mmocr/configs/textdet/panet/README.md

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# 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 |
| :----------------------------------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :----: | :-------: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [PANet](/configs/textdet/panet/panet_r18_fpem_ffm_600e_ctw1500.py) | ImageNet | CTW1500 Train | CTW1500 Test | 600 | 640 | 0.776 | 0.838 | 0.806 | [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) |
### ICDAR2015
| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
| :------------------------------------------------------------------: | :--------------: | :-------------: | :------------: | :-----: | :-------: | :----: | :-------: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [PANet](/configs/textdet/panet/panet_r18_fpem_ffm_600e_icdar2015.py) | ImageNet | ICDAR2015 Train | ICDAR2015 Test | 600 | 736 | 0.734 | 0.856 | 0.791 | [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) |