Skip to content

P
PaddleOCR

PaddlePaddle / PaddleOCR
1 年多 前同步成功

通知 1533
Star 32963
Fork 6643
P
PaddleOCR
提交 d977e550 编写于 作者: W WenmuZhou
浏览文件
操作

merge release/2.1

上级 bee2bb7d 2fa256a7
隐藏空白更改
内联 并排
Showing with 688 addition and 273 deletion
README_ch.md
浏览文件 @ d977e550
......@@ -4,11 +4,11 @@
PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
## 注意
PaddleOCR同时支持动态图与静态图两种编程范式
- 动态图版本：release/2.1（默认分支，开发分支为dygraph分支），需将paddle版本升级至2.0.0（[快速安装](./doc/doc_ch/installation.md)）
- 动态图版本：release/2.1（默认分支，开发分支为dygraph分支），需将paddle版本升级至2.0.0或以上版本（[快速安装](./doc/doc_ch/installation.md)）
- 静态图版本：develop分支
**近期更新**
- 2021.5.17 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数223个，每周一都会更新，欢迎大家持续关注。
- 2021.5.24 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数228个，每周一都会更新，欢迎大家持续关注。
- PaddleOCR研发团队对最新发版内容技术深入解读，4月13日晚上19:00，[直播地址](https://live.bilibili.com/21689802)。
- 2021.4.8 release 2.1版本，新增AAAI 2021论文[端到端识别算法PGNet](./doc/doc_ch/pgnet.md)开源，[多语言模型](./doc/doc_ch/multi_languages.md)支持种类增加到80+。
- 2021.2.8 正式发布PaddleOCRv2.0(branch release/2.0)并设置为推荐用户使用的默认分支. 发布的详细内容，请参考: https://github.com/PaddlePaddle/PaddleOCR/releases/tag/v2.0.0
......@@ -104,8 +104,8 @@ PaddleOCR同时支持动态图与静态图两种编程范式
- [效果展示](#效果展示)
- FAQ
- [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md)
- [【理论篇】OCR通用43个问题](./doc/doc_ch/FAQ.md)
- [【实战篇】PaddleOCR实战170个问题](./doc/doc_ch/FAQ.md)
- [【理论篇】OCR通用44个问题](./doc/doc_ch/FAQ.md)
- [【实战篇】PaddleOCR实战174个问题](./doc/doc_ch/FAQ.md)
- [技术交流群](#欢迎加入PaddleOCR技术交流群)
- [参考文献](./doc/doc_ch/reference.md)
- [许可证书](#许可证书)
......
deploy/cpp_infer/readme.md
浏览文件 @ d977e550
......@@ -77,7 +77,7 @@ opencv3/
#### 1.2.1 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（*建议选择paddle版本>=2.0.1版本的预测库* ）。
* [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（*建议选择paddle版本>=2.0.1版本的预测库* ）。
* 下载之后使用下面的方法解压。
......@@ -89,7 +89,7 @@ tar -xf paddle_inference.tgz
#### 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。
* 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。
* 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
git clone https://github.com/PaddlePaddle/Paddle.git
......@@ -115,7 +115,7 @@ make -j
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/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)。
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。
......@@ -227,7 +227,10 @@ 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。
```
* 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。
* 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。
多语言模型和字典文件对应关系可以参考[文档](../../doc/doc_ch/multi_languages.md#预测部署)
最终屏幕上会输出检测结果如下。
......
deploy/cpp_infer/readme_en.md
浏览文件 @ d977e550
......@@ -234,6 +234,7 @@ visualize 1 # Whether to visualize the results，when it is set as 1, The predic
```
* 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 corresponding relationship between the multi-language model and the dictionary file can be found in [document](../../doc/doc_en/multi_languages_en.md#inference)
The detection results will be shown on the screen, which is as follows.
......
deploy/hubserving/readme.md
浏览文件 @ d977e550
......@@ -42,6 +42,8 @@ pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
**模型路径可在`params.py`中查看和修改。** 更多模型可以从PaddleOCR提供的[模型库](../../doc/doc_ch/models_list.md)下载，也可以替换成自己训练转换好的模型。
* 如果需要使用多语言模型，请同时修改 `ocr_rec/params.py` 中 `cfg.rec_model_dir` 和 `cfg.rec_char_dict_path` 参数，多语言模型和字典文件对应关系可以参考[文档](../../doc/doc_ch/multi_languages.md#预测部署)
### 3. 安装服务模块
PaddleOCR提供3种服务模块，根据需要安装所需模块。
......
deploy/hubserving/readme_en.md
浏览文件 @ d977e550
......@@ -43,6 +43,10 @@ text direction classifier: ./inference/ch_ppocr_mobile_v2.0_cls_infer/
**The model path can be found and modified in `params.py`.** More models provided by PaddleOCR can be obtained from the [model library](../../doc/doc_en/models_list_en.md). You can also use models trained by yourself.
* If you need to use a multi-language model, please modify the parameters of `cfg.rec_model_dir` and `cfg.rec_char_dict_path` in `ocr_rec/params.py` at the same time.
The corresponding relationship between the multi-language model and the dictionary file can be found in [document](../../doc/doc_en/multi_languages_en.md#inference)
### 3. Install Service Module
PaddleOCR provides 3 kinds of service modules, install the required modules according to your needs.
