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102 lines
4.6 KiB
Markdown
102 lines
4.6 KiB
Markdown
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---
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typora-copy-images-to: images
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comments: true
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---
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# SAST
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## 1. 算法简介
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论文信息：
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> [A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning](https://arxiv.org/abs/1908.05498)
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> Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming
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> ACM MM, 2019
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在ICDAR2015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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|SAST|ResNet50_vd|[configs/det/det_r50_vd_sast_icdar15.yml](../../configs/det/det_r50_vd_sast_icdar15.yml)|91.39%|83.77%|87.42%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
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在Total-text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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|SAST|ResNet50_vd|[configs/det/det_r50_vd_sast_totaltext.yml](../../configs/det/det_r50_vd_sast_totaltext.yml)|89.63%|78.44%|83.66%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
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## 2. 环境配置
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请先参考[《运行环境准备》](../../ppocr/environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](../../ppocr/blog/clone.md)克隆项目代码。
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## 3. 模型训练、评估、预测
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请参考[文本检测训练教程](../../ppocr/model_train/detection.md)。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
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### 4.1 Python推理
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#### (1). 四边形文本检测模型（ICDAR2015）
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首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar))，可以使用如下命令进行转换：
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```bash linenums="1"
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python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_sast_ic15
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```
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**SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`**，可以执行如下命令：
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```bash linenums="1"
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python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_sast_ic15/"
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```
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可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
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#### (2). 弯曲文本检测模型（Total-Text）
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首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在Total-Text英文数据集训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar))，可以使用如下命令进行转换：
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```bash linenums="1"
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python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_sast_tt
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```
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SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`，同时，还需要增加参数`--det_box_type=poly`，可以执行如下命令：
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```bash linenums="1"
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python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_box_type='poly'
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```
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可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
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**注意**：本代码库中，SAST后处理Locality-Aware NMS有python和c++两种版本，c++版速度明显快于python版。由于c++版本nms编译版本问题，只有python3.5环境下会调用c++版nms，其他情况将调用python版nms。
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### 4.2 C++推理
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暂未支持
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### 4.3 Serving服务化部署
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暂未支持
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### 4.4 更多推理部署
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暂未支持
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## 5. FAQ
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## 引用
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```bibtex
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@inproceedings{wang2019single,
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title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
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author={Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming},
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booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
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pages={1277--1285},
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year={2019}
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}
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```
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