Matchnet is a deep learning approach for patch-based local image matching, which
jointly learns feature representation and matching function from data. More
details about this approach can be found in ourCVPR'15 paper.
This repository contains reference source code for evaluating MatchNet models onPhototour Patch dataset.
Below is a step-by-step guide for downloading the dataset, generate patch
database and running evaluation with Matchnet models. We assumeCaffe is installed (preferably with GPU
support) and Pycaffe (Caffe's python interface) is also installed and added to
PYTHONPATH.
Clone the repository.
git clone https://github.com/hanxf/matchnet.git
cd matchnet
Generate leveldb database for each dataset. The databases are saved under data/leveldb.
./run_gen_data.sh
Download pretrained Matchnet models. (Here we only download the model trained on liberty. For more models see models/README.md
cd models
curl -O http://cs.unc.edu/~xufeng/matchnet/models/liberty_r_0.01_m_0.feature_net.pb
curl -O http://cs.unc.edu/~xufeng/matchnet/models/liberty_r_0.01_m_0.classifier_net.pb
cd ..
Evalute the liberty model on notredame's test set. (Remove the quoted argument to use CPU.)
./run_eval.sh liberty notredame "--use_gpu --gpu_id=0"
When the script is done, the last line should be the following:
Error rate at 95% recall: 4.48%
License and Citation
Matchnet source code is released under BSD license. The reference models are released for unrestriced use.
Please cite our paper if Matchnet helps your research:
@inproceedings{matchnet_cvpr_15,
Author = {Han, Xufeng and Leung, Thomas and Jia, Yangqing and Sukthankar, Rahul and Berg, Alexander. C.},
Booktitle = {CVPR},
Title = {MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching},
Year = {2015}
}