This chapter introduces the C++ deployment method of the PaddleOCR model, and the corresponding python predictive deployment method refers to [document](../../doc/doc_ch/inference.md).
This chapter introduces the C++ deployment steps of the PaddleOCR model. The corresponding Python predictive deployment method refers to [document](../../doc/doc_ch/inference.md).
C++ is better than python in terms of performance calculation. Therefore, in most CPU and GPU deployment scenarios, C++ deployment is mostly used.
C++ is better than python in terms of performance. Therefore, in CPU and GPU deployment scenarios, C++ deployment is mostly used.
This section will introduce how to configure the C++ environment and complete it in the Linux\Windows (CPU\GPU) environment
This section will introduce how to configure the C++ environment and deploy PaddleOCR in Linux (CPU\GPU) environment. For Windows deployment please refer to [Windows](./docs/windows_vs2019_build.md) compilation guidelines.
PaddleOCR model deployment.
## 1. Prepare the Environment
## 1. Prepare the Environment
...
@@ -15,7 +14,7 @@ PaddleOCR model deployment.
...
@@ -15,7 +14,7 @@ PaddleOCR model deployment.
### 1.1 Compile OpenCV
### 1.1 Compile OpenCV
* First of all, you need to download the source code compiled package in the Linux environment from the opencv official website. Taking opencv3.4.7 as an example, the download command is as follows.
* First of all, you need to download the source code compiled package in the Linux environment from the OpenCV official website. Taking OpenCV 3.4.7 as an example, the download command is as follows.
Finally, you can see the folder of `opencv-3.4.7/` in the current directory.
Finally, you will see the folder of `opencv-3.4.7/` in the current directory.
* Compile opencv, the opencv source path (`root_path`) and installation path (`install_path`) should be set by yourself. Enter the opencv source code path and compile it in the following way.
* Compile OpenCV, the OpenCV source path (`root_path`) and installation path (`install_path`) should be set by yourself. Enter the OpenCV source code path and compile it in the following way.
```shell
```shell
...
@@ -58,11 +57,11 @@ make -j
...
@@ -58,11 +57,11 @@ make -j
make install
make install
```
```
Among them, `root_path` is the downloaded opencv source code path, and `install_path` is the installation path of opencv. After `make install` is completed, the opencv header file and library file will be generated in this folder for later OCR source code compilation.
In the above commands, `root_path` is the downloaded OpenCV source code path, and `install_path` is the installation path of OpenCV. After `make install` is completed, the OpenCV header file and library file will be generated in this folder for later OCR source code compilation.
The final file structure under the opencv installation path is as follows.
The final file structure under the OpenCV installation path is as follows.
```
```
opencv3/
opencv3/
...
@@ -79,20 +78,20 @@ opencv3/
...
@@ -79,20 +78,20 @@ opencv3/
#### 1.2.1 Direct download and installation
#### 1.2.1 Direct download and installation
[Paddle inference library official website](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html). You can view and select the appropriate version of the inference library on the official website.
[Paddle inference library official website](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html). You can review and select the appropriate version of the inference library on the official website.
* After downloading, use the following method to uncompress.
* After downloading, use the following command to extract files.
```
```
tar -xf paddle_inference.tgz
tar -xf paddle_inference.tgz
```
```
Finally you can see the following files in the folder of `paddle_inference/`.
Finally you will see the the folder of `paddle_inference/` in the current path.
#### 1.2.2 Compile from the source code
#### 1.2.2 Compile the inference 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. It is recommended to download the inference library with paddle version greater than or equal to 2.0.1.
* 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. It is recommended to download the inference library with paddle version greater than or equal to 2.0.1.
* 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/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.
Among them, `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
`paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
## 2. Compile and Run the Demo
## 2. Compile and Run the Demo
### 2.1 Export the inference model
### 2.1 Export the inference model
* You can refer to [Model inference](../../doc/doc_ch/inference.md)，export the inference model. After the model is exported, assuming it is placed in the `inference` directory, the directory structure is as follows.
* You can refer to [Model inference](../../doc/doc_ch/inference.md) and export the inference model. After the model is exported, assuming it is placed in the `inference` directory, the directory structure is as follows.
