Our paper was accepted to CVPR2023. You can also find our full-version paper on arXiv
conda create -n TINC python=3.9
conda activate TINC
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia
pip install -r requirements.txt
(1) Single compression task
relevant compression parameters can be modified in opt/SingleTask/default.yaml.
python main.py -p opt/SingleTask/default.yaml -g 0
final compressed file path: outputs/default_{time}/compressed/
final decompressed file path: outputs/default_{time}/decompressed.tif
training result:
tensorboard --logdir=outputs/default_{time}
(2) Run multiple tasks at once
relevant compression parameters can be modified in opt/MultiTask/default.yaml.
python MultiTask.py -p opt/MultiTask/default.yaml -g 0,1,2,3 -stp main.py -debug
@inproceedings{yang2023tinc,
title={TINC: Tree-structured Implicit Neural Compression},
author={Yang, Runzhao},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={18517--18526},
year={2023}
}
If you need any help or are looking for cooperation feel free to contact us. [email protected]