提交 6941fa57 编写于 作者: z37757's avatar z37757

添加drrg

上级 67ae525e
# DRRG
- [1. 算法简介](#1-算法简介)
- [2. 环境配置](#2-环境配置)
- [3. 模型训练、评估、预测](#3-模型训练评估预测)
- [4. 推理部署](#4-推理部署)
- [4.1 Python推理](#41-python推理)
- [4.2 C++推理](#42-c推理)
- [4.3 Serving服务化部署](#43-serving服务化部署)
- [4.4 更多推理部署](#44-更多推理部署)
- [5. FAQ](#5-faq)
- [引用](#引用)
<a name="1"></a>
## 1. 算法简介
论文信息:
> [Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection](https://arxiv.org/abs/2003.07493)
> Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Liu, Chang and Yang, Chun and Wang, Hongfa and Yin, Xu-Cheng
> CVPR, 2020
在CTW1500文本检测公开数据集上,算法复现效果如下:
| 模型 |骨干网络|配置文件|precision|recall|Hmean|下载链接|
|-----| --- | --- | --- | --- | --- | --- |
| 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](drrg_model)|
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## 2. 环境配置
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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## 3. 模型训练、评估、预测
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)
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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## 4. 推理部署
<a name="4-1"></a>
### 4.1 Python推理
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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### 4.2 C++推理
暂未支持
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### 4.3 Serving服务化部署
暂未支持
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### 4.4 更多推理部署
暂未支持
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## 5. FAQ
## 引用
```bibtex
@inproceedings{zhang2020deep,
title={Deep relational reasoning graph network for arbitrary shape text detection},
author={Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Liu, Chang and Yang, Chun and Wang, Hongfa and Yin, Xu-Cheng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={9699--9708},
year={2020}
}
```
......@@ -29,6 +29,7 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型,**欢迎广
- [x] [SAST](./algorithm_det_sast.md)
- [x] [PSENet](./algorithm_det_psenet.md)
- [x] [FCENet](./algorithm_det_fcenet.md)
- [x] [DRRG](./algorithm_det_drrg.md)
在ICDAR2015文本检测公开数据集上,算法效果如下:
......@@ -54,6 +55,7 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型,**欢迎广
|模型|骨干网络|precision|recall|Hmean|下载链接|
| --- | --- | --- | --- | --- | --- |
|FCE|ResNet50_dcn|88.39%|82.18%|85.27%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar)|
|DRRG|ResNet50_vd|89.92%|80.91%|85.18%|[训练模型](drrg_model)|
**说明:** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载:
* [百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
......
# DRRG
- [1. Introduction](#1)
- [2. Environment](#2)
- [3. Model Training / Evaluation / Prediction](#3)
- [3.1 Training](#3-1)
- [3.2 Evaluation](#3-2)
- [3.3 Prediction](#3-3)
- [4. Inference and Deployment](#4)
- [4.1 Python Inference](#4-1)
- [4.2 C++ Inference](#4-2)
- [4.3 Serving](#4-3)
- [4.4 More](#4-4)
- [5. FAQ](#5)
<a name="1"></a>
## 1. Introduction
Paper:
> [Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection](https://arxiv.org/abs/2003.07493)
> Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Liu, Chang and Yang, Chun and Wang, Hongfa and Yin, Xu-Cheng
> CVPR, 2020
On the CTW1500 dataset, the text detection result is as follows:
|Model|Backbone|Configuration|Precision|Recall|Hmean|Download|
| --- | --- | --- | --- | --- | --- | --- |
| DRRG | ResNet50_vd | [configs/det/det_r50_drrg_ctw.yml](../../configs/det/det_r50_drrg_ctw.yml)| 89.92%|80.91%|85.18%|[trained model](drrg_model)|
<a name="2"></a>
## 2. Environment
Please prepare your environment referring to [prepare the environment](./environment_en.md) and [clone the repo](./clone_en.md).
<a name="3"></a>
## 3. Model Training / Evaluation / Prediction
The above DRRG model is trained using the CTW1500 text detection public dataset. For the download of the dataset, please refer to [ocr_datasets](./dataset/ocr_datasets_en.md).
After the data download is complete, please refer to [Text Detection Training Tutorial](./detection_en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models.
<a name="4"></a>
## 4. Inference and Deployment
<a name="4-1"></a>
### 4.1 Python Inference
Since the model needs to be converted to Numpy data for many times in the forward, DRRG dynamic graph to static graph is not supported.
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### 4.2 C++ Inference
Not supported
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### 4.3 Serving
Not supported
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### 4.4 More
Not supported
<a name="5"></a>
## 5. FAQ
## Citation
```bibtex
@inproceedings{zhang2020deep,
title={Deep relational reasoning graph network for arbitrary shape text detection},
author={Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Liu, Chang and Yang, Chun and Wang, Hongfa and Yin, Xu-Cheng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={9699--9708},
year={2020}
}
```
......@@ -27,6 +27,7 @@ Supported text detection algorithms (Click the link to get the tutorial):
- [x] [SAST](./algorithm_det_sast_en.md)
- [x] [PSENet](./algorithm_det_psenet_en.md)
- [x] [FCENet](./algorithm_det_fcenet_en.md)
- [x] [DRRG](./algorithm_det_drrg_en.md)
On the ICDAR2015 dataset, the text detection result is as follows:
......@@ -52,6 +53,7 @@ On CTW1500 dataset, the text detection result is as follows:
|Model|Backbone|Precision|Recall|Hmean| Download link|
| --- | --- | --- | --- | --- |---|
|FCE|ResNet50_dcn|88.39%|82.18%|85.27%| [trained model](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar) |
|DRRG|ResNet50_vd|89.92%|80.91%|85.18%|[trained model](drrg_model)|
**Note:** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from:
* [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi).
......
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