By Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun at Microsoft Research

Faster R-CNN is an object detection framework based on deep convolutional networks, which includes a Region Proposal Network (RPN) and an Object Detection Network. Both networks are trained for sharing convolutional layers for fast testing.

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

This repo contains a MATLAB re-implementation of Fast R-CNN. Details about Fast R-CNN are in: rbgirshick/fast-rcnn.

This code has been tested on Windows 7/8 64-bit, Windows Server 2012 R2, and Linux, and on MATLAB 2014a.

Python version is available at py-faster-rcnn.

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

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

@article{ren15fasterrcnn,
    Author = {Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun},
    Title = {{Faster R-CNN}: Towards Real-Time Object Detection with Region Proposal Networks},
    Journal = {arXiv preprint arXiv:1506.01497},
    Year = {2015}
}

                      | training data                          | test data            | mAP   | time/img

------------------------- |:--------------------------------------:|:--------------------:|:-----:|:-----: Faster RCNN, VGG-16 | VOC 2007 trainval | VOC 2007 test | 69.9% | 198ms Faster RCNN, VGG-16 | VOC 2007 trainval + 2012 trainval | VOC 2007 test | 73.2% | 198ms Faster RCNN, VGG-16 | VOC 2012 trainval | VOC 2012 test | 67.0% | 198ms Faster RCNN, VGG-16 | VOC 2007 trainval&test + 2012 trainval | VOC 2012 test | 70.4% | 198ms

Note: The mAP results are subject to random variations. We have run 5 times independently for ZF net, and the mAPs are 59.9 (as in the paper), 60.4, 59.5, 60.1, and 59.5, with a mean of 59.88 and std 0.39.

0. Requirements: software
1. Requirements: hardware
2. Preparation for Testing
3. Testing Demo
4. Preparation for Training
5. Training
6. Resources

0. Caffe build for Faster R-CNN (included in this repository, see external/caffe)
   • If you are using Windows, you may download a compiled mex file by running fetch_data/fetch_caffe_mex_windows_vs2013_cuda65.m
   • If you are using Linux or you want to compile for Windows, please follow the instructions on our Caffe branch.
1. MATLAB

GPU: Titan, Titan Black, Titan X, K20, K40, K80.

0. Region Proposal Network (RPN)
   • 2GB GPU memory for ZF net
   • 5GB GPU memory for VGG-16 net
1. Object Detection Network (Fast R-CNN)
   • 3GB GPU memory for ZF net
   • 8GB GPU memory for VGG-16 net

0. Run fetch_data/fetch_caffe_mex_windows_vs2013_cuda65.m to download a compiled Caffe mex (for Windows only).
1. Run faster_rcnn_build.m
2. Run startup.m

0. Run fetch_data/fetch_faster_rcnn_final_model.m to download our trained models.
1. Run experiments/script_faster_rcnn_demo.m to test a single demo image.
   • You will see the timing information as below. We get the following running time on K40 @ 875 MHz and Intel Xeon CPU E5-2650 v2 @ 2.60GHz for the demo images with VGG-16:

   001763.jpg (500x375): time 0.201s (resize+conv+proposal: 0.150s, nms+regionwise: 0.052s)
   004545.jpg (500x375): time 0.201s (resize+conv+proposal: 0.151s, nms+regionwise: 0.050s)
   000542.jpg (500x375): time 0.192s (resize+conv+proposal: 0.151s, nms+regionwise: 0.041s)
   000456.jpg (500x375): time 0.202s (resize+conv+proposal: 0.152s, nms+regionwise: 0.050s)
   001150.jpg (500x375): time 0.194s (resize+conv+proposal: 0.151s, nms+regionwise: 0.043s)
   mean time: 0.198s

   and with ZF net:

   001763.jpg (500x375): time 0.061s (resize+conv+proposal: 0.032s, nms+regionwise: 0.029s)
   004545.jpg (500x375): time 0.063s (resize+conv+proposal: 0.034s, nms+regionwise: 0.029s)
   000542.jpg (500x375): time 0.052s (resize+conv+proposal: 0.034s, nms+regionwise: 0.018s)
   000456.jpg (500x375): time 0.062s (resize+conv+proposal: 0.034s, nms+regionwise: 0.028s)
   001150.jpg (500x375): time 0.058s (resize+conv+proposal: 0.034s, nms+regionwise: 0.023s)
   mean time: 0.059s

   • The visual results might be different from those in the paper due to numerical variations.

   • Running time on other GPUs

      GPU / mean time        |        VGG-16        |        ZF          
     

   :------------------------:|:--------------------:|:--------------------: K40 | 198ms | 59ms
   Titan Black | 174ms | 56ms
   Titan X | 151ms | 59ms

0. Run fetch_data/fetch_model_ZF.m to download an ImageNet-pre-trained ZF net.
1. Run fetch_data/fetch_model_VGG16.m to download an ImageNet-pre-trained VGG-16 net.
2. Download VOC 2007 and 2012 data to ./datasets

0. Run experiments/script_faster_rcnn_VOC2007_ZF.m to train a model with ZF net. It runs four steps as follows:
   • Train RPN with conv layers tuned; compute RPN results on the train/test sets.
   • Train Fast R-CNN with conv layers tuned using step-1 RPN proposals; evaluate detection mAP.
   • Train RPN with conv layers fixed; compute RPN results on the train/test sets.
   • Train Fast R-CNN with conv layers fixed using step-3 RPN proposals; evaluate detection mAP.
   • Note: the entire training time is ~12 hours on K40.
1. Run experiments/script_faster_rcnn_VOC2007_VGG16.m to train a model with VGG net.
   • Note: the entire training time is ~2 days on K40.
2. Check other scripts in ./experiments for more settings.

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0. Experiment logs: OneDrive, DropBox, BaiduYun
1. Regions proposals of our trained RPN:
   • ZF net trained on VOC 07 trainval OneDrive, BaiduYun
   • ZF net trained on VOC 07/12 trainval OneDrive, BaiduYun
   • VGG net trained on VOC 07 trainval OneDrive, BaiduYun
   • VGG net trained on VOC 07/12 trainval OneDrive, BaiduYun
   • Note: the proposals are in the format of [left, top, right, bottom, confidence]

If the automatic "fetch_data" fails, you may manually download resouces from:

0. Pre-complied caffe mex:
   • Windows-based mex complied with VS2013 and Cuda6.5: OneDrive, DropBox, BaiduYun
1. ImageNet-pretrained networks:
   • Zeiler & Fergus (ZF) net OneDrive, DropBox, BaiduYun
   • VGG-16 net OneDrive, DropBox, BaiduYun
2. Final RPN+FastRCNN models: OneDrive, DropBox, BaiduYun