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--- |
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license: apache-2.0 |
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tags: |
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- scene text erase |
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- poster text erase |
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--- |
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# Self-supervised Text Erasing Model (STE) |
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Paper: [https://arxiv.org/abs/2204.12743](https://arxiv.org/abs/2204.12743)<br/> |
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Project Page: [https://github.com/alimama-creative/Self-supervised-Text-Erasing](https://github.com/alimama-creative/Self-supervised-Text-Erasing)<br/> |
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## Description |
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The checkpoints are trained from the posterErase dataset. There are two versions with different training mechanism. |
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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' |
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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' |
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## Usage |
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First, download the github project and install the python package. |
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```bash |
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git clone https://github.com/alimama-creative/Self-supervised-Text-Erasing.git |
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pip install -r requirements.txt |
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``` |
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Then, follow the command line provied in the github to run the inference code. |
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```bash |
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python test.py --dataset_mode items --dataroot ./examples/poster --model erasenet --name ft --which_epoch best # inferece with the ste model on poster |
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python test.py --dataset_mode items --dataroot ./examples/poster --model erasenet --name ste --which_epoch best # inferece with the finetuned model model on poster |
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``` |
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