Fast R-CNN

Requirements

1. Requirements for Caffe and pycaffe
2. Additional Python packages: cython, python-opencv, easydict
3. Matlab (required for PASCAL VOC evaluation only)

Installation

1. Extract the source code: $ tar zxvf fast-rcnn.tgz; call the source directory $FRCNN
2. Build Cython modules: $ cd $FRCNN/src && make (there will probably be some benign warnings that you can ignore)
3. Build Caffe and pycaffe: $ cd $FRCNN/caffe-fast-rcnn and follow the Caffe installation instructions
4. Establish symlinks for VOCdevkits
5. Symlink $FRCNN/data/VOCdevkit2007 to where you have the PASCAL VOC 2007 devkit and data installed
6. And similiarly for other PASCAL VOC 20XY datasets
7. Symlink $FRCNN/data/cache to somewhere that will store cached dataset files

Usage

Train a Fast R-CNN detector. For example, train a VGG 16 network on VOC 2007 trainval:

./tools/train_net.py --gpu 0 --solver models/VGG_16/solver.prototxt --weights /data/reference_caffe_nets/VGG_ILSVRC_16_layers.v2.caffemodel

Test a Fast R-CNN detector. For example, test the VGG 16 network on VOC 2007 test:

./tools/test_net.py --gpu 1 --def models/VGG_16/test.prototxt --net snapshots/vgg16_fast_rcnn_iter_40000.caffemodel

Test output is written underneath $FRCNN/output.

Compress a Fast R-CNN model using SVD on the fully-connected layers:

./tools/compress_model.py --def models/VGG_16/test.prototxt --def-svd models/VGG_16/compressed/test.prototxt --net snapshots/vgg16_fast_rcnn_iter_40000.caffemodel