- Inference code
- Training code
Thank you everyone for your interest in GIM. I am currently catching up with the DDL for my paper. After the completion of my paper, I will continue with the code releasing of GIM. Thank you for your patience. Code releasing is expected to continue in late March.
Go to Huggingface to quickly try our model online.
My code running environment is:
GeForce RTX 3090
Ubuntu 20.04.3
Python (3.8.10)
Pytorch 1.10.2 (py3.8_cuda11.3_cudnn8.2.0_0)
For the specific environment, please run the following command to install anaconda
conda env create -f environment.yml
If the above command fails to install the environment directly, please refer to the clean environment in environment.txt
to install each package.
Clone the repository
git clone https://github.com/xuelunshen/gim.git
cd gim
mkdir weights
Download model weight from Google Drive
Put it on the folder weights
Run the following command
python demo.py
The code will match a.png
and b.png
in the folder assets/demo
, and output a_b_match.png
and a_b_warp.png
.
Click to show
a_b_warp.png
.
a_b_warp.png
shows the effect of projecting `image b` onto `image a` using homography
If our code helps your research, please give a citation to our paper โค๏ธ Thank you very much.
@inproceedings{
xuelun2024gim,
title={GIM: Learning Generalizable Image Matcher From Internet Videos},
author={Xuelun Shen and Zhipeng Cai and Wei Yin and Matthias Mรผller and Zijun Li and Kaixuan Wang and Xiaozhi Chen and Cheng Wang},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024}
}
This repository is under the MIT License. This content/model is provided here for research purposes only. Any use beyond this is your sole responsibility and subject to your securing the necessary rights for your purpose.