Official repository for CVPR2023 paper: "Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method"
- Clone the repository
git clone https://github.com/Dreemurr-T/BAID.git
cd BAID/
- Install the necessary dependencies using:
pip install pandas
pip install tqdm
- Download the dataset using:
python downloading_script/download.py
The images will be saved to images/ folder.
Since it might be slow when downloading the images, we provide alternatives to obtain the dataset:
- Baidu Netdisk: Link, Code: 9y91
- Google Drive: Coming soon
Ground-truth labels of the dataset can be found in the dataset folder.
- Python >= 3.8
- Pytorch >= 1.12.0
- Torchvision >= 0.13.0
Other dependencies can be installed with:
pip install -r requirements.txt
- Download the BAID dataset and place the images in the
images/folder - Preprocess the data using:
python pretraining_utils/pretrain_mani.py
- Pretrain the ResNet50 backbone using:
python pretraining.py
The whole pretraining process takes about 2 days on a single RTX3090. We provide our pretrained weights at Drive.
For training on BAID, use:
python train.py
Checkpoints will be save to checkpoint/SAAN folder.
For testing on BAID, download the pretrained weights from Drive, place the checkpoint in checkpoint/BAID
Then use:
python test.py
The dataset is licensed under CC BY-NC-ND 4.0
The code borrowed from pytorch-AdaIN and Non-local_pytorch.
If you find our work useful, please cite our work as:
@InProceedings{Yi_2023_CVPR,
author = {Yi, Ran and Tian, Haoyuan and Gu, Zhihao and Lai, Yu-Kun and Rosin, Paul L.},
title = {Towards Artistic Image Aesthetics Assessment: A Large-Scale Dataset and a New Method},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {22388-22397}
}