Woon-Ha Yeo, Wang-Taek Oh, Kyung-Su Kang, Young-Il Kim, Han-Cheol Ryu
- AIM 2022 Instagram Filter Removal Challenge
| Filtered Image | Original Image | CAIR* (ours) | IFRNet, CVPRW2021 | CIFR, CVPRW2022 |
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This implementation is based on NAFNet.
python 3.9.5
pytorch 1.11.0
cuda 11.3
pip install -r requirements.txt
python setup.py develop --no_cuda_ext
You can download IFFI(Instagram Filter Fashion Image) dataset on challenge website
You can train on IFFI dataset by following these steps:
Datasets directory structure
datasets
└──IFFI
└──IFFI-dataset-train
| └──0
| └──1
| └──2
| └──...
└──IFFI-dataset-lr-train
| └──0
| └──1
| └──2
| └──...
└──IFFI-dataset-lr-challenge-test-wo-gt
└──0
└──1
└──2
└──...
Your Instagram Filter Removal Challenge dataset to lmdb format. If you want to use ensemble learning, set --ensemble true.
python scripts/data/dataset_to_lmdb.py --basedir ./datasets/IFFI --ensemble false
python scripts/data/dataset_to_lmdb.py --basedir ./datasets/IFFI --ensemble true
Result data into submission format
python scripts/data/test_dataset.py --resultdir ./results/model_name
If you want to train/test other model, replace option file with others in options/ folder.
- Train
python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/train/CAIR/CAIR_M-width32.yml --launcher pytorch
- Test
# General test
python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/test.py -opt options/test/CAIR/CAIR_M-width32.yml --launcher pytorch
# TTA
python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/test.py -opt options/test/CAIR-TTA/CAIR_M-width32.yml --launcher pytorch
-
Data preparation
For ensemble learning, train and test set should be inferenced with three models, and then three images from three models should be contatenated.
# CAIR_M-width32
python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/test.py -opt ./options/test/Ensemble/CAIR_M-width32.yml --launcher pytorch
# CAIR_S-width32
python -m torch.distributed.launch --nproc_per_node=4 --master_port=4319 basicsr/test.py -opt ./options/test/Ensemble/CAIR_S-width32.yml --launcher pytorch
python scripts/data/concat_ensemble_input.py
- Train ensemble network
python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/train/Ensemble/CAIR_Ensemble.yml --launcher pytorch
- Test ensemble network
python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/test.py -opt options/test/Ensemble/CAIR_Ensemble.yml --launcher pytorch
| name | PSNR | SSIM | pretrained models |
|---|---|---|---|
| CAIR-S | 33.87 | 0.970 | Synology drive |
| CAIR-M | 34.39 | 0.971 | Synology drive |
| CAIR-Ensemble(CAIR*) | 34.42 | 0.972 | Synology drive |
@article{yeo2022cair,
title={CAIR: Fast and Lightweight Multi-Scale Color Attention Network for Instagram Filter Removal},
author={Yeo, Woon-Ha and Oh, Wang-Taek and Kang, Kyung-Su and Kim, Young-Il and Ryu, Han-Cheol},
journal={arXiv preprint arXiv:2208.14039},
year={2022}
}
If you have any question, please contact [email protected]