2022-10-10 15:52:30 +08:00
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# DRRG
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- [1. 算法简介](#1-算法简介)
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- [2. 环境配置](#2-环境配置)
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- [3. 模型训练、评估、预测](#3-模型训练评估预测)
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- [4. 推理部署](#4-推理部署)
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- [4.1 Python推理](#41-python推理)
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- [4.2 C++推理](#42-c推理)
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- [4.3 Serving服务化部署](#43-serving服务化部署)
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- [4.4 更多推理部署](#44-更多推理部署)
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- [5. FAQ](#5-faq)
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- [引用](#引用)
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<a name="1"></a>
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## 1. 算法简介
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论文信息：
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> [Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection](https://arxiv.org/abs/2003.07493)
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> Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Liu, Chang and Yang, Chun and Wang, Hongfa and Yin, Xu-Cheng
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> CVPR, 2020
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在CTW1500文本检测公开数据集上，算法复现效果如下：
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| 模型 |骨干网络|配置文件|precision|recall|Hmean|下载链接|
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|-----| --- | --- | --- | --- | --- | --- |
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2022-10-20 17:05:53 +08:00
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| DRRG | ResNet50_vd | [configs/det/det_r50_drrg_ctw.yml](../../configs/det/det_r50_drrg_ctw.yml)| 89.92%|80.91%|85.18%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/det_r50_drrg_ctw_train.tar)|
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2022-10-10 15:52:30 +08:00
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<a name="2"></a>
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## 2. 环境配置
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请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。
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<a name="3"></a>
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## 3. 模型训练、评估、预测
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上述DRRG模型使用CTW1500文本检测公开数据集训练得到，数据集下载可参考 [ocr_datasets](./dataset/ocr_datasets.md)。
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数据下载完成后，请参考[文本检测训练教程](./detection.md)进行训练。PaddleOCR对代码进行了模块化，训练不同的检测模型只需要**更换配置文件**即可。
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<a name="4"></a>
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## 4. 推理部署
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<a name="4-1"></a>
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### 4.1 Python推理
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由于模型前向运行时需要多次转换为Numpy数据进行运算，因此DRRG的动态图转静态图暂未支持。
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<a name="4-2"></a>
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### 4.2 C++推理
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暂未支持
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<a name="4-3"></a>
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### 4.3 Serving服务化部署
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暂未支持
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<a name="4-4"></a>
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### 4.4 更多推理部署
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暂未支持
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<a name="5"></a>
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## 5. FAQ
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## 引用
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```bibtex
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@inproceedings{zhang2020deep,
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title={Deep relational reasoning graph network for arbitrary shape text detection},
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author={Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Liu, Chang and Yang, Chun and Wang, Hongfa and Yin, Xu-Cheng},
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
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pages={9699--9708},
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year={2020}
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}
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```
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