`OPENCV_DIR` is the opencv installation path; `LIB_DIR` is the download (`paddle_inference` folder)
`OPENCV_DIR` is the OpenCV installation path; `LIB_DIR` is the download (`paddle_inference` folder)
or the generated Paddle inference library path (`build/paddle_inference_install_dir` folder);
or the generated Paddle inference library path (`build/paddle_inference_install_dir` folder);
`CUDA_LIB_DIR` is the cuda library file path, in docker; it is `/usr/local/cuda/lib64`; `CUDNN_LIB_DIR` is the cudnn library file path, in docker it is `/usr/lib/x86_64-linux-gnu/`.
`CUDA_LIB_DIR` is the CUDA library file path, in docker; it is `/usr/local/cuda/lib64`; `CUDNN_LIB_DIR` is the cuDNN library file path, in docker it is `/usr/lib/x86_64-linux-gnu/`.
* After the compilation is completed, an executable file named `ppocr` will be generated in the `build` folder.
* After the compilation is completed, an executable file named `ppocr` will be generated in the `build` folder.
...
@@ -184,7 +183,15 @@ Execute the built executable file:
...
@@ -184,7 +183,15 @@ Execute the built executable file:
```shell
```shell
./build/ppocr <mode> [--param1][--param2][...]
./build/ppocr <mode> [--param1][--param2][...]
```
```
Here, `mode` is a required parameter，and the value range is ['det', 'rec', 'system'], representing using detection only, using recognition only and using the end-to-end system respectively. Specifically,
`mode` is a required parameter，and the valid values are
mode value | Model used
-----|------
det | Detection only
rec | Recognition only
system | End-to-end system
Specifically,
##### 1. run det demo:
##### 1. run det demo:
```shell
```shell
...
@@ -216,7 +223,7 @@ Here, `mode` is a required parameter，and the value range is ['det', 'rec', 'sy
...
@@ -216,7 +223,7 @@ Here, `mode` is a required parameter，and the value range is ['det', 'rec', 'sy
More parameters are as follows,
More parameters are as follows,
-common parameters
-Common parameters
|parameter|data type|default|meaning|
|parameter|data type|default|meaning|
| --- | --- | --- | --- |
| --- | --- | --- | --- |
...
@@ -226,7 +233,7 @@ More parameters are as follows,
...
@@ -226,7 +233,7 @@ More parameters are as follows,
|cpu_math_library_num_threads|int|10|Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed|
|cpu_math_library_num_threads|int|10|Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed|
|use_mkldnn|bool|true|Whether to use mkdlnn library|
|use_mkldnn|bool|true|Whether to use mkdlnn library|
-detection related parameters
-Detection related parameters
|parameter|data type|default|meaning|
|parameter|data type|default|meaning|
| --- | --- | --- | --- |
| --- | --- | --- | --- |
...
@@ -238,7 +245,7 @@ More parameters are as follows,
...
@@ -238,7 +245,7 @@ More parameters are as follows,
|use_polygon_score|bool|false|Whether to use polygon box to calculate bbox score, false means to use rectangle box to calculate. Use rectangular box to calculate faster, and polygonal box more accurate for curved text area.|
|use_polygon_score|bool|false|Whether to use polygon box to calculate bbox score, false means to use rectangle box to calculate. Use rectangular box to calculate faster, and polygonal box more accurate for curved text area.|
|visualize|bool|true|Whether to visualize the results，when it is set as true, The prediction result will be save in the image file `./ocr_vis.png`.|
|visualize|bool|true|Whether to visualize the results，when it is set as true, The prediction result will be save in the image file `./ocr_vis.png`.|
-classifier related parameters
-Classifier related parameters
|parameter|data type|default|meaning|
|parameter|data type|default|meaning|
| --- | --- | --- | --- |
| --- | --- | --- | --- |
...
@@ -246,7 +253,7 @@ More parameters are as follows,
...
@@ -246,7 +253,7 @@ More parameters are as follows,
|cls_model_dir|string|-|Address of direction classifier inference model|
|cls_model_dir|string|-|Address of direction classifier inference model|
|cls_thresh|float|0.9|Score threshold of the direction classifier|
|cls_thresh|float|0.9|Score threshold of the direction classifier|
-recogniton related parameters
-Recognition related parameters
|parameter|data type|default|meaning|
|parameter|data type|default|meaning|
| --- | --- | --- | --- |
| --- | --- | --- | --- |
...
@@ -265,4 +272,4 @@ The detection results will be shown on the screen, which is as follows.
...
@@ -265,4 +272,4 @@ The detection results will be shown on the screen, which is as follows.