......
deploy/lite/Makefile
浏览文件 @ d977e550
......@@ -40,7 +40,7 @@ CXX_LIBS = ${OPENCV_LIBS} -L$(LITE_ROOT)/cxx/lib/ -lpaddle_light_api_shared $(SY
#CXX_LIBS = $(LITE_ROOT)/cxx/lib/libpaddle_api_light_bundled.a $(SYSTEM_LIBS)
ocr_db_crnn: fetch_opencv ocr_db_crnn.o crnn_process.o db_post_process.o clipper.o cls_process.o
ocr_db_crnn: fetch_clipper fetch_opencv ocr_db_crnn.o crnn_process.o db_post_process.o clipper.o cls_process.o
$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) ocr_db_crnn.o crnn_process.o db_post_process.o clipper.o cls_process.o -o ocr_db_crnn $(CXX_LIBS) $(LDFLAGS)
ocr_db_crnn.o: ocr_db_crnn.cc
......
deploy/lite/readme.md
浏览文件 @ d977e550
......@@ -125,7 +125,7 @@ cd build.opt/lite/api/
```
# 【推荐】 下载PaddleOCR V2.0版本的中英文 inference模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_slim_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_slim_infer.tar
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_slim_nfer.tar && tar xf ch_ppocr_mobile_v2.0_rec_slim_infer.tar
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_slim_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_slim_infer.tar
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls_slim_infer.tar && tar xf ch_ppocr_mobile_v2.0_cls_slim_infer.tar
# 转换V2.0检测模型
./opt --model_file=./ch_ppocr_mobile_v2.0_det_slim_infer/inference.pdmodel --param_file=./ch_ppocr_mobile_v2.0_det_slim_infer/inference.pdiparams --optimize_out=./ch_ppocr_mobile_v2.0_det_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer
......@@ -224,6 +224,8 @@ ppocr_keys_v1.txt # 中文字典
...
```
更多语言模型和字典文件对应关系可以参考[文档](../../doc/doc_ch/multi_languages.md#预测部署)
2. `config.txt` 包含了检测器、分类器的超参数，如下：
```
max_side_len 960 # 输入图像长宽大于960时，等比例缩放图像，使得图像最长边为960
......@@ -238,7 +240,7 @@ use_direction_classify 0 # 是否使用方向分类器，0表示不使用，1
上述步骤完成后就可以使用adb将文件push到手机上运行，步骤如下：
```
# 执行编译，得到可执行文件ocr_db_crnn
# 执行编译，得到可执行文件ocr_db_crnn，第一次执行此命令会下载opencv等依赖库，下载完成后，需要再执行一次
make -j
# 将编译的可执行文件移动到debug文件夹中
......
deploy/lite/readme_en.md
浏览文件 @ d977e550
......@@ -222,6 +222,7 @@ dict/japan_dict.txt # japan
dict/korean_dict.txt # korean
ppocr_keys_v1.txt # chinese
```
For more language model and dictionary file correspondence, please refer to [Document](../../doc/doc_en/multi_languages_en.md#inference)
2. `config.txt` of the detector and classifier, as shown below:
```
......@@ -237,7 +238,8 @@ use_direction_classify 0 # Whether to use the direction classifier, 0 means no
After the above steps are completed, you can use adb to push the file to the phone to run, the steps are as follows:
```
# Execute the compilation and get the executable file ocr_db_crnn
# Execute the compilation and get the executable file ocr_db_crnn.
# The first execution of this command will download dependent libraries such as opencv. After the download is complete, you need to execute it again
make -j
# Move the compiled executable file to the debug folder
mv ocr_db_crnn ./debug/
......
deploy/pdserving/README.md
浏览文件 @ d977e550
......@@ -30,38 +30,32 @@ The introduction and tutorial of Paddle Serving service deployment framework ref
PaddleOCR operating environment and Paddle Serving operating environment are needed.
1. Please prepare PaddleOCR operating environment reference [link](../../doc/doc_ch/installation.md).
Download the corresponding paddle whl package according to the environment, it is recommended to install version 2.0.1.
2. The steps of PaddleServing operating environment prepare are as follows:
Install serving which used to start the service
```
pip3 install paddle-serving-server==0.5.0 # for CPU
pip3 install paddle-serving-server-gpu==0.5.0 # for GPU
pip3 install paddle-serving-server==0.6.1 # for CPU
pip3 install paddle-serving-server-gpu==0.6.1 # for GPU
# Other GPU environments need to confirm the environment and then choose to execute the following commands
pip3 install paddle-serving-server-gpu==0.5.0.post9 # GPU with CUDA9.0
pip3 install paddle-serving-server-gpu==0.5.0.post10 # GPU with CUDA10.0
pip3 install paddle-serving-server-gpu==0.5.0.post101 # GPU with CUDA10.1 + TensorRT6
pip3 install paddle-serving-server-gpu==0.5.0.post11 # GPU with CUDA10.1 + TensorRT7
pip3 install paddle-serving-server-gpu==0.6.1.post101 # GPU with CUDA10.1 + TensorRT6
pip3 install paddle-serving-server-gpu==0.6.1.post11 # GPU with CUDA11 + TensorRT7
```
3. Install the client to send requests to the service
```
pip3 install paddle-serving-client==0.5.0 # for CPU
In [download link](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md) find the client installation package corresponding to the python version.
The python3.7 version is recommended here:
pip3 install paddle-serving-client-gpu==0.5.0 # for GPU
```
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.0.0-cp37-none-any.whl
pip3 install paddle_serving_client-0.0.0-cp37-none-any.whl
```
4. Install serving-app
```
pip3 install paddle-serving-app==0.3.0
# fix local_predict to support load dynamic model
# find the install directoory of paddle_serving_app
vim /usr/local/lib/python3.7/site-packages/paddle_serving_app/local_predict.py
# replace line 85 of local_predict.py config = AnalysisConfig(model_path) with:
if os.path.exists(os.path.join(model_path, "__params__")):
config = AnalysisConfig(os.path.join(model_path, "__model__"), os.path.join(model_path, "__params__"))
else:
config = AnalysisConfig(model_path)
pip3 install paddle-serving-app==0.6.1
```
**note:** If you want to install the latest version of PaddleServing, refer to [link](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md).
