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
- Clone the repository
git clone https://github.com/DeepLearnPhysics/faster-rcnn.git
- Source configure script (you have to do this each time you login to a new shell).
cd faster-rcnn
source configure.sh
- 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.
- Build the Cython modules
make clean
make
here)
Setup data for training (py-faster-rcnn has more detailed instructions-
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
-
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
-
It should have this basic structure
$VOCdevkit/ # development kit $VOCdevkit/VOCcode/ # VOC utility code $VOCdevkit/VOC2007 # image sets, annotations, etc. # ... and several other directories ...
-
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.
-
[Optional] follow similar steps to get PASCAL VOC 2010 and 2012
-
[Optional] for coco data set see py-faster-rcnn instruction here
Train your own model
- 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 ..
- Train (and test, evaluation)
python example/train_coco.py
- 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}
}