oCLIP

This repository is the official implementation for the following paper:

Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting

Chuhui Xue, Wenqing Zhang, Yu Hao, Shijian Lu, Philip Torr, Song Bai, ECCV 2022 (Oral)

Part of the code is inherited from open_clip.

Models

• English

Backbone	Pre-train Data	Pre-train Model	Fine-tune Data	Fine-tune Model (PSENet)	Precision	Recall	F-score
ResNet-50	SynthText	Link	Total-Text	Link	89.9	81.6	85.5
ResNet-101	SynthText	Link	Total-Text	Link	89.9	82.2	85.9
ResNet-50	Web Image	Link	Total-Text	Link	90.1	83.5	86.7

• Chinese

Backbone	Pre-train Data	Pre-train Model
ResNet-50	LSVT-Weak Annotation	Link

Training oCLIP

Conda

conda create -n oclip python=3.7
conda activate oclip
pip install -r requirement.txt

git clone https://github.com/bytedance/oclip.git
cd oclip
export PYTHONPATH="$PYTHONPATH:$PWD/src"

Data

Download SynthText and put it to ./data.

You may use the provided script to generate the annotation for pre-training:

python tools/convert_synthtext_csv.py --data_dir data/SynthText/ --save_dir data/SynthText/

• Note we use [space] to represent the masked characters. For customized datasets, you may modify codes in src/training/data.py and your data annotation accordingly.

Train

Sample running code for training:

CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -u src/training/main.py \
    --save-frequency 3 \
    --report-to tensorboard \
    --train-data="data/SynthText/train_char.csv"  \
    --char-dict-pth="data/SynthText/char_dict" \
    --csv-img-key filepath \
    --csv-caption-key title \
    --warmup 10000 \
    --batch-size=64 \
    --lr=1e-4\
    --wd=0.1 \
    --epochs=100 \
    --workers=8 \
    --model RN50 \
    --logs='output/RN50_synthtext' 

Visualization

We also provide a script for visualization of attention maps in the pre-trained model.

Download the pre-trained model to ./pretrained.

python3 tools/visualize_attn.py --model_path pretrained/RN50_synthtext.pt --char_dict_path data/SynthText/char_dict --model_config_file src/training/model_configs/RN50.json --im_fn demo/sample.jpg --text_list "ST LING" "STRLIN " "A GYLL'S" " ODGINGS" --demo_path demo/

Input Image	Image Attenion Map	"ST LING"	"STRLIN "	"A GYLL'S"	" ODGINGS"

Fine-tune in MMOCR

We provide a script for converting model parameter names, thus it could be used in the dev-1.x branch of MMOCR

# first modify the model_path and save_path in tools/convert2mmocr.py
python tools/convert2mmocr.py

Citation

@article{xue2022language,
  title={Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting},
  author={Xue, Chuhui and Zhang, Wenqing and Hao, Yu and Lu, Shijian and Torr, Philip and Bai, Song},
  journal={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2022}
}