提交 4e98a521 编写于 作者: littletomatodonkey's avatar littletomatodonkey

improve doc

上级 f02dcc06
...@@ -7,7 +7,6 @@ Global: ...@@ -7,7 +7,6 @@ Global:
save_epoch_step: 3 save_epoch_step: 3
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [0, 1000] eval_batch_step: [0, 1000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
......
...@@ -7,7 +7,10 @@ Global: ...@@ -7,7 +7,10 @@ Global:
save_epoch_step: 1200 save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [3000, 2000] eval_batch_step: [3000, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True # 1. If pretrained_model is saved in static mode, such as classification pretrained model
# from static branch, load_static_weights must be set as True.
# 2. If you want to finetune the pretrained models we provide in the docs,
# you should set load_static_weights as False.
load_static_weights: True load_static_weights: True
cal_metric_during_train: False cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
......
...@@ -7,7 +7,10 @@ Global: ...@@ -7,7 +7,10 @@ Global:
save_epoch_step: 1200 save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [3000, 2000] eval_batch_step: [3000, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True # 1. If pretrained_model is saved in static mode, such as classification pretrained model
# from static branch, load_static_weights must be set as True.
# 2. If you want to finetune the pretrained models we provide in the docs,
# you should set load_static_weights as False.
load_static_weights: True load_static_weights: True
cal_metric_during_train: False cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet18_vd_pretrained pretrained_model: ./pretrain_models/ResNet18_vd_pretrained
......
...@@ -7,7 +7,10 @@ Global: ...@@ -7,7 +7,10 @@ Global:
save_epoch_step: 1200 save_epoch_step: 1200
# evaluation is run every 2000 iterations # evaluation is run every 2000 iterations
eval_batch_step: [0, 2000] eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True # 1. If pretrained_model is saved in static mode, such as classification pretrained model
# from static branch, load_static_weights must be set as True.
# 2. If you want to finetune the pretrained models we provide in the docs,
# you should set load_static_weights as False.
load_static_weights: True load_static_weights: True
cal_metric_during_train: False cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
......
...@@ -7,7 +7,10 @@ Global: ...@@ -7,7 +7,10 @@ Global:
save_epoch_step: 1000 save_epoch_step: 1000
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000] eval_batch_step: [4000, 5000]
# if pretrained_model is saved in static mode, load_static_weights must set to True # 1. If pretrained_model is saved in static mode, such as classification pretrained model
# from static branch, load_static_weights must be set as True.
# 2. If you want to finetune the pretrained models we provide in the docs,
# you should set load_static_weights as False.
load_static_weights: True load_static_weights: True
cal_metric_during_train: False cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
......
...@@ -7,7 +7,10 @@ Global: ...@@ -7,7 +7,10 @@ Global:
save_epoch_step: 1200 save_epoch_step: 1200
# evaluation is run every 2000 iterations # evaluation is run every 2000 iterations
eval_batch_step: [0,2000] eval_batch_step: [0,2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True # 1. If pretrained_model is saved in static mode, such as classification pretrained model
# from static branch, load_static_weights must be set as True.
# 2. If you want to finetune the pretrained models we provide in the docs,
# you should set load_static_weights as False.
load_static_weights: True load_static_weights: True
cal_metric_during_train: False cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
......
...@@ -7,7 +7,10 @@ Global: ...@@ -7,7 +7,10 @@ Global:
save_epoch_step: 1000 save_epoch_step: 1000
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000] eval_batch_step: [4000, 5000]
# if pretrained_model is saved in static mode, load_static_weights must set to True # 1. If pretrained_model is saved in static mode, such as classification pretrained model
# from static branch, load_static_weights must be set as True.
# 2. If you want to finetune the pretrained models we provide in the docs,
# you should set load_static_weights as False.
load_static_weights: True load_static_weights: True
cal_metric_during_train: False cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet50_vd_pretrained/ pretrained_model: ./pretrain_models/ResNet50_vd_pretrained/
......
