| --- |
| license: apache-2.0 |
| tags: |
| - scene text erase |
| - poster text erase |
| --- |
| |
| # Self-supervised Text Erasing Model (STE) |
| Paper: [https://arxiv.org/abs/2204.12743](https://arxiv.org/abs/2204.12743)<br/> |
| Project Page: [https://github.com/alimama-creative/Self-supervised-Text-Erasing](https://github.com/alimama-creative/Self-supervised-Text-Erasing)<br/> |
|
|
| ## 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. |
| ```bash |
| 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. |
|
|
| ```bash |
| 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 |
| |
| ``` |
|
|
|
|