### 2.3 Notes
### 2.3 Notes
* Paddle2.0.0 inference model library is recommended for this toturial.
* Paddle2.0.0 inference model library is recommended for this tutorial.
Many users hope package the PaddleOCR service into a docker image, so that it can be quickly released and used in the docker or k8s environment.
Many users hope package the PaddleOCR service into a docker image, so that it can be quickly released and used in the docker or K8s environment.
This page provides some standardized code to achieve this goal. You can quickly publish the PaddleOCR project into a callable Restful API service through the following steps. (At present, the deployment based on the HubServing mode is implemented first, and author plans to increase the deployment of the PaddleServing mode in the futrue)
This page provides some standardized code to achieve this goal. You can quickly publish the PaddleOCR project into a callable Restful API service through the following steps. (At present, the deployment based on the HubServing mode is implemented first, and author plans to increase the deployment of the PaddleServing mode in the future)
## 1. Prerequisites
## 1. Prerequisites
...
@@ -14,7 +14,7 @@ c. NVIDIA Container Toolkit（GPU，Docker 19.03+ can skip this）
...
@@ -14,7 +14,7 @@ c. NVIDIA Container Toolkit（GPU，Docker 19.03+ can skip this）
d. cuDNN 7.6+（GPU）
d. cuDNN 7.6+（GPU）
## 2. Build Image
## 2. Build Image
a. Goto Dockerfile directory（ps：Need to distinguish between cpu and gpu version, the following takes cpu as an example, gpu version needs to replace the keyword）
a. Goto Dockerfile directory（PS: Need to distinguish between CPU and GPU version, the following takes CPU as an example, GPU version needs to replace the keyword）
```
```
cd deploy/docker/hubserving/cpu
cd deploy/docker/hubserving/cpu
```
```
...
@@ -42,13 +42,13 @@ docker logs -f paddle_ocr
...
@@ -42,13 +42,13 @@ docker logs -f paddle_ocr
```
```
## 4. Test
## 4. Test
a. Calculate the Base64 encoding of the picture to be recognized (if you just test, you can use a free online tool, like：https://freeonlinetools24.com/base64-image/）
a. Calculate the Base64 encoding of the picture to be recognized (For test purpose, you can use a free online tool such as https://freeonlinetools24.com/base64-image/ )
b. Post a service request（sample request in sample_request.txt）
b. Post a service request（sample request in sample_request.txt）
```
```
curl -H "Content-Type:application/json" -X POST --data "{\"images\": [\"Input image Base64 encode(need to delete the code 'data:image/jpg;base64,'）\"]}" http://localhost:8868/predict/ocr_system
curl -H "Content-Type:application/json" -X POST --data "{\"images\": [\"Input image Base64 encode(need to delete the code 'data:image/jpg;base64,'）\"]}" http://localhost:8868/predict/ocr_system
```
```
c. Get resposne（If the call is successful, the following result will be returned）
c. Get response（If the call is successful, the following result will be returned）
This tutorial will introduce how to use [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) to deploy paddleOCR ultra-lightweight Chinese and English detection models on mobile phones.
This tutorial will introduce how to use [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) to deploy PaddleOCR ultra-lightweight Chinese and English detection models on mobile phones.
paddle-lite is a lightweight inference engine for PaddlePaddle. It provides efficient inference capabilities for mobile phones and IoTs, and extensively integrates cross-platform hardware to provide lightweight deployment solutions for end-side deployment issues.
paddle-lite is a lightweight inference engine for PaddlePaddle. It provides efficient inference capabilities for mobile phones and IoT, and extensively integrates cross-platform hardware to provide lightweight deployment solutions for end-side deployment issues.
PaddleOCR also provides a series of [models](../../../doc/doc_en/models_list_en.md). Developers can choose their own models or use their own models according to their needs.
PaddleOCR also provides a series of [models](../../../doc/doc_en/models_list_en.md). Developers can choose their own models or use their own models according to their needs.