...@@ -7,7 +7,10 @@ Global: ...@@ -7,7 +7,10 @@ Global:
save_epoch_step: 1000 save_epoch_step: 1000
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000] eval_batch_step: [4000, 5000]
# if pretrained_model is saved in static mode, load_static_weights must set to True # 1. If pretrained_model is saved in static mode, such as classification pretrained model
# from static branch, load_static_weights must be set as True.
# 2. If you want to finetune the pretrained models we provide in the docs,
# you should set load_static_weights as False.
load_static_weights: True load_static_weights: True
cal_metric_during_train: False cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/ pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
......
...@@ -7,7 +7,10 @@ Global: ...@@ -7,7 +7,10 @@ Global:
save_epoch_step: 1000 save_epoch_step: 1000
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000] eval_batch_step: [4000, 5000]
# if pretrained_model is saved in static mode, load_static_weights must set to True # 1. If pretrained_model is saved in static mode, such as classification pretrained model
# from static branch, load_static_weights must be set as True.
# 2. If you want to finetune the pretrained models we provide in the docs,
# you should set load_static_weights as False.
load_static_weights: True load_static_weights: True
cal_metric_during_train: False cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/ pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
......
...@@ -7,7 +7,6 @@ Global: ...@@ -7,7 +7,6 @@ Global:
save_epoch_step: 3 save_epoch_step: 3
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [0, 2000] eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
......
...@@ -7,7 +7,6 @@ Global: ...@@ -7,7 +7,6 @@ Global:
save_epoch_step: 3 save_epoch_step: 3
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [0, 2000] eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
......
...@@ -7,7 +7,6 @@ Global: ...@@ -7,7 +7,6 @@ Global:
save_epoch_step: 3 save_epoch_step: 3
# evaluation is run every 2000 iterations # evaluation is run every 2000 iterations
eval_batch_step: [0, 2000] eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
......
...@@ -7,7 +7,6 @@ Global: ...@@ -7,7 +7,6 @@ Global:
save_epoch_step: 3 save_epoch_step: 3
# evaluation is run every 2000 iterations # evaluation is run every 2000 iterations
eval_batch_step: [0, 2000] eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
......
...@@ -7,7 +7,6 @@ Global: ...@@ -7,7 +7,6 @@ Global:
save_epoch_step: 3 save_epoch_step: 3
# evaluation is run every 2000 iterations # evaluation is run every 2000 iterations
eval_batch_step: [0, 2000] eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
......
...@@ -7,7 +7,6 @@ Global: ...@@ -7,7 +7,6 @@ Global:
save_epoch_step: 3 save_epoch_step: 3
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [0, 2000] eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
......
...@@ -7,7 +7,6 @@ Global: ...@@ -7,7 +7,6 @@ Global:
save_epoch_step: 3 save_epoch_step: 3
# evaluation is run every 2000 iterations # evaluation is run every 2000 iterations
eval_batch_step: [0, 2000] eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
......
...@@ -7,7 +7,6 @@ Global: ...@@ -7,7 +7,6 @@ Global:
save_epoch_step: 3 save_epoch_step: 3
# evaluation is run every 2000 iterations # evaluation is run every 2000 iterations
eval_batch_step: [0, 2000] eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
......
...@@ -7,7 +7,6 @@ Global: ...@@ -7,7 +7,6 @@ Global:
save_epoch_step: 3 save_epoch_step: 3
# evaluation is run every 2000 iterations # evaluation is run every 2000 iterations
eval_batch_step: [0, 2000] eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
......
...@@ -7,7 +7,6 @@ Global: ...@@ -7,7 +7,6 @@ Global:
save_epoch_step: 3 save_epoch_step: 3
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [0, 2000] eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
......
...@@ -7,7 +7,6 @@ Global: ...@@ -7,7 +7,6 @@ Global:
save_epoch_step: 3 save_epoch_step: 3
# evaluation is run every 2000 iterations # evaluation is run every 2000 iterations
eval_batch_step: [0, 2000] eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
......
...@@ -7,7 +7,6 @@ Global: ...@@ -7,7 +7,6 @@ Global:
save_epoch_step: 3 save_epoch_step: 3
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [0, 5000] eval_batch_step: [0, 5000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
......