......@@ -74,38 +68,38 @@ When using PaddleServing for service deployment, you need to convert the saved i
Firstly, download the [inference model](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-20-series-model-listupdate-on-dec-15) of PPOCR
```
# Download and unzip the OCR text detection model
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
# Download and unzip the OCR text recognition model
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
```
Then, you can use installed paddle_serving_client tool to convert inference model to server model.
Then, you can use installed paddle_serving_client tool to convert inference model to mobile model.
```
# Detection model conversion
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_det_infer/ \
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_det_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./ppocr_det_server_2.0_serving/ \
--serving_client ./ppocr_det_server_2.0_client/
--serving_server ./ppocr_det_mobile_2.0_serving/ \
--serving_client ./ppocr_det_mobile_2.0_client/
# Recognition model conversion
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_rec_infer/ \
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_rec_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./ppocr_rec_server_2.0_serving/ \
--serving_client ./ppocr_rec_server_2.0_client/
--serving_server ./ppocr_rec_mobile_2.0_serving/ \
--serving_client ./ppocr_rec_mobile_2.0_client/
```
After the detection model is converted, there will be additional folders of `ppocr_det_server_2.0_serving` and `ppocr_det_server_2.0_client` in the current folder, with the following format:
After the detection model is converted, there will be additional folders of `ppocr_det_mobile_2.0_serving` and `ppocr_det_mobile_2.0_client` in the current folder, with the following format:
```
|- ppocr_det_server_2.0_serving/
|- ppocr_det_mobile_2.0_serving/
|- __model__
|- __params__
|- serving_server_conf.prototxt
|- serving_server_conf.stream.prototxt
|- ppocr_det_server_2.0_client
|- ppocr_det_mobile_2.0_client
|- serving_client_conf.prototxt
|- serving_client_conf.stream.prototxt
......@@ -147,6 +141,89 @@ The recognition model is the same.
After successfully running, the predicted result of the model will be printed in the cmd window. An example of the result is:
![](./imgs/results.png)
Adjust the number of concurrency in config.yml to get the largest QPS. Generally, the number of concurrent detection and recognition is 2:1
```
det:
concurrency: 8
...
rec:
concurrency: 4
...
```
Multiple service requests can be sent at the same time if necessary.
The predicted performance data will be automatically written into the `PipelineServingLogs/pipeline.tracer` file.
Tested on 200 real pictures, and limited the detection long side to 960. The average QPS on T4 GPU can reach around 23:
```
2021-05-13 03:42:36,895 ==================== TRACER ======================
2021-05-13 03:42:36,975 Op(rec):
2021-05-13 03:42:36,976 in[14.472382882882883 ms]
2021-05-13 03:42:36,976 prep[9.556855855855856 ms]
2021-05-13 03:42:36,976 midp[59.921905405405404 ms]
2021-05-13 03:42:36,976 postp[15.345945945945946 ms]
2021-05-13 03:42:36,976 out[1.9921216216216215 ms]
2021-05-13 03:42:36,976 idle[0.16254943864471572]
2021-05-13 03:42:36,976 Op(det):
2021-05-13 03:42:36,976 in[315.4468035714286 ms]
2021-05-13 03:42:36,976 prep[69.5980625 ms]
2021-05-13 03:42:36,976 midp[18.989535714285715 ms]
2021-05-13 03:42:36,976 postp[18.857803571428573 ms]
2021-05-13 03:42:36,977 out[3.1337544642857145 ms]
2021-05-13 03:42:36,977 idle[0.7477961159203756]
2021-05-13 03:42:36,977 DAGExecutor:
2021-05-13 03:42:36,977 Query count[224]
2021-05-13 03:42:36,977 QPS[22.4 q/s]
2021-05-13 03:42:36,977 Succ[0.9910714285714286]
2021-05-13 03:42:36,977 Error req[169, 170]
2021-05-13 03:42:36,977 Latency:
2021-05-13 03:42:36,977 ave[535.1678348214285 ms]
2021-05-13 03:42:36,977 .50[172.651 ms]
2021-05-13 03:42:36,977 .60[187.904 ms]
2021-05-13 03:42:36,977 .70[245.675 ms]
2021-05-13 03:42:36,977 .80[526.684 ms]
2021-05-13 03:42:36,977 .90[854.596 ms]
2021-05-13 03:42:36,977 .95[1722.728 ms]
2021-05-13 03:42:36,977 .99[3990.292 ms]
2021-05-13 03:42:36,978 Channel (server worker num[10]):
2021-05-13 03:42:36,978 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
2021-05-13 03:42:36,979 chl1(In: ['det'], Out: ['rec']) size[6/0]
2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
```
## WINDOWS Users
Windows does not support Pipeline Serving, if we want to lauch paddle serving on Windows, we should use Web Service, for more infomation please refer to [Paddle Serving for Windows Users](https://github.com/PaddlePaddle/Serving/blob/develop/doc/WINDOWS_TUTORIAL.md)
**WINDOWS user can only use version 0.5.0 CPU Mode**
**Prepare Stage:**
```
pip3 install paddle-serving-server==0.5.0
pip3 install paddle-serving-app==0.3.1
```
1. Start Server
```
cd win
python3 ocr_web_server.py gpu(for gpu user)
or
python3 ocr_web_server.py cpu(for cpu user)
```
2. Client Send Requests
```
python3 ocr_web_client.py
```
<a name="faq"></a>
## FAQ
**Q1**: No result return after sending the request.
......