### 3. Pruning sensitivity analysis
### 3. Pruning sensitivity analysis
After the pre-training model is loaded, sensitivity analysis is performed on each network layer of the model to understand the redundancy of each network layer, and save a sensitivity file which named: sen.pickle. After that, user could load the sensitivity file via the [methods provided by PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py#L221) and determining the pruning ratio of each network layer automatically. For specific details of sensitivity analysis, see：[Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/tutorials/image_classification_sensitivity_analysis_tutorial.md)
After the pre-trained model is loaded, sensitivity analysis is performed on each network layer of the model to understand the redundancy of each network layer, and save a sensitivity file which named: sen.pickle. After that, user could load the sensitivity file via the [methods provided by PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py#L221) and determining the pruning ratio of each network layer automatically. For specific details of sensitivity analysis, see：[Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/tutorials/image_classification_sensitivity_analysis_tutorial.md)
The function would return a dict after loading the sensitivity file. The keys of the dict are name of parameters in each layer. And the value of key is the information about pruning sensitivity of correspoding layer. In example, pruning 10% filter of the layer corresponding to conv10_expand_weights would lead to 0.65% degradation of model performance. The details could be seen at: [Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/algo/algo.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)
The function would return a dict after loading the sensitivity file. The keys of the dict are name of parameters in each layer. And the value of key is the information about pruning sensitivity of corresponding layer. In example, pruning 10% filter of the layer corresponding to conv10_expand_weights would lead to 0.65% degradation of model performance. The details could be seen at: [Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/algo/algo.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)
Enter the PaddleOCR root directory，perform sensitivity analysis on the model with the following command：
Enter the PaddleOCR root directory，perform sensitivity analysis on the model with the following command：
Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model.
Generally, a more complex model would achieve better performance in the task, but it also leads to some redundancy in the model.
Quantization is a technique that reduces this redundancy by reducing the full precision data to a fixed number,
Quantization is a technique that reduces this redundancy by reducing the full precision data to a fixed number,
so as to reduce model calculation complexity and improve model inference performance.
so as to reduce model calculation complexity and improve model inference performance.
...
@@ -31,14 +31,14 @@ python setup.py install
...
@@ -31,14 +31,14 @@ python setup.py install
```
```
### 2. Download Pretrain Model
### 2. Download Pre-trained Model
PaddleOCR provides a series of trained [models](../../../doc/doc_en/models_list_en.md).
PaddleOCR provides a series of pre-trained [models](../../../doc/doc_en/models_list_en.md).
If the model to be quantified is not in the list, you need to follow the [Regular Training](../../../doc/doc_en/quickstart_en.md) method to get the trained model.
If the model to be quantified is not in the list, you need to follow the [Regular Training](../../../doc/doc_en/quickstart_en.md) method to get the trained model.
### 3. Quant-Aware Training
### 3. Quant-Aware Training
Quantization training includes offline quantization training and online quantization training.
Quantization training includes offline quantization training and online quantization training.
Online quantization training is more effective. It is necessary to load the pre-training model.
Online quantization training is more effective. It is necessary to load the pre-trained model.
After the quantization strategy is defined, the model can be quantified.
After the quantization strategy is defined, the model can be quantified.
The code for quantization training is located in `slim/quantization/quant.py`. For example, to train a detection model, the training instructions are as follows:
The code for quantization training is located in `slim/quantization/quant.py`. For example, to train a detection model, the training instructions are as follows:
1.**Prediction error: got an unexpected keyword argument 'gradient_clip'**
1.**Prediction error: got an unexpected keyword argument 'gradient_clip'**
The installed version of paddle is incorrect. Currently, this project only supports paddle1.7, which will be adapted to 1.8 in the near future.
The installed version of paddle is incorrect. Currently, this project only supports Paddle1.7, which will be adapted to 1.8 in the near future.
2.**Error when converting attention recognition model: KeyError: 'predict'**
2.**Error when converting attention recognition model: KeyError: 'predict'**
Solved. Please update to the latest version of the code.
Solved. Please update to the latest version of the code.
...
@@ -31,7 +31,7 @@ At present, PaddleOCR has opensourced two Chinese models, namely 8.6M ultra-ligh
...
@@ -31,7 +31,7 @@ At present, PaddleOCR has opensourced two Chinese models, namely 8.6M ultra-ligh
|General Chinese OCR model|Resnet50_vd+Resnet34_vd|det_r50_vd_db.yml|rec_chinese_common_train.yml|
|General Chinese OCR model|Resnet50_vd+Resnet34_vd|det_r50_vd_db.yml|rec_chinese_common_train.yml|
8.**Is there a plan to opensource a model that only recognizes numbers or only English + numbers?**
8.**Is there a plan to opensource a model that only recognizes numbers or only English + numbers?**
It is not planned to opensource numbers only, numbers + English only, or other vertical text models. Paddleocr has opensourced a variety of detection and recognition algorithms for customized training. The two Chinese models are also based on the training output of the open-source algorithm library. You can prepare the data according to the tutorial, choose the appropriate configuration file, train yourselves, and we believe that you can get good result. If you have any questions during the training, you are welcome to open issues or ask in the communication group. We will answer them in time.