...@@ -14,7 +14,7 @@ PaddleOCR在Windows 平台下基于`Visual Studio 2019 Community` 进行了测 ...@@ -14,7 +14,7 @@ PaddleOCR在Windows 平台下基于`Visual Studio 2019 Community` 进行了测
### Step1: 下载PaddlePaddle C++ 预测库 fluid_inference ### Step1: 下载PaddlePaddle C++ 预测库 fluid_inference
PaddlePaddle C++ 预测库针对不同的`CPU``CUDA`版本提供了不同的预编译版本,请根据实际情况下载: [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/windows_cpp_inference.html) PaddlePaddle C++ 预测库针对不同的`CPU``CUDA`版本提供了不同的预编译版本,请根据实际情况下载: [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/windows_cpp_inference.html)
解压后`D:\projects\fluid_inference`目录包含内容为: 解压后`D:\projects\fluid_inference`目录包含内容为:
``` ```
......
...@@ -72,9 +72,21 @@ opencv3/ ...@@ -72,9 +72,21 @@ opencv3/
* 有2种方式获取Paddle预测库,下面进行详细介绍。 * 有2种方式获取Paddle预测库,下面进行详细介绍。
#### 1.2.1 预测库源码编译 #### 1.2.1 直接下载安装
* [Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html)上提供了不同cuda版本的Linux预测库,可以在官网查看并选择合适的预测库版本。
* 下载之后使用下面的方法解压。
```
tar -xf paddle_inference.tgz
```
最终会在当前的文件夹中生成`paddle_inference/`的子文件夹。
#### 1.2.2 预测库源码编译
* 如果希望获取最新预测库特性,可以从Paddle github上克隆最新代码,源码编译预测库。 * 如果希望获取最新预测库特性,可以从Paddle github上克隆最新代码,源码编译预测库。
* 可以参考[Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html)的说明,从github上获取Paddle代码,然后进行编译,生成最新的预测库。使用git获取代码方法如下。 * 可以参考[Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html)的说明,从github上获取Paddle代码,然后进行编译,生成最新的预测库。使用git获取代码方法如下。
```shell ```shell
git clone https://github.com/PaddlePaddle/Paddle.git git clone https://github.com/PaddlePaddle/Paddle.git
...@@ -100,7 +112,7 @@ make -j ...@@ -100,7 +112,7 @@ make -j
make inference_lib_dist make inference_lib_dist
``` ```
更多编译参数选项可以参考Paddle C++预测库官网:[https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html) 更多编译参数选项可以参考Paddle C++预测库官网:[https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html)
* 编译完成之后,可以在`build/paddle_inference_install_dir/`文件下看到生成了以下文件及文件夹。 * 编译完成之后,可以在`build/paddle_inference_install_dir/`文件下看到生成了以下文件及文件夹。
...@@ -115,17 +127,7 @@ build/paddle_inference_install_dir/ ...@@ -115,17 +127,7 @@ build/paddle_inference_install_dir/
其中`paddle`就是C++预测所需的Paddle库,`version.txt`中包含当前预测库的版本信息。 其中`paddle`就是C++预测所需的Paddle库,`version.txt`中包含当前预测库的版本信息。
#### 1.2.2 直接下载安装
* [Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html)上提供了不同cuda版本的Linux预测库,可以在官网查看并选择合适的预测库版本。
* 下载之后使用下面的方法解压。
```
tar -xf paddle_inference.tgz
```
最终会在当前的文件夹中生成`paddle_inference/`的子文件夹。
## 2 开始运行 ## 2 开始运行
...@@ -223,7 +225,7 @@ char_list_file ../../ppocr/utils/ppocr_keys_v1.txt # 字典文件 ...@@ -223,7 +225,7 @@ char_list_file ../../ppocr/utils/ppocr_keys_v1.txt # 字典文件
visualize 1 # 是否对结果进行可视化,为1时,会在当前文件夹下保存文件名为`ocr_vis.png`的预测结果。 visualize 1 # 是否对结果进行可视化,为1时,会在当前文件夹下保存文件名为`ocr_vis.png`的预测结果。
``` ```
* PaddleOCR也支持多语言的预测,更多细节可以参考[识别文档](../../doc/doc_ch/recognition.md)中的多语言字典与模型部分 * PaddleOCR也支持多语言的预测,更多支持的语言和模型可以参考[识别文档](../../doc/doc_ch/recognition.md)中的多语言字典与模型部分,如果希望进行多语言预测,只需将修改`tools/config.txt`中的`char_list_file`(字典文件路径)以及`rec_model_dir`(inference模型路径)字段即可
最终屏幕上会输出检测结果如下。 最终屏幕上会输出检测结果如下。
...@@ -234,4 +236,4 @@ visualize 1 # 是否对结果进行可视化,为1时,会在当前文件夹 ...@@ -234,4 +236,4 @@ visualize 1 # 是否对结果进行可视化,为1时,会在当前文件夹
### 2.3 注意 ### 2.3 注意
* 在使用Paddle预测库时,推荐使用2.0.0-beta0版本的预测库。 * 在使用Paddle预测库时,推荐使用2.0.0版本的预测库。
...@@ -74,10 +74,23 @@ opencv3/ ...@@ -74,10 +74,23 @@ opencv3/
* There are 2 ways to obtain the Paddle inference library, described in detail below. * There are 2 ways to obtain the Paddle inference library, described in detail below.