deploy/pdserving/README_CN.md
浏览文件 @ d977e550
......@@ -29,41 +29,31 @@ PaddleOCR提供2种服务部署方式：
需要准备PaddleOCR的运行环境和Paddle Serving的运行环境。
- 准备PaddleOCR的运行环境参考[链接](../../doc/doc_ch/installation.md)
- 准备PaddleOCR的运行环境[链接](../../doc/doc_ch/installation.md)
根据环境下载对应的paddle whl包，推荐安装2.0.1版本
- 准备PaddleServing的运行环境，步骤如下
1. 安装serving，用于启动服务
```
pip3 install paddle-serving-server==0.5.0 # for CPU
pip3 install paddle-serving-server-gpu==0.5.0 # for GPU
pip3 install paddle-serving-server==0.6.1 # for CPU
pip3 install paddle-serving-server-gpu==0.6.1 # for GPU
# 其他GPU环境需要确认环境再选择执行如下命令
pip3 install paddle-serving-server-gpu==0.5.0.post9 # GPU with CUDA9.0
pip3 install paddle-serving-server-gpu==0.5.0.post10 # GPU with CUDA10.0
pip3 install paddle-serving-server-gpu==0.5.0.post101 # GPU with CUDA10.1 + TensorRT6
pip3 install paddle-serving-server-gpu==0.5.0.post11 # GPU with CUDA10.1 + TensorRT7
pip3 install paddle-serving-server-gpu==0.6.1.post101 # GPU with CUDA10.1 + TensorRT6
pip3 install paddle-serving-server-gpu==0.6.1.post11 # GPU with CUDA11 + TensorRT7
```
2. 安装client，用于向服务发送请求
```
pip3 install paddle-serving-client==0.5.0 # for CPU
在[下载链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)中找到对应python版本的client安装包，这里推荐python3.7版本：
pip3 install paddle-serving-client-gpu==0.5.0 # for GPU
```
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.0.0-cp37-none-any.whl
pip3 install paddle_serving_client-0.0.0-cp37-none-any.whl
```
3. 安装serving-app
```
pip3 install paddle-serving-app==0.3.0
```
**note:** 安装0.3.0版本的serving-app后，为了能加载动态图模型，需要修改serving_app的源码，具体为：
```
# 找到paddle_serving_app的安装目录，找到并编辑local_predict.py文件
vim /usr/local/lib/python3.7/site-packages/paddle_serving_app/local_predict.py
# 将local_predict.py 的第85行 config = AnalysisConfig(model_path) 替换为：
if os.path.exists(os.path.join(model_path, "__params__")):
config = AnalysisConfig(os.path.join(model_path, "__model__"), os.path.join(model_path, "__params__"))
else:
config = AnalysisConfig(model_path)
pip3 install paddle-serving-app==0.6.1
```
**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)。
......@@ -76,38 +66,38 @@ PaddleOCR提供2种服务部署方式：
首先，下载PPOCR的[inference模型](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-20-series-model-listupdate-on-dec-15)
```
# 下载并解压 OCR 文本检测模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
# 下载并解压 OCR 文本识别模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
```
接下来，用安装的paddle_serving_client把下载的inference模型转换成易于server部署的模型格式。
```
# 转换检测模型
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_det_infer/ \
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_det_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./ppocr_det_server_2.0_serving/ \
--serving_client ./ppocr_det_server_2.0_client/
--serving_server ./ppocr_det_mobile_2.0_serving/ \
--serving_client ./ppocr_det_mobile_2.0_client/
# 转换识别模型
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_rec_infer/ \
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_rec_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./ppocr_rec_server_2.0_serving/ \
--serving_client ./ppocr_rec_server_2.0_client/
--serving_server ./ppocr_rec_mobile_2.0_serving/ \
--serving_client ./ppocr_rec_mobile_2.0_client/
```
检测模型转换完成后，会在当前文件夹多出`ppocr_det_server_2.0_serving` 和`ppocr_det_server_2.0_client`的文件夹，具备如下格式：
检测模型转换完成后，会在当前文件夹多出`ppocr_det_mobile_2.0_serving` 和`ppocr_det_mobile_2.0_client`的文件夹，具备如下格式：
```
|- ppocr_det_server_2.0_serving/
|- ppocr_det_mobile_2.0_serving/
|- __model__
|- __params__
|- serving_server_conf.prototxt
|- serving_server_conf.stream.prototxt
|- ppocr_det_server_2.0_client
|- ppocr_det_mobile_2.0_client
|- serving_client_conf.prototxt
|- serving_client_conf.stream.prototxt
......@@ -148,6 +138,86 @@ python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_rec_in
成功运行后，模型预测的结果会打印在cmd窗口中，结果示例为：
![](./imgs/results.png)
调整 config.yml 中的并发个数获得最大的QPS, 一般检测和识别的并发数为2：1
```
det:
#并发数，is_thread_op=True时，为线程并发；否则为进程并发
concurrency: 8
...
rec:
#并发数，is_thread_op=True时，为线程并发；否则为进程并发
concurrency: 4
...