It is not planned to opensource numbers only, numbers + English only, or other vertical text models. PaddleOCR has opensourced a variety of detection and recognition algorithms for customized training. The two Chinese models are also based on the training output of the open-source algorithm library. You can prepare the data according to the tutorial, choose the appropriate configuration file, train yourselves, and we believe that you can get good result. If you have any questions during the training, you are welcome to open issues or ask in the communication group. We will answer them in time.
9.**What is the training data used by the open-source model? Can it be opensourced?**
9.**What is the training data used by the open-source model? Can it be opensourced?**
At present, the open source model, dataset and magnitude are as follows:
At present, the open source model, dataset and magnitude are as follows:
...
@@ -46,11 +46,11 @@ At present, the open source model, dataset and magnitude are as follows:
...
@@ -46,11 +46,11 @@ At present, the open source model, dataset and magnitude are as follows:
10.**Error in using the model with TPS module for prediction**
10.**Error in using the model with TPS module for prediction**
Error message: Input(X) dims[3] and Input(Grid) dims[2] should be equal, but received X dimension[3]\(108) != Grid dimension[2]\(100)
Error message: Input(X) dims[3] and Input(Grid) dims[2] should be equal, but received X dimension[3]\(108) != Grid dimension[2]\(100)
Solution：TPS does not support variable shape. Please set --rec_image_shape='3,32,100' and --rec_char_type='en'
Solution: TPS does not support variable shape. Please set --rec_image_shape='3,32,100' and --rec_char_type='en'
11.**Custom dictionary used during training, the recognition results show that words do not appear in the dictionary**
11.**Custom dictionary used during training, the recognition results show that words do not appear in the dictionary**
The used custom dictionary path is not set when making prediction. The solution is setting parameter `rec_char_dict_path` to the corresponding dictionary file.
The used custom dictionary path is not set when making prediction. The solution is setting parameter `rec_char_dict_path` to the corresponding dictionary file.
12.**Results of cpp_infer and python_inference are very different**
12.**Results of cpp_infer and python_inference are very different**
Versions of exprted inference model and inference libraray should be same. For example, on Windows platform, version of the inference libraray that PaddlePaddle provides is 1.8, but version of the inference model that PaddleOCR provides is 1.7, you should export model yourself(`tools/export_model.py`) on PaddlePaddle1.8 and then use the exported model for inference.
Versions of exported inference model and inference library should be same. For example, on Windows platform, version of the inference library that PaddlePaddle provides is 1.8, but version of the inference model that PaddleOCR provides is 1.7, you should export model yourself(`tools/export_model.py`) on PaddlePaddle1.8 and then use the exported model for inference.
PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, Please uncomment the `RecAug` and `RandAugment` fields under `Train.dataset.transforms` in the configuration file.
PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, Please uncomment the `RecAug` and `RandAugment` fields under `Train.dataset.transforms` in the configuration file.
The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, RandAugment.
The default perturbation methods are: cvtColor, blur, jitter, Gauss noise, random crop, perspective, color reverse, RandAugment.
Except for RandAugment, each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to:
Except for RandAugment, each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to:
@@ -15,9 +15,9 @@ The following list can be viewed through `--help`
...
@@ -15,9 +15,9 @@ The following list can be viewed through `--help`
| -c | ALL | Specify configuration file to use | None | **Please refer to the parameter introduction for configuration file usage** |
| -c | ALL | Specify configuration file to use | None | **Please refer to the parameter introduction for configuration file usage** |
| -o | ALL | set configuration options | None | Configuration using -o has higher priority than the configuration file selected with -c. E.g: -o Global.use_gpu=false |
| -o | ALL | set configuration options | None | Configuration using -o has higher priority than the configuration file selected with -c. E.g: -o Global.use_gpu=false |
| save_model_dir | Set model save path | output/{算法名称} | \ |
| save_model_dir | Set model save path | output/{算法名称} | \ |
| save_epoch_step | Set model save interval | 3 | \ |
| save_epoch_step | Set model save interval | 3 | \ |
| eval_batch_step | Set the model evaluation interval | 2000 or [1000, 2000] | runing evaluation every 2000 iters or evaluation is run every 2000 iterations after the 1000th iteration |
| eval_batch_step | Set the model evaluation interval | 2000 or [1000, 2000] | running evaluation every 2000 iters or evaluation is run every 2000 iterations after the 1000th iteration |
| cal_metric_during_train | Set whether to evaluate the metric during the training process. At this time, the metric of the model under the current batch is evaluated | true | \ |
| cal_metric_during_train | Set whether to evaluate the metric during the training process. At this time, the metric of the model under the current batch is evaluated | true | \ |
| load_static_weights | Set whether the pre-training model is saved in static graph mode (currently only required by the detection algorithm) | true | \ |
| load_static_weights | Set whether the pre-training model is saved in static graph mode (currently only required by the detection algorithm) | true | \ |
| pretrained_model | Set the path of the pre-trained model | ./pretrain_models/CRNN/best_accuracy | \ |
| pretrained_model | Set the path of the pre-trained model | ./pretrain_models/CRNN/best_accuracy | \ |
...