#### 1.2.1 Direct download and installation
#### 1.2.1 Compile from the source code * Different cuda versions of the Linux inference library (based on GCC 4.8.2) are provided on the
[Paddle inference library official website](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/05_inference_deployment/inference/build_and_install_lib_en.html). You can view and select the appropriate version of the inference library on the official website.
* After downloading, use the following method to uncompress.
```
tar -xf paddle_inference.tgz
```
Finally you can see the following files in the folder of `paddle_inference/`.
#### 1.2.2 Compile from the source code
* If you want to get the latest Paddle inference library features, you can download the latest code from Paddle github repository and compile the inference library from the source code. * If you want to get the latest Paddle inference library features, you can download the latest code from Paddle github repository and compile the inference library from the source code.
* You can refer to [Paddle inference library] (https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from github, and then compile To generate the latest inference library. The method of using git to access the code is as follows. * You can refer to [Paddle inference library] (https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/05_inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from github, and then compile To generate the latest inference library. The method of using git to access the code is as follows.
```shell ```shell
...@@ -104,7 +117,7 @@ make -j ...@@ -104,7 +117,7 @@ make -j
make inference_lib_dist make inference_lib_dist
``` ```
For more compilation parameter options, please refer to the official website of the Paddle C++ inference library:[https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html](https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html). For more compilation parameter options, please refer to the official website of the Paddle C++ inference library:[https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/05_inference_deployment/inference/build_and_install_lib_en.html](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/05_inference_deployment/inference/build_and_install_lib_en.html).
* After the compilation process, you can see the following files in the folder of `build/paddle_inference_install_dir/`. * After the compilation process, you can see the following files in the folder of `build/paddle_inference_install_dir/`.
...@@ -120,22 +133,6 @@ build/paddle_inference_install_dir/ ...@@ -120,22 +133,6 @@ build/paddle_inference_install_dir/
Among them, `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library. Among them, `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
#### 1.2.2 Direct download and installation
* Different cuda versions of the Linux inference library (based on GCC 4.8.2) are provided on the
[Paddle inference library official website](https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html). You can view and select the appropriate version of the inference library on the official website.
* After downloading, use the following method to uncompress.
```
tar -xf paddle_inference.tgz
```
Finally you can see the following files in the folder of `paddle_inference/`.
