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 formatting
You 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
done
At 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 0
mkdir logs
# all datasets
bash scripts/benchmark/benchmark_loda_eval_all.sh 0
If 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.