diff --git a/doc/doc_ch/algorithm_det_sast.md b/doc/doc_ch/algorithm_det_sast.md index eafb16801aa0bf1a6c070842bd1459732ee80a73..038d73fc15f3203bbcc17997c1a8e1c208f80ba8 100644 --- a/doc/doc_ch/algorithm_det_sast.md +++ b/doc/doc_ch/algorithm_det_sast.md @@ -8,6 +8,9 @@ - [3.3 预测](#3-3) - [4. 推理部署](#4) - [4.1 Python推理](#4-1) + - [4.2 C++推理](#4-2) + - [4.3 Serving服务化部署](#4-3) + - [4.4 更多推理部署](#4-4) - [5. FAQ](#5) @@ -48,24 +51,52 @@ ### 4.1 Python推理 -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： +#### (1). 四边形文本检测模型（ICDAR2015） +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： +``` +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 -```shell -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 ``` +**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： +``` +python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_sast_ic15/" +``` +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： -SAST文本检测模型推理，可以执行如下命令： +![](../imgs_results/det_res_img_10_sast.jpg) + +#### (2). 弯曲文本检测模型（Total-Text） +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： + +``` +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 -```shell -python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_sast/" ``` +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_sast_polygon=True`，可以执行如下命令： +``` +python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_sast_polygon=True +``` 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： -![](../imgs_results/det_res_img_10_sast.jpg) +![](../imgs_results/det_res_img623_sast.jpg) + +**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。 + + +### 4.2 C++推理 + +暂未支持 + + +### 4.3 Serving服务化部署 + +暂未支持 -**注意**：由于ICDAR2015数据集只有1000张训练图像，且主要针对英文场景，所以上述模型对中文文本图像检测效果会比较差。 + +### 4.4 更多推理部署 +暂未支持 ## 5. FAQ diff --git a/doc/doc_en/algorithm_det_sast_en.md b/doc/doc_en/algorithm_det_sast_en.md index 70fa51dafa8e7e8b2f9a76c5434933a66165e5f3..e3437d22be9d75835aaa43e72363b498225db9e1 100644 --- a/doc/doc_en/algorithm_det_sast_en.md +++ b/doc/doc_en/algorithm_det_sast_en.md @@ -8,6 +8,9 @@ - [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) @@ -47,24 +50,56 @@ Please refer to [text detection training tutorial](./detection_en.md). PaddleOCR ### 4.1 Python Inference -First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)), you can use the following command to convert: +#### (1). Quadrangle text detection model (ICDAR2015) +First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)), you can use the following command to convert: -```shell -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 +``` +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 ``` -SAST text detection model inference, you can execute the following command: +**For SAST quadrangle text detection model inference, you need to set the parameter `--det_algorithm="SAST"`**, run the following command: -```shell -python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_sast/" +``` +python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_sast_ic15/" ``` The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows: ![](../imgs_results/det_res_img_10_sast.jpg) -**Note**: Since the ICDAR2015 dataset has only 1,000 training images, mainly for English scenes, the above model has very poor detection result on Chinese text images. +#### (2). Curved text detection model (Total-Text) +First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)), you can use the following command to convert: + +``` +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 +``` + +For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`, run the following command: + +``` +python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_sast_polygon=True +``` + +The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows: + +![](../imgs_results/det_res_img623_sast.jpg) + +**Note**: SAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases. + + +### 4.2 C++ Inference + +Not supported + + +### 4.3 Serving + +Not supported + + +### 4.4 More +Not supported ## 5. FAQ