## 2. Compile and run the demo ## 2. Compile and run the demo
### 2.1 Export the inference model ### 2.1 Export the inference model
...@@ -233,7 +230,7 @@ char_list_file ../../ppocr/utils/ppocr_keys_v1.txt # dictionary file ...@@ -233,7 +230,7 @@ char_list_file ../../ppocr/utils/ppocr_keys_v1.txt # dictionary file
visualize 1 # Whether to visualize the results,when it is set as 1, The prediction result will be save in the image file `./ocr_vis.png`. visualize 1 # Whether to visualize the results,when it is set as 1, The prediction result will be save in the image file `./ocr_vis.png`.
``` ```
* Multi-language inference is also supported in PaddleOCR, for more details, please refer to part of multi-language dictionaries and models in [recognition tutorial](../../doc/doc_en/recognition_en.md). * Multi-language inference is also supported in PaddleOCR, you can refer to [recognition tutorial](../../doc/doc_en/recognition_en.md) for more supported languages and models in PaddleOCR. Specifically, if you want to infer using multi-language models, you just need to modify values of `char_list_file` and `rec_model_dir` in file `tools/config.txt`.
The detection results will be shown on the screen, which is as follows. The detection results will be shown on the screen, which is as follows.
...@@ -245,4 +242,4 @@ The detection results will be shown on the screen, which is as follows. ...@@ -245,4 +242,4 @@ The detection results will be shown on the screen, which is as follows.
### 2.3 Notes ### 2.3 Notes
* Paddle2.0.0-beta0 inference model library is recommended for this toturial. * Paddle2.0.0 inference model library is recommended for this toturial.
...@@ -12,9 +12,14 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训 ...@@ -12,9 +12,14 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|模型类型|模型格式|简介| |模型类型|模型格式|简介|
|--- | --- | --- | |--- | --- | --- |
|推理模型|inference.pdmodel、inference.pdiparams|用于python预测引擎推理,[详情](./inference.md)| |推理模型|inference.pdmodel、inference.pdiparams|用于预测引擎推理,[详情](./inference.md)|
|训练模型、预训练模型|\*.pdparams、\*.pdopt、\*.states |训练过程中保存的模型的参数、优化器状态和训练中间信息,多用于模型指标评估和恢复训练| |训练模型、预训练模型|\*.pdparams、\*.pdopt、\*.states |训练过程中保存的模型的参数、优化器状态和训练中间信息,多用于模型指标评估和恢复训练|
|slim模型|\*.nb|用于lite部署| |slim模型|\*.nb|经过飞桨模型压缩工具PaddleSlim压缩后的模型,适用于移动端/IoT端等端侧部署场景(需使用飞桨Paddle Lite部署)。|
各个模型的关系如下面的示意图所示。
![](../imgs/model_prod_flow_ch.png)
<a name="文本检测模型"></a> <a name="文本检测模型"></a>
......
...@@ -12,9 +12,13 @@ The downloadable models provided by PaddleOCR include `inference model`, `traine ...@@ -12,9 +12,13 @@ The downloadable models provided by PaddleOCR include `inference model`, `traine
|model type|model format|description| |model type|model format|description|
|--- | --- | --- | |--- | --- | --- |
|inference model|inference.pdmodel、inference.pdiparams|Used for reasoning based on Python prediction engine,[detail](./inference_en.md)| |inference model|inference.pdmodel、inference.pdiparams|Used for inference based on Paddle inference engine,[detail](./inference_en.md)|
|trained model, pre-trained model|\*.pdparams、\*.pdopt、\*.states |The checkpoints model saved in the training process, which stores the parameters of the model, mostly used for model evaluation and continuous training.| |trained model, pre-trained model|\*.pdparams、\*.pdopt、\*.states |The checkpoints model saved in the training process, which stores the parameters of the model, mostly used for model evaluation and continuous training.|
|slim model|\*.nb|Generally used for Lite deployment| |slim model|\*.nb| Model compressed by PaddleSim (a model compression tool using PaddlePaddle), which is suitable for mobile-side deployment scenarios (Paddle-Lite is needed for slim model deployment). |
Relationship of the above models is as follows.
![](../imgs_en/model_prod_flow_en.png)
<a name="Detection"></a> <a name="Detection"></a>
### 1. Text Detection Model ### 1. Text Detection Model
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