```
有需要的话可以同时发送多个服务请求
预测性能数据会被自动写入 `PipelineServingLogs/pipeline.tracer` 文件中。
在200张真实图片上测试，把检测长边限制为960。T4 GPU 上 QPS 均值可达到23左右：
```
2021-05-13 03:42:36,895 ==================== TRACER ======================
2021-05-13 03:42:36,975 Op(rec):
2021-05-13 03:42:36,976 in[14.472382882882883 ms]
2021-05-13 03:42:36,976 prep[9.556855855855856 ms]
2021-05-13 03:42:36,976 midp[59.921905405405404 ms]
2021-05-13 03:42:36,976 postp[15.345945945945946 ms]
2021-05-13 03:42:36,976 out[1.9921216216216215 ms]
2021-05-13 03:42:36,976 idle[0.16254943864471572]
2021-05-13 03:42:36,976 Op(det):
2021-05-13 03:42:36,976 in[315.4468035714286 ms]
2021-05-13 03:42:36,976 prep[69.5980625 ms]
2021-05-13 03:42:36,976 midp[18.989535714285715 ms]
2021-05-13 03:42:36,976 postp[18.857803571428573 ms]
2021-05-13 03:42:36,977 out[3.1337544642857145 ms]
2021-05-13 03:42:36,977 idle[0.7477961159203756]
2021-05-13 03:42:36,977 DAGExecutor:
2021-05-13 03:42:36,977 Query count[224]
2021-05-13 03:42:36,977 QPS[22.4 q/s]
2021-05-13 03:42:36,977 Succ[0.9910714285714286]
2021-05-13 03:42:36,977 Error req[169, 170]
2021-05-13 03:42:36,977 Latency:
2021-05-13 03:42:36,977 ave[535.1678348214285 ms]
2021-05-13 03:42:36,977 .50[172.651 ms]
2021-05-13 03:42:36,977 .60[187.904 ms]
2021-05-13 03:42:36,977 .70[245.675 ms]
2021-05-13 03:42:36,977 .80[526.684 ms]
2021-05-13 03:42:36,977 .90[854.596 ms]
2021-05-13 03:42:36,977 .95[1722.728 ms]
2021-05-13 03:42:36,977 .99[3990.292 ms]
2021-05-13 03:42:36,978 Channel (server worker num[10]):
2021-05-13 03:42:36,978 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
2021-05-13 03:42:36,979 chl1(In: ['det'], Out: ['rec']) size[6/0]
2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
```
## WINDOWS用户
Windows用户不能使用上述的启动方式，需要使用Web Service，详情参见[Windows平台使用Paddle Serving指导](https://github.com/PaddlePaddle/Serving/blob/develop/doc/WINDOWS_TUTORIAL_CN.md)
**WINDOWS只能使用0.5.0版本的CPU模式**
准备阶段：
```
pip3 install paddle-serving-server==0.5.0
pip3 install paddle-serving-app==0.3.1
```
1. 启动服务端程序
```
cd win
python3 ocr_web_server.py gpu(使用gpu方式)
或者
python3 ocr_web_server.py cpu(使用cpu方式)
```
2. 发送服务请求
```
python3 ocr_web_client.py
```
<a name="FAQ"></a>
## FAQ
......
deploy/pdserving/config.yml
浏览文件 @ d977e550
#rpc端口, rpc_port和http_port不允许同时为空。当rpc_port为空且http_port不为空时，会自动将rpc_port设置为http_port+1
rpc_port: 18090
rpc_port: 18091
#http端口, rpc_port和http_port不允许同时为空。当rpc_port可用且http_port为空时，不自动生成http_port
http_port: 9999
http_port: 9998
#worker_num, 最大并发数。当build_dag_each_worker=True时, 框架会创建worker_num个进程，每个进程内构建grpcSever和DAG
##当build_dag_each_worker=False时，框架会设置主线程grpc线程池的max_workers=worker_num
worker_num: 20
worker_num: 10
#build_dag_each_worker, False，框架在进程内创建一条DAG；True，框架会每个进程内创建多个独立的DAG
build_dag_each_worker: false
build_dag_each_worker: False
dag:
#op资源类型, True, 为线程模型；False，为进程模型
is_thread_op: False
#重试次数
retry: 1
retry: 10
#使用性能分析, True，生成Timeline性能数据，对性能有一定影响；False为不使用
use_profile: False
use_profile: True
tracer:
interval_s: 10
op:
det:
#并发数，is_thread_op=True时，为线程并发；否则为进程并发
concurrency: 4
concurrency: 8
#当op配置没有server_endpoints时，从local_service_conf读取本地服务配置
local_service_conf:
......@@ -34,18 +34,18 @@ op:
client_type: local_predictor
#det模型路径
model_config: /paddle/serving/models/det_serving_server/ #ocr_det_model
model_config: ./ppocr_det_mobile_2.0_serving
#Fetch结果列表，以client_config中fetch_var的alias_name为准
fetch_list: ["save_infer_model/scale_0.tmp_1"]
#计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
devices: "2"
devices: "0"
ir_optim: True
rec:
#并发数，is_thread_op=True时，为线程并发；否则为进程并发
concurrency: 1
concurrency: 4
#超时时间, 单位ms
timeout: -1
......@@ -60,12 +60,12 @@ op:
client_type: local_predictor
#rec模型路径
model_config: /paddle/serving/models/rec_serving_server/ #ocr_rec_model
model_config: ./ppocr_rec_mobile_2.0_serving
#Fetch结果列表，以client_config中fetch_var的alias_name为准
fetch_list: ["save_infer_model/scale_0.tmp_1"] #["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
fetch_list: ["save_infer_model/scale_0.tmp_1"]
#计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
devices: "2"
devices: "0"
ir_optim: True
deploy/pdserving/ocr_reader.py
浏览文件 @ d977e550
......@@ -21,7 +21,6 @@ import sys
import argparse
import string
from copy import deepcopy
import paddle
class DetResizeForTest(object):
......@@ -34,12 +33,12 @@ class DetResizeForTest(object):
elif 'limit_side_len' in kwargs:
self.limit_side_len = kwargs['limit_side_len']
self.limit_type = kwargs.get('limit_type', 'min')
elif 'resize_long' in kwargs:
self.resize_type = 2
self.resize_long = kwargs.get('resize_long', 960)
else:
elif 'resize_short' in kwargs:
self.limit_side_len = 736
self.limit_type = 'min'
else:
self.resize_type = 2
self.resize_long = kwargs.get('resize_long', 960)
def __call__(self, data):
img = deepcopy(data)
......@@ -227,8 +226,6 @@ class CTCLabelDecode(BaseRecLabelDecode):
super(CTCLabelDecode, self).__init__(config)
def __call__(self, preds, label=None, *args, **kwargs):
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
preds_idx = preds.argmax(axis=2)
preds_prob = preds.max(axis=2)
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
......
