This is the official repository of the paper "Boosting Image Quality Assessment through Efficient Transformer Adaptation with Local Feature Enhancement".
- [06/2024] We released the source code of 'LoDa', check the code on GitHub
- Checkpoints & Logs
- Initialization
We recommend using the conda package manager to avoid dependency problems.
- Clone the repository
git clone https://github.com/NeosXu/LoDa- Install Python dependencies
# Using conda (Recommend)
conda env create -f environment.yaml
conda activate loda
# Using pip
pip install -r requirements.txt
pip install -r requirements-dev.txt # Optional, for code formatting
pre-commit install # Optional, for code formattingYou need to download the corresponding datasets in the paper and place them under the same directory data.
For each dataset, run the corresponding preprocess script to process the image, metadata and train/test split of the datasets.
dataset_names=("live" "tid2013" "kadid10k" "livechallenge" "koniq10k" "spaq" "flive")
for dn in "${dataset_names[@]}"
do
python scripts/process_"$dn".py
doneAt the end, the directory structure should look like this:
├── data
| ├── flive
| ├── kadid10k
| ├── koniq10k
| ├── live_iqa
| ├── LIVEC
| ├── spaq
| ├── tid2013
| ├── meta_info
| | ├── meta_info_FLIVEDataset.csv
| | ├── meta_info_KADID10kDataset.csv
| | ├── meta_info_KonIQ10kDataset.csv
| | ├── ...
| ├── train_split_info
| | ├── flive_82_seed3407.pkl
| | ├── kadid10k_82_seed3407.pkl
| | ├── koniq10k_82_seed3407.pkl
| | ├── ...
Or you can simply download the meta_info and train_split_info from Google Drive.
mkdir logs
# all datasets
bash scripts/benchmark/benchmark_loda_all.sh 0
# a single dataset
bash scripts/benchmark/benchmark_loda_koniq10k.sh 0mkdir logs
# all datasets
bash scripts/benchmark/benchmark_loda_eval_all.sh 0If you find this project helpful in your research, please consider citing our papers:
@InProceedings{Xu_2024_CVPR,
author = {Xu, Kangmin and Liao, Liang and Xiao, Jing and Chen, Chaofeng and Wu, Haoning and Yan, Qiong and Lin, Weisi},
title = {Boosting Image Quality Assessment through Efficient Transformer Adaptation with Local Feature Enhancement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2024},
pages = {2662-2672}
}
We borrowed some parts from the following open-source projects:
Many thanks to them.