@@ -65,7 +65,7 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck
...
@@ -65,7 +65,7 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck
| model_type | Network Type | rec | Currently support`rec`,`det`,`cls` |
| model_type | Network Type | rec | Currently support`rec`,`det`,`cls` |
| algorithm | Model name | CRNN | See [algorithm_overview](./algorithm_overview.md) for the support list |
| algorithm | Model name | CRNN | See [algorithm_overview](./algorithm_overview_en.md) for the support list |
| **Transform** | Set the transformation method | - | Currently only recognition algorithms are supported, see [ppocr/modeling/transform](../../ppocr/modeling/transform) for details |
| **Transform** | Set the transformation method | - | Currently only recognition algorithms are supported, see [ppocr/modeling/transform](../../ppocr/modeling/transform) for details |
| name | Transformation class name | TPS | Currently supports `TPS` |
| name | Transformation class name | TPS | Currently supports `TPS` |
| num_fiducial | Number of TPS control points | 20 | Ten on the top and bottom |
| num_fiducial | Number of TPS control points | 20 | Ten on the top and bottom |
...
@@ -134,14 +134,14 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck
...
@@ -134,14 +134,14 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck
## 3. Multilingual Config File Generation
## 3. Multilingual Config File Generation
PaddleOCR currently supports 80 (except Chinese) language recognition. A multi-language configuration file template is
PaddleOCR currently supports recognition for 80 languages (besides Chinese). A multi-language configuration file template is
provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
There are two ways to create the required configuration file:：
There are two ways to create the required configuration file:
1. Automatically generated by script
1. Automatically generated by script
[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py)Can help you generate configuration files for multi-language models
Script [generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py)can help you generate configuration files for multi-language models.
- Take Italian as an example, if your data is prepared in the following format:
- Take Italian as an example, if your data is prepared in the following format:
@@ -22,7 +22,7 @@ For more details about data preparation and training tutorials, refer to the doc
...
@@ -22,7 +22,7 @@ For more details about data preparation and training tutorials, refer to the doc
PaddleOCR provides a concatenation tool for detection and recognition models, which can connect any trained detection model and any recognition model into a two-stage text recognition system. The input image goes through four main stages: text detection, text rectification, text recognition, and score filtering to output the text position and recognition results, and at the same time, you can choose to visualize the results.
PaddleOCR provides a concatenation tool for detection and recognition models, which can connect any trained detection model and any recognition model into a two-stage text recognition system. The input image goes through four main stages: text detection, text rectification, text recognition, and score filtering to output the text position and recognition results, and at the same time, you can choose to visualize the results.
When performing prediction, you need to specify the path of a single image or a image folder through the parameter `image_dir`, the parameter `det_model_dir` specifies the path of detection model, and the parameter `rec_model_dir` specifies the path of recogniton model. The visualized results are saved to the `./inference_results` folder by default.
When performing prediction, you need to specify the path of a single image or a image folder through the parameter `image_dir`, the parameter `det_model_dir` specifies the path of detection model, and the parameter `rec_model_dir` specifies the path of recognition model. The visualized results are saved to the `./inference_results` folder by default.
@@ -59,10 +59,10 @@ The `points` in the dictionary represent the coordinates (x, y) of the four poin
...
@@ -59,10 +59,10 @@ The `points` in the dictionary represent the coordinates (x, y) of the four poin
If you want to train PaddleOCR on other datasets, please build the annotation file according to the above format.
