faster-rcnn

A Tensorflow implementation of faster RCNN detection framework toward 3D object detection. This repository is based on the original python Caffe implementation of faster RCNN by Ross Girshick available here and implementation to Tensorflow by Xinlei Chen available here. While much of the code follows Xinlei's implementation, the reason why we did not fork is because we plan to extend further to 3D. This involves much reorganization of code/directory structures and departs from the goal of replicating py-caffe implementation. Note changes made by Xinlei (summarized in their paper An Implementation of Faster RCNN with Study for Region Sampling) is inherited as is.

Note: This implementation followed Xinlei's method step by step, and may still lack some functionalities and/or performance. The current goal is to reproduce the results in Xinlei's paper. The network does get trained with vgg implementation (resnet not yet tried).

Prerequisites

• Tensorflow: currently tested with v1.3. If concerned, Xinlei's Tensorflow fork implements the original ROI pooling in py-caffe.
• Python packages: cython, opencv-python, easydict

Installation

1. Clone the repository

git clone https://github.com/DeepLearnPhysics/faster-rcnn.git

2. Source configure script (you have to do this each time you login to a new shell).

cd faster-rcnn
source configure.sh

3. Update your -arch in setup script to match your GPU

# Change the GPU architecture (-arch) if necessary
vim setup.py

GPU model	Architecture
TitanX (Maxwell/Pascal)	sm_52
GTX 960M	sm_50
GTX 1080 (Ti)	sm_61
Grid K520 (AWS g2.2xlarge)	sm_30
Tesla K80 (AWS p2.xlarge)	sm_37

Note: You are welcome to contribute the settings on your end if you have made the code work properly on other GPUs.

4. Build the Cython modules

make clean
make

Setup data for training (py-faster-rcnn has more detailed instructions here)

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

   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
   wget http://host.robots.ox.ac.uk/pascal/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. Create symlinks for the PASCAL VOC dataset

    cd $FRCN_ROOT/data
    ln -s $VOCdevkit VOCdevkit2007

   Using symlinks is a good idea because you will likely want to share the same PASCAL dataset installation between multiple projects.

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

6. [Optional] for coco data set see py-faster-rcnn instruction here

Train your own model

1. Download pre-trained models and weights. The current code support VGG16. Pre-trained models are provided by slim, you can get the pre-trained models here and set them in the data folder. For example for VGG16 model, you can set up like:

   cd data
   wget -v http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz
   tar -xzvf vgg_16_2016_08_28.tar.gz
   mv vgg_16.ckpt vgg16.ckpt
   cd ..

2. Train (and test, evaluation)

python example/train_coco.py

3. Visualization with Tensorboard

tensorboard --logdir=tensorboard/log &

By default, trained networks are saved under:

output/[NET]

Tensorboard information for train and validation is saved under:

tensorboard/[NET]/

Citation

For citation, please consider citing Xinlei's work

@article{chen17implementation,
    Author = {Xinlei Chen and Abhinav Gupta},
    Title = {An Implementation of Faster RCNN with Study for Region Sampling},
    Journal = {arXiv preprint arXiv:1702.02138},
    Year = {2017}
}

as well as faster RCNN citation:

@inproceedings{renNIPS15fasterrcnn,
    Author = {Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun},
    Title = {Faster {R-CNN}: Towards Real-Time Object Detection
             with Region Proposal Networks},
    Booktitle = {Advances in Neural Information Processing Systems ({NIPS})},
    Year = {2015}
}