deploy/pdserving/pipeline_http_client.py
浏览文件 @ d977e550
......@@ -23,8 +23,8 @@ def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
url = "http://127.0.0.1:9999/ocr/prediction"
test_img_dir = "../doc/imgs/"
url = "http://127.0.0.1:9998/ocr/prediction"
test_img_dir = "../../doc/imgs/"
for idx, img_file in enumerate(os.listdir(test_img_dir)):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data1 = file.read()
......@@ -36,5 +36,5 @@ for idx, img_file in enumerate(os.listdir(test_img_dir)):
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
test_img_dir = "../doc/imgs/"
test_img_dir = "../../doc/imgs/"
print("==> total number of test imgs: ", len(os.listdir(test_img_dir)))
deploy/pdserving/pipeline_rpc_client.py
浏览文件 @ d977e550
......@@ -23,7 +23,7 @@ import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:18090'])
client.connect(['127.0.0.1:18091'])
def cv2_to_base64(image):
......@@ -39,4 +39,3 @@ for img_file in os.listdir(test_img_dir):
for i in range(1):
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
print(ret)
#print(ret)
deploy/pdserving/web_service.py
浏览文件 @ d977e550
......@@ -11,10 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
try:
from paddle_serving_server_gpu.web_service import WebService, Op
except ImportError:
from paddle_serving_server.web_service import WebService, Op
from paddle_serving_server.web_service import WebService, Op
import logging
import numpy as np
......@@ -48,28 +45,24 @@ class DetOp(Op):
def preprocess(self, input_dicts, data_id, log_id):
(_, input_dict), = input_dicts.items()
data = base64.b64decode(input_dict["image"].encode('utf8'))
self.raw_im = data
data = np.fromstring(data, np.uint8)
# Note: class variables(self.var) can only be used in process op mode
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
self.im = im
self.ori_h, self.ori_w, _ = im.shape
det_img = self.det_preprocess(self.im)
det_img = self.det_preprocess(im)
_, self.new_h, self.new_w = det_img.shape
print("det image shape", det_img.shape)
return {"x": det_img[np.newaxis, :].copy()}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
print("input_dicts: ", input_dicts)
det_out = fetch_dict["save_infer_model/scale_0.tmp_1"]
ratio_list = [
float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
]
dt_boxes_list = self.post_func(det_out, [ratio_list])
dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
out_dict = {"dt_boxes": dt_boxes, "image": self.im}
out_dict = {"dt_boxes": dt_boxes, "image": self.raw_im}
print("out dict", out_dict["dt_boxes"])
return out_dict, None, ""
......@@ -83,35 +76,75 @@ class RecOp(Op):
def preprocess(self, input_dicts, data_id, log_id):
(_, input_dict), = input_dicts.items()
im = input_dict["image"]
raw_im = input_dict["image"]
data = np.frombuffer(raw_im, np.uint8)
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
dt_boxes = input_dict["dt_boxes"]
dt_boxes = self.sorted_boxes(dt_boxes)
feed_list = []
img_list = []
max_wh_ratio = 0
for i, dtbox in enumerate(dt_boxes):
boximg = self.get_rotate_crop_image(im, dt_boxes[i])
img_list.append(boximg)
h, w = boximg.shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
_, w, h = self.ocr_reader.resize_norm_img(img_list[0],
max_wh_ratio).shape
imgs = np.zeros((len(img_list), 3, w, h)).astype('float32')
for id, img in enumerate(img_list):
norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
imgs[id] = norm_img
print("rec image shape", imgs.shape)
feed = {"x": imgs.copy()}
return feed, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
rec_res = self.ocr_reader.postprocess(fetch_dict, with_score=True)
res_lst = []
for res in rec_res:
res_lst.append(res[0])
res = {"res": str(res_lst)}
## Many mini-batchs, the type of feed_data is list.
max_batch_size = 6 # len(dt_boxes)
# If max_batch_size is 0, skipping predict stage
if max_batch_size == 0:
return {}, True, None, ""
boxes_size = len(dt_boxes)
batch_size = boxes_size // max_batch_size
rem = boxes_size % max_batch_size
for bt_idx in range(0, batch_size + 1):
imgs = None
boxes_num_in_one_batch = 0
if bt_idx == batch_size:
if rem == 0:
continue
else:
boxes_num_in_one_batch = rem
elif bt_idx < batch_size:
boxes_num_in_one_batch = max_batch_size
else:
_LOGGER.error("batch_size error, bt_idx={}, batch_size={}".
