Skip to content

Codes for CVPR2016 paper "CRAFT Objects from Images"

Notifications You must be signed in to change notification settings

byangderek/CRAFT

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

17 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

README

The codes are with the CVPR2016 paper "CRAFT Objects from Images".

In a word, we extend the conventional two-stage object detection framework (first locating object proposals, then classifying object categories) to a four-stage pipeline, in which the proposal localization task is solved with a cascade network of Region Proposal Network (RPN) and Fast R-CNN to improve the proposal quality, while the object classification task is handled by a cascade network of two Fast R-CNN nets with different objective functions (one-hot classification and one-vs-rest classification) to eliminate false positives.

We name our approach "CRAFT" (short for "Cascade Rpn And FasT-rcnn") and show considerable improvement over Fast R-CNN and Faster R-CNN baselines on PASCAL VOC 07/12 and ILSVRC datasets. For more details please refer to our CVPR2016 paper.

The codes are built on RPN (Stage 1) and Fast R-CNN (Stage 2,3,4). It would be easier to use the codes if you are familiar with these two projects.

The codes are tested on Ubuntu 14.04, 256GB Memory, Titan X GPU, MATLAB R2015a.

Preparation

  1. Follow instructions in Faster R-CNN to make the codes in 1_RPN, using Caffe provided by Shaoqing Ren
  2. Follow instructions in Fast R-CNN to make the codes in 2_CasRPN, 3_FRCN, and 4_CasFRCN, using our slightly modified Caffe
  3. Download the VGG16 pre-trained model and PASCAL VOC 2012 dataset and make proper links pointing to them
  4. You can create a soft link of folders caffe-fast-rcnn and data for 2_CasRPN, 3_FRCN, and 4_CasFRCN for convenience.

Training and testing

The whole pipeline is stage-wise. Now we show how to train an object detector using CRAFT approach on PASCAL VOC 2012 train+val dataset and test it on PASCAL VOC 2012 test set. For simplicity, we do not use joint training between RPN and Fast R-CNN networks.

Stage 1. RPN

cd 1_RPN
matlab ./experiments/script_faster_rcnn_VOC2012_VGG16.m
matlab saveProposals.m

Stage 2. CasRPN

cd 2_CasRPN
bash train.sh
bash test.sh
matlab saveProposals.m

Stage 3. FRCN

cd 3_FRCN
bash train.sh
bash test.sh
matlab saveDetections.m

Stage 4. CasFRCN

cd 4_CasFRCN
bash train.sh
bash test.sh

Results

                      | training data                          | test data            | mAP   

------------------------- |:--------------------------------------:|:--------------------:|:-----: CRAFT, VGG-16 | VOC 2007 trainval + 2012 trainval | VOC 2007 test | 75.7% CRAFT, VGG-16 | VOC 2012 trainval | VOC 2012 test | 71.3%

Note: The real mAP results may vary a little from the above results reported in the paper. We do not adopt joint training between RPN and Fast R-CNN currently.

Reference

If you use our codes in your research, we are grateful if you cite the paper:

@inproceedings{binyang16craft,
  title={Craft Objects from Images},
  author={Yang, Bin and Yan, Junjie and Lei, Zhen and Li, Stan},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2016}
}

Acknowledgement

We give our sincere gratitude to the following people, groups and institutions:

  • Anonymous reviewers
  • Ross Girshick for the Fast R-CNN project
  • Shaoqing Ren for the Faster R-CNN project
  • Caffe team
  • VGG team
  • SenseTime Group Limited
  • NVIDIA Corporation

About

Codes for CVPR2016 paper "CRAFT Objects from Images"

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published