If you want to train PaddleOCR on other datasets, please build the annotation file according to the above format.
### 1.2 Download Pretrained Model
### 1.2 Download Pre-trained Model
First download the pretrained model. The detection model of PaddleOCR currently supports 3 backbones, namely MobileNetV3, ResNet18_vd and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.0/ppcls/modeling/architectures) to replace backbone according to your needs.
First download the pre-trained model. The detection model of PaddleOCR currently supports 3 backbones, namely MobileNetV3, ResNet18_vd and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.0/ppcls/modeling/architectures) to replace backbone according to your needs.
And the responding download link of backbone pretrain weights can be found in (https://github.com/PaddlePaddle/PaddleClas/blob/release%2F2.0/README_cn.md#resnet%E5%8F%8A%E5%85%B6vd%E7%B3%BB%E5%88%97).
And the responding download link of backbone pre-trained weights can be found in (https://github.com/PaddlePaddle/PaddleClas/blob/release%2F2.0/README_cn.md#resnet%E5%8F%8A%E5%85%B6vd%E7%B3%BB%E5%88%97).
The high performance of distributed training is one of the core advantages of PaddlePaddle. In the classification task, distributed training can achieve almost linear speedup ratio. Generally, OCR training task need massive training data. Such as recognition, ppocrv2.0 model is trained based on 1800W dataset, which is very time-consuming if using single machine. Therefore, the distributed training is used in paddleocr to speedup the training task. For more information about distributed training, please refer to [distributed training quick start tutorial](https://fleet-x.readthedocs.io/en/latest/paddle_fleet_rst/parameter_server/ps_quick_start.html).
The high performance of distributed training is one of the core advantages of PaddlePaddle. In the classification task, distributed training can achieve almost linear speedup ratio. Generally, OCR training task need massive training data. Such as recognition, PP-OCR v2.0 model is trained based on 1800W dataset, which is very time-consuming if using single machine. Therefore, the distributed training is used in PaddleOCR to speedup the training task. For more information about distributed training, please refer to [distributed training quick start tutorial](https://fleet-x.readthedocs.io/en/latest/paddle_fleet_rst/parameter_server/ps_quick_start.html).
* The IP addresses of different machines need to be separated by commas, which can be queried through `ifconfig` or `ipconfig`.
* The IP addresses of different machines need to be separated by commas, which can be queried through `ifconfig` or `ipconfig`.
* Different machines need to be set to be secret free and can `ping` success with others directly, otherwise communication cannot establish between them.
* Different machines need to be set to be secret free and can `ping` success with others directly, otherwise communication cannot establish between them.
* The code, data and start command betweent different machines must be completely consistent and then all machines need to run start command. The first machine in the `ip_list` is set to `trainer0`, and so on.
* The code, data and start command between different machines must be completely consistent and then all machines need to run start command. The first machine in the `ip_list` is set to `trainer0`, and so on.
- To activate the conda environment you just created, enter the following command at the command line.
- To activate the Conda environment you just created, enter the following command at the command line.
```shell
```shell
# Activate the paddle_env environment
# Activate the paddle_env environment
...
@@ -91,7 +91,7 @@ The above anaconda environment and python environment are installed
...
@@ -91,7 +91,7 @@ The above anaconda environment and python environment are installed
#### 1.2.1 Installing Anaconda
#### 1.2.1 Installing Anaconda
- Note: To use paddlepaddle you need to install the python environment first, here we choose the python integrated environment Anaconda toolkit
- Note: To use PaddlePaddle you need to install the python environment first, here we choose the python integrated environment Anaconda toolkit
- Anaconda is a common python package manager
- Anaconda is a common python package manager
- After installing Anaconda, you can install the python environment, as well as numpy and other required toolkit environment
- After installing Anaconda, you can install the python environment, as well as numpy and other required toolkit environment
...
@@ -108,17 +108,17 @@ The above anaconda environment and python environment are installed
...
@@ -108,17 +108,17 @@ The above anaconda environment and python environment are installed
- Just follow the default settings, it will take a while to install
- Just follow the default settings, it will take a while to install
- It is recommended to install a code editor such as vscode or pycharm
- It is recommended to install a code editor such as VSCode or PyCharm
#### 1.2.2 Open a terminal and create a conda environment
#### 1.2.2 Open a terminal and create a Conda environment
- Open the terminal
- Open the terminal
- Press command and spacebar at the same time, type "terminal" in the focus search, double click to enter terminal
- Press command and spacebar at the same time, type "terminal" in the focus search, double click to enter terminal
-**Add conda to the environment variables**
-**Add Conda to the environment variables**
- Environment variables are added so that the system can recognize the conda command
- Environment variables are added so that the system can recognize the Conda command
- Open `~/.bash_profile` in the terminal by typing the following command.
