From c48e6ddfa97adab28005fcd50ef9e1227ea4e7cd Mon Sep 17 00:00:00 2001 From: huangjun12 <12272008@bjtu.edu.cn> Date: Thu, 28 Apr 2022 06:36:40 +0000 Subject: [PATCH 1/4] add sast doc --- doc/doc_en/algorithm_det_sast_en.md | 85 +++++++++++++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 doc/doc_en/algorithm_det_sast_en.md diff --git a/doc/doc_en/algorithm_det_sast_en.md b/doc/doc_en/algorithm_det_sast_en.md new file mode 100644 index 0000000000..b302e32948 --- /dev/null +++ b/doc/doc_en/algorithm_det_sast_en.md @@ -0,0 +1,85 @@ +# SAST + +- [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) +- [5. FAQ](#5) + + +## 1. Introduction + +Paper: +> [A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning](https://arxiv.org/abs/1908.05498) +> 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 +> ACM MM, 2019 + +On the ICDAR2015 dataset, the text detection result is as follows: + +|Model|Backbone|Configuration|Precision|Recall|Hmean|Download| +| --- | --- | --- | --- | --- | --- | --- | +|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)| + + +On the Total-text dataset, the text detection result is as follows: + +|Model|Backbone|Configuration|Precision|Recall|Hmean|Download| +| --- | --- | --- | --- | --- | --- | --- | +|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)| + + + +## 2. Environment +Please prepare your environment referring to [prepare the environment](./environment_en.md) and [clone the repo](./clone_en.md). + + + +## 3. Model Training / Evaluation / Prediction + +Please refer to [text detection training tutorial](./detection_en.md). PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models. + + +## 4. Inference and Deployment + + +### 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: + +```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 text detection model inference, you can execute the following command: + +```shell +python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_sast/" +``` + +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. + + + +## 5. FAQ + + +## Citation + +```bibtex +@inproceedings{wang2019single, + title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning}, + 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}, + booktitle={Proceedings of the 27th ACM International Conference on Multimedia}, + pages={1277--1285}, + year={2019} +} +``` From 30bc3c4396e69a7f2db48acb21291248cab91099 Mon Sep 17 00:00:00 2001 From: huangjun12 <12272008@bjtu.edu.cn> Date: Thu, 28 Apr 2022 06:39:26 +0000 Subject: [PATCH 2/4] add sast ch doc --- doc/doc_ch/algorithm_det_sast.md | 84 +++++++++++++++++++++++++++++ doc/doc_en/algorithm_det_sast_en.md | 2 - 2 files changed, 84 insertions(+), 2 deletions(-) create mode 100644 doc/doc_ch/algorithm_det_sast.md diff --git a/doc/doc_ch/algorithm_det_sast.md b/doc/doc_ch/algorithm_det_sast.md new file mode 100644 index 0000000000..eafb16801a --- /dev/null +++ b/doc/doc_ch/algorithm_det_sast.md @@ -0,0 +1,84 @@ +# SAST + +- [1. 算法简介](#1) +- [2. 环境配置](#2) +- [3. 模型训练、评估、预测](#3) + - [3.1 训练](#3-1) + - [3.2 评估](#3-2) + - [3.3 预测](#3-3) +- [4. 推理部署](#4) + - [4.1 Python推理](#4-1) +- [5. FAQ](#5) + + +## 1. 算法简介 + +论文信息： +> [A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning](https://arxiv.org/abs/1908.05498) +> 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 +> ACM MM, 2019 + +在ICDAR2015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| +| --- | --- | --- | --- | --- | --- | --- | +|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)| + + +在Total-text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| +| --- | --- | --- | --- | --- | --- | --- | +|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)| + + + +## 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。 + + + +## 3. 模型训练、评估、预测 + +请参考[文本检测训练教程](./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。 + + + +## 4. 推理部署 + + +### 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： + +```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，可以执行如下命令： + +```shell +python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_sast/" +``` + +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) + +**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。 + + + +## 5. FAQ + + +## 引用 + +```bibtex +@inproceedings{wang2019single, + title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning}, + 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}, + booktitle={Proceedings of the 27th ACM International Conference on Multimedia}, + pages={1277--1285}, + year={2019} +} +``` diff --git a/doc/doc_en/algorithm_det_sast_en.md b/doc/doc_en/algorithm_det_sast_en.md index b302e32948..e76ddd672c 100644 --- a/doc/doc_en/algorithm_det_sast_en.md +++ b/doc/doc_en/algorithm_det_sast_en.md @@ -8,8 +8,6 @@ - [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) - [5. FAQ](#5) From 8dc8f142934731be0743e661cfe20c70932c8a26 Mon Sep 17 00:00:00 2001 From: huangjun12 <12272008@bjtu.edu.cn> Date: Thu, 28 Apr 2022 08:40:57 +0000 Subject: [PATCH 3/4] refine en doc --- doc/doc_en/algorithm_det_sast_en.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/doc_en/algorithm_det_sast_en.md b/doc/doc_en/algorithm_det_sast_en.md index e76ddd672c..70fa51dafa 100644 --- a/doc/doc_en/algorithm_det_sast_en.md +++ b/doc/doc_en/algorithm_det_sast_en.md @@ -22,14 +22,14 @@ On the ICDAR2015 dataset, the text detection result is as follows: |Model|Backbone|Configuration|Precision|Recall|Hmean|Download| | --- | --- | --- | --- | --- | --- | --- | -|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)| +|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%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)| On the Total-text dataset, the text detection result is as follows: |Model|Backbone|Configuration|Precision|Recall|Hmean|Download| | --- | --- | --- | --- | --- | --- | --- | -|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)| +|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%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)| From 1fa754ee543fd57dd585e0b3bc0a5170a48b9b3a Mon Sep 17 00:00:00 2001 From: huangjun12 <12272008@bjtu.edu.cn> Date: Thu, 28 Apr 2022 11:31:51 +0000 Subject: [PATCH 4/4] fix inference doc --- doc/doc_ch/algorithm_det_sast.md | 51 +++++++++++++++++++++++------ doc/doc_en/algorithm_det_sast_en.md | 49 +++++++++++++++++++++++---- 2 files changed, 83 insertions(+), 17 deletions(-) diff --git a/doc/doc_ch/algorithm_det_sast.md b/doc/doc_ch/algorithm_det_sast.md index eafb16801a..038d73fc15 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： - -```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 +#### (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 -SAST文本检测模型推理，可以执行如下命令： - -```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"`**，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： ![](../imgs_results/det_res_img_10_sast.jpg) -**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。 +#### (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 + +``` + +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_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 + + +### 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 70fa51dafa..e3437d22be 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