By Jifeng Dai, Yi Li, Kaiming He, Jian Sun
R-FCN is a region-based object detection framework leveraging deep fully-convolutional networks, which is accurate and efficient. In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region sub-network hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image. R-FCN can natually adopt powerful fully convolutional image classifier backbones, such as ResNets, for object detection.
R-FCN was initially described in a NIPS 2016 paper.
This code has been tested on Windows 7/8 64 bit, Windows Server 2012 R2, and Ubuntu 14.04, with Matlab 2014a.
R-FCN is released under the MIT License (refer to the LICENSE file for details).
If you find R-FCN useful in your research, please consider citing:
@article{dai16rfcn,
Author = {Jifeng Dai, Yi Li, Kaiming He, Jian Sun},
Title = {{R-FCN}: Object Detection via Region-based Fully Convolutional Networks},
Journal = {arXiv preprint arXiv:1605.06409},
Year = {2016}
}
| training data | test data | mAP | time/img (K40) | time/img (Titian X)
-------------------|:-------------------:|:---------------------:|:-----:|:--------------:|:------------------:| R-FCN, ResNet-50 | VOC 07+12 trainval | VOC 07 test | 77.4% | 0.12sec | 0.09sec | R-FCN, ResNet-101 | VOC 07+12 trainval | VOC 07 test | 79.5% | 0.17sec | 0.12sec |
Caffe
build for R-FCN (included in this repository, seeexternal/caffe
)- If you are using Windows, you may download a compiled mex file by running
fetch_data/fetch_caffe_mex_windows_vs2013_cuda75.m
- If you are using Linux or you want to compile for Windows, please recompile our Caffe branch.
- If you are using Windows, you may download a compiled mex file by running
- MATLAB 2014a or later
GPU: Titan, Titan X, K40, K80.
- Run
fetch_data/fetch_caffe_mex_windows_vs2013_cuda75.m
to download a compiled Caffe mex (for Windows only). - Run
fetch_data/fetch_demo_model_ResNet101.m
to download a R-FCN model using ResNet-101 net (trained on VOC 07+12 trainval). - Run
rfcn_build.m
. - Run
startup.m
. - Run
experiments/script_rfcn_demo.m
to apply the R-FCN model on demo images.
- Run
fetch_data/fetch_caffe_mex_windows_vs2013_cuda75.m
to download a compiled Caffe mex (for Windows only). - Run
fetch_data/fetch_model_ResNet50.m
to download an ImageNet-pre-trained ResNet-50 net. - Run
fetch_data/fetch_model_ResNet101.m
to download an ImageNet-pre-trained ResNet-101 net. - Run
fetch_data/fetch_region_proposals.m
to download the pre-computed region proposals. - Download VOC 2007 and 2012 data to ./datasets.
- Run
rfcn_build.m
. - Run
startup.m
.
- Run
experiments/script_rfcn_VOC0712_ResNet50_OHEM_ss.m
to train a model using ResNet-50 net with online hard example mining (OHEM), leveraging selective search proposals. The accuracy should be ~75.4% in mAP.- Note: the training time is ~13 hours on Titian X.
- Run
experiments/script_rfcn_VOC0712_ResNet50_OHEM_rpn.m
to train a model using ResNet-50 net with OHEM, leveraging RPN proposals (using ResNet-50 net). The accuracy should be ~77.4% in mAP.- Note: the training time is ~13 hours on Titian X.
- Run
experiments/script_rfcn_VOC0712_ResNet101_OHEM_rpn.m
to train a model using ResNet-101 net with OHEM, leveraging RPN proposals (using ResNet-101 net). The accuracy should be ~79.5% in mAP.- Note: the training time is ~19 hours on Titian X.
- Check other scripts in
./experiments
for more settings.
Note:
- In all the experiments, training is performed on VOC 07+12 trainval, and testing is performed on VOC 07 test.
- Results are subject to some random variations. We have run 'experiments/script_rfcn_VOC0712_ResNet50_OHEM_rpn.m' for 5 times, the results are 77.1%, 77.3%, 77.7%, 77.9%, and 77.0%. The mean is 77.4%, and the std is 0.39%.
- Running time is not recorded in the test log (which is slower), but instead in an optimized implementation.
If the automatic "fetch_data" fails, you may manually download resouces from: