Single Tube Text Spotter Enhancing Spatial Relationship in Chinese Scene Text Images

Usage

• Installation

Python 3.8 + PyTorch 1.9.0 + CUDA 11.1 + Detectron2 (v0.6)

git clone https://github.com/unxiaohao/STTS.git
cd STTS
conda create -n stts python=3.8 -y
conda activate stts
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt
python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.9/index.html
python setup.py build develop

• Preparation

Datasets

• [SynChinese130K] images | annotations

• [ArT] images | annotations

• [LSVT] images | annotations

• [ReCTS] images | annotations

• [Evaluation ground-truth] Link

• Training

1. Pre-train

python tools/train_net.py --config-file configs/R_50/ReCTS/pretrain.yaml --num-gpus 8

2. Fine-tune

python tools/train_net.py --config-file configs/R_50/ReCTS/finetune.yaml --num-gpus 8

• Evaluation

To evaluate ICDAR 2019 ReCTS, you can directly submit the saved file to the official website for evaluation.

Acknowledgement

This project is based on Adelaidet. For academic use, this project is licensed under the 2-clause BSD License.