R*CNN

Source code for R*CNN, created by Georgia Gkioxari at UC Berkeley.

Introduction

R*CNN was initialiy described in an arXiv tech report

License

R*CNN is released under the BSD License

Citing R*CNN

If you use R*CNN, please consider citing:

@article{rstarcnn2015,
    Author = {G. Gkioxari and R. Girshick and J. Malik},
    Title = {Contextual Action Recognition with R\*CNN},
    Booktitle = {ICCV},
    Year = {2015}
}

Contents

1. Requirements
2. Installation
3. Usage
4. Downloads

Requirements

1. Requirements for Caffe and pycaffe (see: Caffe installation instructions)

   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

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

Installation

1. Clone the RstarCNN repository

   # Make sure to clone with --recursive
   git clone --recursive https://github.com/gkioxari/RstarCNN.git

2. Build the Cython modules

   cd $ROOT/lib
   make

3. Build Caffe and pycaffe

   cd $ROOT/caffe-fast-rcnn
   # Now follow the Caffe installation instructions here:
   #   http://caffe.berkeleyvision.org/installation.html
   
   # If you're experienced with Caffe and have all of the requirements installed
   # and your Makefile.config in place, then simply do:
   make -j8 && make pycaffe

Usage

Train a R*CNN classifier. For example, train a VGG16 network on VOC 2012 trainval:

./tools/train_net.py --gpu 0 --solver models/VGG16_RstarCNN/solver.prototxt \
	--weights reference_models/VGG16.v2.caffemodel

Test a R*CNN classifier

./tools/test_net.py --gpu 0 --def models/VGG16_RstarCNN/test.prototxt \
	--net output/default/voc_2012_trainval/vgg16_fast_rstarcnn_joint_iter_40000.caffemodel

Downloads

1. PASCAL VOC 2012 Action Dataset

   Place the VOCdevkit2012 inside the $ROOT/data directory

   Download the selective search regions for the images from here and place them inside the $ROOT/data/cache directory

2. Berkeley Attributes of People Dataset

   Download the data from here and place them inside the $ROOT/data directory

3. Stanford 40 Dataset

   Download the data from here and place them inside $ROOT/data directory. R*CNN achieves 90.85% on the test set (trained models provided in 5)

4. Reference models

   Download the VGG16 reference model trained on ImageNet from here (500M)

5. Trained models

   Download the models as described in the paper from here (3.6G)