Self-supervised Text Erasing Model (STE)
Paper: https://arxiv.org/abs/2204.12743
Project Page: https://github.com/alimama-creative/Self-supervised-Text-Erasing
Description
The checkpoints are trained from the posterErase dataset. There are two versions with different training mechanism.
Self-supervised Text Trasing (ste_best_net_G.pth): To use it, please download from this page, and put it under './checkpoints/erasenet/ste/best_net_G.pth'
Finetuning after STE (ft_best_net_G.pth): To use it, please download from this page, and put it under './checkpoints/erasenet/ste/best_net_G.pth'
Usage
First, download the github project and install the python package.
git clone https://github.com/alimama-creative/Self-supervised-Text-Erasing.git
pip install -r requirements.txt
Then, follow the command line provied in the github to run the inference code.
python test.py --dataset_mode items --dataroot ./examples/poster --model erasenet --name ft --which_epoch best # inferece with the ste model on poster
python test.py --dataset_mode items --dataroot ./examples/poster --model erasenet --name ste --which_epoch best # inferece with the finetuned model model on poster
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The model has no library tag.