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

R*CNN was initialiy described in an [arXiv tech report] (http://arxiv.org/abs/1505.01197)

R*CNN is released under the BSD License

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}
}

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

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

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

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

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. Reference models

   Download the VGG16 reference model trained on ImageNet from here

4. Trained models

   Download the models as described in the paper from here