- Open `~/.bash_profile` in the terminal by typing the following command.
...
@@ -126,7 +126,7 @@ The above anaconda environment and python environment are installed
...
@@ -126,7 +126,7 @@ The above anaconda environment and python environment are installed
vim ~/.bash_profile
vim ~/.bash_profile
```
```
- Add conda as an environment variable in `~/.bash_profile`.
- Add Conda as an environment variable in `~/.bash_profile`.
```shell
```shell
# Press i first to enter edit mode
# Press i first to enter edit mode
...
@@ -156,12 +156,12 @@ The above anaconda environment and python environment are installed
...
@@ -156,12 +156,12 @@ The above anaconda environment and python environment are installed
- When you are done, press `esc` to exit edit mode, then type `:wq!` and enter to save and exit
- When you are done, press `esc` to exit edit mode, then type `:wq!` and enter to save and exit
- Verify that the conda command is recognized.
- Verify that the Conda command is recognized.
- Enter `source ~/.bash_profile` in the terminal to update the environment variables
- Enter `source ~/.bash_profile` in the terminal to update the environment variables
- Enter `conda info --envs` in the terminal again, if it shows that there is a base environment, then conda has been added to the environment variables
- Enter `conda info --envs` in the terminal again, if it shows that there is a base environment, then Conda has been added to the environment variables
- Create a new conda environment
- Create a new Conda environment
```shell
```shell
# Enter the following command at the command line to create an environment called paddle_env
# Enter the following command at the command line to create an environment called paddle_env
...
@@ -175,7 +175,7 @@ The above anaconda environment and python environment are installed
...
@@ -175,7 +175,7 @@ The above anaconda environment and python environment are installed
- To activate the conda environment you just created, enter the following command at the command line.
- To activate the Conda environment you just created, enter the following command at the command line.
```shell
```shell
# Activate the paddle_env environment
# Activate the paddle_env environment
...
@@ -198,7 +198,7 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
...
@@ -198,7 +198,7 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
#### 1.3.1 Anaconda environment configuration
#### 1.3.1 Anaconda environment configuration
- Note: To use paddlepaddle you need to install the python environment first, here we choose the python integrated environment Anaconda toolkit
- Note: To use PaddlePaddle you need to install the python environment first, here we choose the python integrated environment Anaconda toolkit
- Anaconda is a common python package manager
- Anaconda is a common python package manager
- After installing Anaconda, you can install the python environment, as well as numpy and other required toolkit environment
- After installing Anaconda, you can install the python environment, as well as numpy and other required toolkit environment
...
@@ -214,9 +214,9 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
...
@@ -214,9 +214,9 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
- Select the appropriate version for your operating system
- Select the appropriate version for your operating system
- Type `uname -m` in the terminal to check the command set used by your system
- Type `uname -m` in the terminal to check the command set used by your system
- Download method 1: Download locally, then transfer the installation package to the linux server
- Download method 1: Download locally, then transfer the installation package to the Linux server
- Download method 2: Directly use linux command line to download
- Download method 2: Directly use Linux command line to download
```shell
```shell
# First install wget
# First install wget
...
@@ -277,12 +277,12 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
...
@@ -277,12 +277,12 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
- When you are done, press `esc` to exit edit mode, then type `:wq!` and enter to save and exit
- When you are done, press `esc` to exit edit mode, then type `:wq!` and enter to save and exit
- Verify that the conda command is recognized.
- Verify that the Conda command is recognized.
- Enter `source ~/.bash_profile` in the terminal to update the environment variables
- Enter `source ~/.bash_profile` in the terminal to update the environment variables
- Enter `conda info --envs` in the terminal again, if it shows that there is a base environment, then conda has been added to the environment variables
- Enter `conda info --envs` in the terminal again, if it shows that there is a base environment, then Conda has been added to the environment variables
- Create a new conda environment
- Create a new Conda environment
```shell
```shell
# Enter the following command at the command line to create an environment called paddle_env
# Enter the following command at the command line to create an environment called paddle_env
...
@@ -296,7 +296,7 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
...
@@ -296,7 +296,7 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
