Atten Patch is a research project aimed at enhancing visual place recognition through the utilization of local descriptors. The project focuses on leveraging key features to optimize the algorithm's speed without compromising precision. Additionally, it incorporates training-based methods to further augment accuracy.
To get started with the Atten Patch project, follow these steps:
- Installation: Clone the repository to your local machine.
git clone https://github.com/H-tr/atten_patch.git
cd atten_patch
- Dependencies: Install the required dependencies using
pip
and the providedrequirements.txt
file.
pip install -r requirements.txt
You can install the pretrained models and checkpoints into pretrained_models
mim download mmsegmentation --config segformer_mit-b4_8xb2-160k_ade20k-512x512 --dest pretrained_models
This will install all necessary dependencies for the project.
- Configuration: You can configurate the project in
config
.
Evaluate on SPED dataset:
# Command to run the project
$ python main.py --dataset SPED --config config/vpr_bench_config.yaml
This project is licensed under the MIT License - see the LICENSE file for details.
The MIT License is a permissive open-source license that allows for the modification and distribution of the software while requiring a copy of the original license and disclaimer of warranty to be included in derivative works. You can find the full text of the license in the LICENSE file within this repository.
We would like to extend our gratitude to the following individuals and organizations for their contributions, guidance, and support:
- [Zhang Dongshuo]: Kindly provide source code