2021-04-03 01:03:52 +08:00
# Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network
## Introduction
[ALGORITHM]
```bibtex
@inproceedings {WangXSZWLYS19,
author={Wenhai Wang and Enze Xie and Xiaoge Song and Yuhang Zang and Wenjia Wang and Tong Lu and Gang Yu and Chunhua Shen},
title={Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network},
booktitle={ICCV},
pages={8439--8448},
year={2019}
}
```
## Results and models
### CTW1500
| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
| :----------------------------------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :----: | :-------: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
2021-09-28 10:57:12 +08:00
| [PANet ](configs/textdet/panet/panet_r18_fpem_ffm_600e_ctw1500.py ) | ImageNet | CTW1500 Train | CTW1500 Test | 600 | 640 | 0.776 (0.717) | 0.838 (0.835) | 0.806 (0.801) | [model ](https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm_sbn_600e_ctw1500_20210219-3b3a9aa3.pth ) \| [log ](https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm_sbn_600e_ctw1500_20210219-3b3a9aa3.log.json ) |
2021-04-03 01:03:52 +08:00
### ICDAR2015
| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
| :------------------------------------------------------------------: | :--------------: | :-------------: | :------------: | :-----: | :-------: | :----: | :-------: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
2021-09-28 10:57:12 +08:00
| [PANet ](configs/textdet/panet/panet_r18_fpem_ffm_600e_icdar2015.py ) | ImageNet | ICDAR2015 Train | ICDAR2015 Test | 600 | 736 | 0.734 (0.74) | 0.856 (0.86) | 0.791 (0.795) | [model ](https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm_sbn_600e_icdar2015_20210219-42dbe46a.pth ) \| [log ](https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm_sbn_600e_icdar2015_20210219-42dbe46a.log.json ) |
**Note:** We've upgraded our IoU backend from `Polygon3` to `shapely` . There are some performance differences for some models due to the backends' different logics to handle invalid polygons (more info [here ](https://github.com/open-mmlab/mmocr/issues/465 )). **New evaluation result is presented in brackets** and new logs will be uploaded soon.
