG16_faster_rcnn_final.model?dl=0 VGG16_faster_rcnn_final.model

2. Build extensions

cd lib
python setup.py build_ext -i

3. Use forward.py

wget http://vision.cs.utexas.edu/voc/VOC2007_test/JPEGImages/004545.jpg

python forward.py --img_fn 004545.jpg --gpu 0

--gpu 0 turns on GPU. When you turn off GPU, use --gpu -1 or remove --gpu option.

Training

will be updated soon

Framework