Fast R-CNN

Created by Ross Girshick at Microsoft Research, Redmond.

Introduction

Fast R-CNN is a clean and fast framework for object detection. Compared to traditional R-CNN, and its accelerated version SPPnet, Fast R-CNN trains networks using a multi-task loss in a single fine-tuning run. The multi-task loss simplifies and speeds up training. Unlike SPPnet, all network layers can be learned during fine-tuning. We show that this difference has practical ramifications for very deep networks, such as VGG16, where mAP suffers when only the fully-connected layers are fine-tuned. Compared to "slow" R-CNN, Fast R-CNN is 9x faster at training VGG16 for detection, 213x faster for detection, and achieves a significantly higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate.

Fast R-CNN was initially described in an arXiv tech report.

Citing Fast R-CNN

If you find R-CNN useful in your research, please consider citing:

@article{girshick15fastrcnn,
    Author = {Ross Girshick},
    Title = {Fast R-CNN},
    Journal = {arXiv preprint arXiv:todo},
    Year = {2015}
}

License

Fast R-CNN is released under the MIT License (refer to the LICENSE file for details).

Installation requirements

1. Requirements for Caffe and pycaffe (see: Caffe installation instructions)
2. Additional Python packages: cython, python-opencv, easydict
3. [optional] MATLAB (required for PASCAL VOC evaluation only)

Installation (sufficient for the demo)

1. Clone the Fast R-CNN repository

# Make sure to clone with --recursive
git clone --recursive [email protected]:rbgirshick/fast-rcnn.git

2. We'll call the directory that you cloned Fast R-CNN into FRCN_ROOT

3. Build the Cython modules

   cd $FRCN_ROOT/lib
   make

4. Build Caffe and pycaffe

   cd $FRCN_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

5. Download pre-computed Fast R-CNN detectors

   cd $FRCN_ROOT
   ./data/scripts/fetch_fast_rcnn_models.sh

   This will populate the $FRCN_ROOT/data folder with fast_rcnn_models. See data/README.md for details.

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 2012. The object proposals are pre-computed in order to reduce installation requirements.

Beyond the demo: installation for training and testing models

1. Download the training, validation, test data and VOCdevkit

   wget http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
   wget http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/VOCtest_06-Nov-2007.tar
   wget http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/VOCdevkit_08-Jun-2007.tar

2. Extract all of these tars into one directory named VOCdevkit

   tar xvf VOCtrainval_06-Nov-2007.tar
   tar xvf VOCtest_06-Nov-2007.tar
   tar xvf VOCdevkit_08-Jun-2007.tar

3. It should have this basic structure

   $VOCdevkit/                           # development kit
   $VOCdevkit/VOCcode/                   # VOC utility code
   $VOCdevkit/VOC2007                    # image sets, annotations, etc.
   # ... and several other directories ...

4. Establish symlinks for the PASCAL VOC dataset

   cd $FRCN_ROOT/data
   ln -s $VOCdevkit VOCdevkit2007

5. Establish a symlink for your cache directory

   cd $FRCN_ROOT/data
   # /your/cache/path needs to be a directory that will hold a few GB of data
   ln -s /your/cache/path cache

6. [Optional] follow similar steps to get PASCAL VOC 2010 and 2012

7. Follow the next sections to download pre-computed object proposals and pre-trained ImageNet models

Download pre-computed Selective Search object proposals

Pre-computed selective search boxes can also be downloaded for VOC2007 and VOC2012.

cd $FRCN_ROOT
./data/scripts/fetch_selective_search_data.sh

This will populate the $FRCN_ROOT/data folder with selective_selective_data.

Download pre-trained ImageNet models

Pre-trained ImageNet models can be downloaded for the three networks described in the paper: CaffeNet (model S), VGG_CNN_M_1024 (model M), and VGG16 (model L).

cd $FRCN_ROOT
./data/scripts/fetch_imagenet_models.sh

These models are all available in the Caffe Model Zoo, but are provided here for your convenience.

Usage

Train a Fast R-CNN detector. For example, train a VGG16 network on VOC 2007 trainval:

./tools/train_net.py --gpu 0 --solver models/VGG16/solver.prototxt \
	--weights data/imagenet_models/VGG16.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/VGG16/test.prototxt \
	--net output/default/voc_2007_trainval/vgg16_fast_rcnn_iter_40000.caffemodel

Test output is written underneath $FRCN_ROOT/output.

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

./tools/compress_net.py --def models/VGG16/test.prototxt \
	--def-svd models/VGG16/compressed/test.prototxt \
    --net output/default/voc_2007_trainval/vgg16_fast_rcnn_iter_40000.caffemodel
# Test the model you just compressed
./tools/test_net.py --gpu 0 --def models/VGG16/compressed/test.prototxt \
	--net output/default/voc_2007_trainval/vgg16_fast_rcnn_iter_40000_svd_fc6_1024_fc7_256.caffemodel

Experiment scripts

Scripts to reproduce the experiments in the paper (up to stochastic variation) are provided in $FRCN_ROOT/experiments/scripts. Log files for experiments are located in experiments/logs.