format(bt_idx, batch_size))
break
start = bt_idx * max_batch_size
end = start + boxes_num_in_one_batch
img_list = []
for box_idx in range(start, end):
boximg = self.get_rotate_crop_image(im, dt_boxes[box_idx])
img_list.append(boximg)
h, w = boximg.shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
_, w, h = self.ocr_reader.resize_norm_img(img_list[0],
max_wh_ratio).shape
imgs = np.zeros((boxes_num_in_one_batch, 3, w, h)).astype('float32')
for id, img in enumerate(img_list):
norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
imgs[id] = norm_img
feed = {"x": imgs.copy()}
feed_list.append(feed)
return feed_list, False, None, ""
def postprocess(self, input_dicts, fetch_data, log_id):
res_list = []
if isinstance(fetch_data, dict):
if len(fetch_data) > 0:
rec_batch_res = self.ocr_reader.postprocess(
fetch_data, with_score=True)
for res in rec_batch_res:
res_list.append(res[0])
elif isinstance(fetch_data, list):
for one_batch in fetch_data:
one_batch_res = self.ocr_reader.postprocess(
one_batch, with_score=True)
for res in one_batch_res:
res_list.append(res[0])
res = {"res": str(res_list)}
return res, None, ""
......
deploy/pdserving/win/__init__.py 0 → 100644
浏览文件 @ d977e550
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
deploy/pdserving/win/ocr_web_client.py 0 → 100644
浏览文件 @ d977e550
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# -*- coding: utf-8 -*-
import requests
import json
import cv2
import base64
import os, sys
import time
def cv2_to_base64(image):
#data = cv2.imencode('.jpg', image)[1]
return base64.b64encode(image).decode(
'utf8') #data.tostring()).decode('utf8')
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:9292/ocr/prediction"
test_img_dir = "../../../doc/imgs/"
for idx, img_file in enumerate(os.listdir(test_img_dir)):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
for i in range(1):
data = {
"feed": [{
"image": image
}],
"fetch": ["save_infer_model/scale_0.tmp_1"]
}
r = requests.post(url=url, headers=headers, data=json.dumps(data))
print(r.json())
test_img_dir = "../../../doc/imgs/"
print("==> total number of test imgs: ", len(os.listdir(test_img_dir)))
deploy/pdserving/win/ocr_web_server.py 0 → 100644
浏览文件 @ d977e550
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle_serving_client import Client
import cv2
import sys
import numpy as np
import os
from paddle_serving_client import Client
from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
from paddle_serving_app.reader import Div, Normalize, Transpose
from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
from ocr_reader import OCRReader
from ..ocr_reader import OCRReader
try:
from paddle_serving_server_gpu.web_service import WebService
except ImportError:
from paddle_serving_server.web_service import WebService
from paddle_serving_app.local_predict import LocalPredictor
import time
import re
import base64
class OCRService(WebService):
def init_det_debugger(self, det_model_config):
self.det_preprocess = Sequential([
ResizeByFactor(32, 960), Div(255),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
(2, 0, 1))
])
self.det_client = LocalPredictor()
if sys.argv[1] == 'gpu':
self.det_client.load_model_config(
det_model_config, use_gpu=True, gpu_id=0)
elif sys.argv[1] == 'cpu':
self.det_client.load_model_config(det_model_config)
self.ocr_reader = OCRReader(
char_dict_path="../../../ppocr/utils/ppocr_keys_v1.txt")
def preprocess(self, feed=[], fetch=[]):
data = base64.b64decode(feed[0]["image"].encode('utf8'))
data = np.fromstring(data, np.uint8)
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
ori_h, ori_w, _ = im.shape
det_img = self.det_preprocess(im)
_, new_h, new_w = det_img.shape
det_img = det_img[np.newaxis, :]
det_img = det_img.copy()
det_out = self.det_client.predict(
feed={"x": det_img},
fetch=["save_infer_model/scale_0.tmp_1"],
batch=True)
filter_func = FilterBoxes(10, 10)
post_func = DBPostProcess({
"thresh": 0.3,
"box_thresh": 0.5,
"max_candidates": 1000,
"unclip_ratio": 1.5,
"min_size": 3
})
sorted_boxes = SortedBoxes()
ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
dt_boxes_list = post_func(det_out["save_infer_model/scale_0.tmp_1"],
[ratio_list])
dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
dt_boxes = sorted_boxes(dt_boxes)
get_rotate_crop_image = GetRotateCropImage()
img_list = []
max_wh_ratio = 0
for i, dtbox in enumerate(dt_boxes):
boximg = get_rotate_crop_image(im, dt_boxes[i])
img_list.append(boximg)
h, w = boximg.shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
if len(img_list) == 0:
return [], []
_, w, h = self.ocr_reader.resize_norm_img(img_list[0],
max_wh_ratio).shape
imgs = np.zeros((len(img_list), 3, w, h)).astype('float32')
for id, img in enumerate(img_list):
norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
imgs[id] = norm_img
feed = {"x": imgs.copy()}
fetch = ["save_infer_model/scale_0.tmp_1"]
return feed, fetch, True
def postprocess(self, feed={}, fetch=[], fetch_map=None):
rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
res_lst = []
for res in rec_res:
res_lst.append(res[0])
res = {"res": res_lst}
return res
ocr_service = OCRService(name="ocr")
ocr_service.load_model_config("../ppocr_rec_mobile_2.0_serving")
ocr_service.prepare_server(workdir="workdir", port=9292)
ocr_service.init_det_debugger(
det_model_config="../ppocr_det_mobile_2.0_serving")
if sys.argv[1] == 'gpu':
ocr_service.set_gpus("0")
ocr_service.run_debugger_service(gpu=True)
elif sys.argv[1] == 'cpu':
ocr_service.run_debugger_service()
ocr_service.run_web_service()
deploy/slim/prune/README.md
浏览文件 @ d977e550
......@@ -23,13 +23,13 @@
```bash
git clone https://github.com/PaddlePaddle/PaddleSlim.git
cd PaddleSlim
git checkout develop
cd Paddleslim
python3 setup.py install
```
### 2. 获取预训练模型
模型裁剪需要加载事先训练好的模型，PaddleOCR也提供了一系列(模型)[../../../doc/doc_ch/models_list.md]，开发者可根据需要自行选择模型或使用自己的模型。
模型裁剪需要加载事先训练好的模型，PaddleOCR也提供了一系列[模型](../../../doc/doc_ch/models_list.md)，开发者可根据需要自行选择模型或使用自己的模型。
### 3. 敏感度分析训练
......@@ -49,14 +49,14 @@ python3 setup.py install
进入PaddleOCR根目录，通过以下命令对模型进行敏感度分析训练：
```bash
python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"
python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model" Global.save_model_dir=./output/prune_model/
```
### 4. 导出模型、预测部署
在得到裁剪训练保存的模型后，我们可以将其导出为inference_model：
```bash
pytho3.7 deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy Global.save_inference_dir=inference_model
pytho3.7 deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy Global.save_inference_dir=./prune/prune_inference_model
```
inference model的预测和部署参考：
......
