E-commercial-dataset

Dataset of electronic commercial image used for saliency.

The dataset can be downloaded in https://www.dropbox.com/s/xsui782oy3kvjsm/E-commercial%20dataset.zip?dl=0.

IMAGE

Original images are saved in this path as *.jpg

FIXATION

Fixation maps are saved as *_fixPts.jpg, while saliency maps are saved as *_.fixMap.jpg.

TEXT REGION

The text detection results are stored in csv file, with the affinity score and region score.

SSwin-transformer Model added in Repo

To-do list

Adding environment setting (you can use environment same as swin-transformer as temporary alternatives)
Refine the code into efficient way

Environment preparing

Clone this repo:

git clone https://github.com/leafy-lee/E-commercial-dataset.git
cd e-commercial

Create a conda virtual environment and activate it:

conda create -n ecom python=3.7 -y
conda activate ecom

Install CUDA>=10.2 with cudnn>=7 following the official installation instructions
Install PyTorch>=1.8.0 and torchvision>=0.9.0 with CUDA>=10.2:

conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.2 -c pytorch

Install timm==0.4.12:

pip install timm==0.4.12

Install other requirements:

pip install opencv-python==4.4.0.46 termcolor==1.1.0 yacs==0.1.8 pyyaml scipy

Evaluation

To evaluate a trained model, run:

python main.py --eval --cfg config --resume ckpt --data-path data_dir

Citation

If you use this code, please cite

@InProceedings{Jiang_2022_CVPR,
    author    = {Jiang, Lai and Li, Yifei and Li, Shengxi and Xu, Mai and Lei, Se and Guo, Yichen and Huang, Bo},
    title     = {Does Text Attract Attention on E-Commerce Images: A Novel Saliency Prediction Dataset and Method},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {2088-2097}
}

leafy-lee / E-commercial-dataset