diff --git a/doc/doc_ch/inference.md b/doc/doc_ch/inference.md index 0b082c568dbc609975dd406df03cf035ecf80277..f0f7401538a9f8940f671fdcc170aca6c003040d 100755 --- a/doc/doc_ch/inference.md +++ b/doc/doc_ch/inference.md @@ -13,7 +13,6 @@ inference 模型（`paddle.jit.save`保存的模型） - [检测模型转inference模型](#检测模型转inference模型) - [识别模型转inference模型](#识别模型转inference模型) - [方向分类模型转inference模型](#方向分类模型转inference模型) - - [端到端模型转inference模型](#端到端模型转inference模型) - [二、文本检测模型推理](#文本检测模型推理) - [1. 超轻量中文检测模型推理](#超轻量中文检测模型推理) @@ -119,32 +118,6 @@ python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_mo ├── inference.pdiparams.info # 分类inference模型的参数信息，可忽略 └── inference.pdmodel # 分类inference模型的program文件 ``` - -### 端到端模型转inference模型 - -下载端到端模型： -``` -wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/ -``` - -端到端模型转inference模型与检测的方式相同，如下： -``` -# -c 后面设置训练算法的yml配置文件 -# -o 配置可选参数 -# Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。 -# Global.load_static_weights 参数需要设置为 False。 -# Global.save_inference_dir参数设置转换的模型将保存的地址。 - -python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/e2e/ -``` - -转换成功后，在目录下有三个文件： -``` -/inference/e2e/ - ├── inference.pdiparams # 分类inference模型的参数文件 - ├── inference.pdiparams.info # 分类inference模型的参数信息，可忽略 - └── inference.pdmodel # 分类inference模型的program文件 -``` ## 二、文本检测模型推理 diff --git a/doc/doc_ch/pgnet.md b/doc/doc_ch/pgnet.md index 7e759afa0f8aafe285648e7989e20d7c96462101..6b5f62ddacbc32319c1cc7b9c706e79075c261a5 100644 --- a/doc/doc_ch/pgnet.md +++ b/doc/doc_ch/pgnet.md @@ -25,6 +25,13 @@ PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.Wang ![](../imgs_results/e2e_res_img293_pgnet.png) ![](../imgs_results/e2e_res_img295_pgnet.png) +### 性能指标 +| |det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS (size=640)|下载| +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +|Paper|85.30|86.80|86.1|-|-|61.7|38.20|-| +|Ours|87.03|82.48|84.69|61.71|58.43|60.03|62.61|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar)| + +*note：PaddleOCR里的PGNet实现针对预测速度做了优化，在精度下降可接受范围内，可以显著提升端对端预测速度* ## 二、环境配置 @@ -170,10 +177,3 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im 可视化文本端到端结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下： ![](../imgs_results/e2e_res_img623_pgnet.jpg) - -#### (3). 性能指标 -| |det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS (size=640)| -| --- | --- | --- | --- | --- | --- | --- | --- | -|Ours|87.03|82.48|84.69|61.71|58.43|60.03|62.61| -|Paper|85.30|86.80|86.1|-|-|61.7|38.20| -*note：PaddleOCR里的PGNet实现针对预测速度做了优化，在精度下降可接受范围内，可以显著提升端对端预测速度* diff --git a/doc/doc_en/pgnet_en.md b/doc/doc_en/pgnet_en.md index 8855206a68575cb8a39a1a101ebb9cc97970100b..868764afc74a14637e1214ef91298b054d416c96 100644 --- a/doc/doc_en/pgnet_en.md +++ b/doc/doc_en/pgnet_en.md @@ -23,6 +23,13 @@ The output of TBO and TCL can get text detection results after post-processing, The results of detection and recognition are as follows: ![](../imgs_results/e2e_res_img293_pgnet.png) ![](../imgs_results/e2e_res_img295_pgnet.png) +### Performance +| |det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS (size=640)|download| +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +|Paper|85.30|86.80|86.1|-|-|61.7|38.20|-| +|Ours|87.03|82.48|84.69|61.71|58.43|60.03|62.61|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar)| + +*note：PGNet in PaddleOCR optimizes the prediction speed, and can significantly improve the end-to-end prediction speed within the acceptable range of accuracy reduction* ## 2. Environment Configuration @@ -173,9 +180,3 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'e2e_res'. Examples of results are as follows: ![](../imgs_results/e2e_res_img623_pgnet.jpg) -#### (3). Performance -| |det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS (size=640)| -| --- | --- | --- | --- | --- | --- | --- | --- | -|Ours|87.03|82.48|84.69|61.71|58.43|60.03|62.61| -|Paper|85.30|86.80|86.1|-|-|61.7|38.20| -*note：PGNet in PaddleOCR optimizes the prediction speed, and can significantly improve the end-to-end prediction speed within the acceptable range of accuracy reduction*