This is an experimental Tensorflow implementation of Faster RCNN - a convnet for object detection with a region proposal network. For details about R-CNN please refer to the paper Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun.

1. Requirements for Tensorflow (see: Tensorflow)

2. Python packages you might not have: cython, python-opencv, easydict

1. For training the end-to-end version of Faster R-CNN with VGG16, 3G of GPU memory is sufficient (using CUDNN)

1. Clone the Faster R-CNN repository

# Make sure to clone with --recursive
git clone --recursive https://github.com/smallcorgi/Faster-RCNN_TF.git

2. Build the Cython modules

   cd $FRCN_ROOT/lib
   sh make.sh

After successfully completing basic installation, you'll be ready to run the demo.

To run the demo

cd $FRCN_ROOT
./tools/demo.py

The demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2007.

Pre-trained ImageNet models can be downloaded here.

Download model training on PASCAL VOC 2007 here.