RoseSakurai / PSENet_paddle

News

• PSENet is included in MMOCR.
• We have implemented PSENet using Paddle. You can find the pytorch version here.
• You can find code of PAN here.
• Another group did the same job. You can visit it here. You can also have a try online with all the environment ready here.

Introduction

Official Paddle implementations of PSENet [1].

[1] W. Wang, E. Xie, X. Li, W. Hou, T. Lu, G. Yu, and S. Shao. Shape robust text detection with progressive scale expansion network. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 9336–9345, 2019.

Recommended environment

Python 3.6+
paddlepaddle-gpu 2.0.2
nccl 2.0+
mmcv 0.2.12
editdistance
Polygon3
pyclipper
opencv-python 3.4.2.17
Cython

Install

Install paddle following the official tutorial.

pip install -r requirement.txt
./compile.sh

Training

CUDA_VISIBLE_DEVICES=0,1,2,3 python dist_train.py ${CONFIG_FILE}

For example:

CUDA_VISIBLE_DEVICES=0,1,2,3 python dist_train.py config/psenet/psenet_r50_ic15_736.py

Test

python test.py ${CONFIG_FILE} ${CHECKPOINT_FILE}

For example:

python test.py config/psenet/psenet_r50_ic15_736.py checkpoints/psenet_r50_ic15_736/checkpoint.pdparams

Evaluation

Introduction

The evaluation scripts of ICDAR 2015 (IC15) dataset.

ICDAR 2015

Text detection

./eval_ic15.sh

Benchmark

Results

ICDAR 2015

Method	Backbone	Fine-tuning	Scale	Config	Precision (%)	Recall (%)	F-measure (%)	Model
PSENet	ResNet50	N	Shorter Side: 736	psenet_r50_ic15_736.py	82.2	79.4	80.7	Google Drive

Citation

@inproceedings{wang2019shape,
  title={Shape robust text detection with progressive scale expansion network},
  author={Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={9336--9345},
  year={2019}
}

License

This project is developed and maintained by IMAGINE Lab@National Key Laboratory for Novel Software Technology, Nanjing University.

This project is released under the Apache 2.0 license.