News
- PSENet is included in MMOCR.
- We have upgraded PSENet from python2 to python3. You can find the old version here.
- We have implemented PSENet using Paddle. Visit it here.
- You can find code of PAN here.
- Another group also implemented PSENet using Paddle. You can visit it here. You can also have a try online with all the environment ready here.
Introduction
Official Pytorch 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+
Pytorch 1.1.0
torchvision 0.3
mmcv 0.2.12
editdistance
Polygon3
pyclipper
opencv-python 3.4.2.17
CythonInstall
pip install -r requirement.txt
./compile.shTraining
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py ${CONFIG_FILE}For example:
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py config/psenet/psenet_r50_ic15_736.pyTest
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.pth.tarSpeed
python test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --report_speedFor example:
python test.py config/psenet/psenet_r50_ic15_736.py checkpoints/psenet_r50_ic15_736/checkpoint.pth.tar --report_speedEvaluation
Introduction
The evaluation scripts of ICDAR 2015 (IC15), Total-Text (TT) and CTW1500 (CTW) datasets.
ICDAR 2015
Text detection
./eval_ic15.shTotal-Text
Text detection
./eval_tt.shCTW1500
Text detection
./eval_ctw.shBenchmark
Results
| Method | Backbone | Fine-tuning | Scale | Config | Precision (%) | Recall (%) | F-measure (%) | Model |
|---|---|---|---|---|---|---|---|---|
| PSENet | ResNet50 | N | Shorter Side: 736 | psenet_r50_ic15_736.py | 83.6 | 74.0 | 78.5 | Releases |
| PSENet | ResNet50 | N | Shorter Side: 1024 | psenet_r50_ic15_1024.py | 84.4 | 76.3 | 80.2 | Releases |
| PSENet (paper) | ResNet50 | N | Longer Side: 2240 | - | 81.5 | 79.7 | 80.6 | - |
| PSENet | ResNet50 | Y | Shorter Side: 736 | psenet_r50_ic15_736_finetune.py | 85.3 | 76.8 | 80.9 | Releases |
| PSENet | ResNet50 | Y | Shorter Side: 1024 | psenet_r50_ic15_1024_finetune.py | 86.2 | 79.4 | 82.7 | Releases |
| PSENet (paper) | ResNet50 | Y | Longer Side: 2240 | - | 86.9 | 84.5 | 85.7 | - |
| Method | Backbone | Fine-tuning | Config | Precision (%) | Recall (%) | F-measure (%) | Model |
|---|---|---|---|---|---|---|---|
| PSENet | ResNet50 | N | psenet_r50_ctw.py | 82.6 | 76.4 | 79.4 | Releases |
| PSENet (paper) | ResNet50 | N | - | 80.6 | 75.6 | 78 | - |
| PSENet | ResNet50 | Y | psenet_r50_ctw_finetune.py | 84.5 | 79.2 | 81.8 | Releases |
| PSENet (paper) | ResNet50 | Y | - | 84.8 | 79.7 | 82.2 | - |
| Method | Backbone | Fine-tuning | Config | Precision (%) | Recall (%) | F-measure (%) | Model |
|---|---|---|---|---|---|---|---|
| PSENet | ResNet50 | N | psenet_r50_tt.py | 87.3 | 77.9 | 82.3 | Releases |
| PSENet (paper) | ResNet50 | N | - | 81.8 | 75.1 | 78.3 | - |
| PSENet | ResNet50 | Y | psenet_r50_tt_finetune.py | 89.3 | 79.6 | 84.2 | Releases |
| PSENet (paper) | ResNet50 | Y | - | 84.0 | 78.0 | 80.9 | - |
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.