ShiftTolerant-LPIPS
Shift-tolerant Perceptual Similarity Metric
Abhijay Ghildyal, Feng Liu. In ECCV, 2022. [Arxiv]
Quick start
Please run python lpips_2imgs.py
Training
nohup python -u ./train.py --from_scratch --train_trunk \
--use_gpu --gpu_ids 0 \
--net alex --variant vanilla --name alex_vanilla \
> logs/train_alex_vanilla.out &
nohup python -u ./train.py --from_scratch --train_trunk \
--use_gpu --gpu_ids 1 \
--net alex --variant shift_tolerant --name alex_shift_tolerant \
> logs/train_alex_shift_tolerant.out &
nohup python -u ./train.py --from_scratch --train_trunk \
--use_gpu --gpu_ids 2 \
--net vgg --variant vanilla --name vgg_vanilla \
> logs/train_vgg_vanilla.out &
nohup python -u ./train.py --from_scratch --train_trunk \
--use_gpu --gpu_ids 3 \
--net vgg --variant shift_tolerant --name vgg_shift_tolerant \
> logs/train_vgg_shift_tolerant.out &Testing
Please download the original BAPPS dataset using this script (here). Then, update path to the dataset in global_config.json.
To reproduce the results in the paper run the following:
# AlexNet Vanilla
nohup bash n_pixel_shift_study/test_scripts/test.sh alex vanilla 0 64 50 > logs/eval_alex_vanilla.out &
# AlexNet Shift-tolerant
nohup bash n_pixel_shift_study/test_scripts/test.sh alex shift_tolerant 1 64 50 > logs/eval_alex_shift_tolerant.out &
# Vgg Vanilla
nohup bash n_pixel_shift_study/test_scripts/test.sh vgg vanilla 2 64 50 > logs/eval_vgg_vanilla.out &
# Vgg Shift-tolerant
nohup bash n_pixel_shift_study/test_scripts/test.sh vgg shift_tolerant 3 64 50 > logs/eval_vgg_shift_tolerant.out &Note: To train and test our models in this paper, we used Image.BICUBIC. The results are similar when other resizing methods are used. Please feel free to switch back to bilinear as used in the original LPIPS work (here).
Other Evaluations
For other evaluations refer to ./n_pixel_shift_study/.
Citation
If you find this repository useful for your research, please use the following.
@inproceedings{ghildyal2022stlpips,
title={Shift-tolerant Perceptual Similarity Metric},
author={Ghildyal, Abhijay and Liu, Feng},
booktitle={European Conference on Computer Vision},
year={2022}
}
Acknowledgements
This repository borrows from LPIPS, Anti-aliasedCNNs, and CNNsWithoutBorders. We thank the authors of these repositories for their incredible work and inspiration.
