mirror of https://github.com/open-mmlab/mmocr.git
36 lines
4.2 KiB
Markdown
36 lines
4.2 KiB
Markdown
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# Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network
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## Introduction
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[ALGORITHM]
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```bibtex
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@inproceedings{WangXSZWLYS19,
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author={Wenhai Wang and Enze Xie and Xiaoge Song and Yuhang Zang and Wenjia Wang and Tong Lu and Gang Yu and Chunhua Shen},
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title={Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network},
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booktitle={ICCV},
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pages={8439--8448},
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year={2019}
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}
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```
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## Results and models
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### CTW1500
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| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
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| :----------------------------------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :----: | :-------: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| [PANet](/configs/textdet/panet/panet_r18_fpem_ffm_600e_ctw1500.py) | ImageNet | CTW1500 Train | CTW1500 Test | 600 | 640 | 0.790 | 0.838 | 0.813 | [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) |
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### ICDAR2015
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| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
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| :------------------------------------------------------------------: | :--------------: | :-------------: | :------------: | :-----: | :-------: | :----: | :-------: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| [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) |
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### ICDAR2017
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| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
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| :------------------------------------------------------------------: | :--------------: | :-------------: | :-----------: | :-----: | :-------: | :----: | :-------: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| [PANet](/configs/textdet/panet/panet_r50_fpem_ffm_600e_icdar2017.py) | ImageNet | ICDAR2017 Train | ICDAR2017 Val | 600 | 800 | 0.604 | 0.812 | 0.693 | [model](https://download.openmmlab.com/mmocr/textdet/panet/panet_r50_fpem_ffm_sbn_600e_icdar2017_20210219-b4877a4f.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/panet/panet_r50_fpem_ffm_sbn_600e_icdar2017_20210219-b4877a4f.log.json) |
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