Awesome
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
- -[x] 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
withcudnn>=7
following the official installation instructions - Install
PyTorch>=1.8.0
andtorchvision>=0.9.0
withCUDA>=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 train the model, run:
python train.py --batch-size 8 --cfg configs/sswin.yaml --data-path DATA/ECdata/ --dataset ecdata --head headname
Evaluation
To evaluate a trained model, run:
python main.py --eval --cfg config --resume True --finetune 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}
}