deploy/slim/prune/README_en.md
浏览文件 @ d977e550
......@@ -22,15 +22,15 @@ Five steps for OCR model prune:
```bash
git clone https://github.com/PaddlePaddle/PaddleSlim.git
cd PaddleSlim
git checkout develop
cd Paddleslim
python3 setup.py install
```
### 2. Download Pretrain Model
Model prune needs to load pre-trained models.
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
......@@ -54,7 +54,7 @@ Enter the PaddleOCR root directory，perform sensitivity analysis on the model w
```bash
python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"
python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model" Global.save_model_dir=./output/prune_model/
```
......@@ -63,7 +63,7 @@ python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_
We can export the pruned model as inference_model for deployment:
```bash
python deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy Global.save_inference_dir=inference_model
python deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy Global.save_inference_dir=./prune/prune_inference_model
```
Reference for prediction and deployment of inference model:
......
deploy/slim/quantization/README.md
浏览文件 @ d977e550
......@@ -23,7 +23,7 @@
```bash
git clone https://github.com/PaddlePaddle/PaddleSlim.git
cd Paddleslim
cd PaddleSlim
python setup.py install
```
......@@ -37,12 +37,12 @@ PaddleOCR提供了一系列训练好的[模型](../../../doc/doc_ch/models_list.
量化训练的代码位于slim/quantization/quant.py 中，比如训练检测模型，训练指令如下：
```bash
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model='your trained model' Global.save_model_dir=./output/quant_model
python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model='your trained model' Global.save_model_dir=./output/quant_model
# 比如下载提供的训练模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
tar -xf ch_ppocr_mobile_v2.0_det_train.tar
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_inference_dir=./output/quant_inference_model
python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_inference_model
```
如果要训练识别模型的量化，修改配置文件和加载的模型参数即可。
......@@ -52,7 +52,7 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global
在得到量化训练保存的模型后，我们可以将其导出为inference_model，用于预测部署：
```bash
python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
python deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model
```
### 5. 量化模型部署
......
deploy/slim/quantization/README_en.md
浏览文件 @ d977e550
......@@ -26,7 +26,7 @@ After training, if you want to further compress the model size and accelerate th
```bash
git clone https://github.com/PaddlePaddle/PaddleSlim.git
cd Paddleslim
cd PaddlSlim
python setup.py install
```
......@@ -43,12 +43,12 @@ 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:
```bash
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model='your trained model' Global.save_model_dir=./output/quant_model
python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model='your trained model' Global.save_model_dir=./output/quant_model
# download provided model
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
tar -xf ch_ppocr_mobile_v2.0_det_train.tar
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model
python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model
```
......@@ -57,7 +57,7 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global
After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment:
```bash
python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model
python deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model
```
### 5. Deploy
......
deploy/slim/quantization/quant.py
浏览文件 @ d977e550
......@@ -112,10 +112,6 @@ def main(config, device, logger, vdl_writer):
config['Architecture']["Head"]['out_channels'] = char_num
model = build_model(config['Architecture'])
# prepare to quant
quanter = QAT(config=quant_config, act_preprocess=PACT)
quanter.quantize(model)
if config['Global']['distributed']:
model = paddle.DataParallel(model)
......@@ -136,31 +132,15 @@ def main(config, device, logger, vdl_writer):
logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
format(len(train_dataloader), len(valid_dataloader)))
quanter = QAT(config=quant_config, act_preprocess=PACT)
quanter.quantize(model)
# start train
program.train(config, train_dataloader, valid_dataloader, device, model,
loss_class, optimizer, lr_scheduler, post_process_class,
eval_class, pre_best_model_dict, logger, vdl_writer)
def test_reader(config, device, logger):
loader = build_dataloader(config, 'Train', device, logger)
import time
starttime = time.time()
count = 0
try:
for data in loader():
count += 1
if count % 1 == 0:
batch_time = time.time() - starttime
starttime = time.time()
logger.info("reader: {}, {}, {}".format(
count, len(data[0]), batch_time))
except Exception as e:
logger.info(e)
logger.info("finish reader: {}, Success!".format(count))
if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess(is_train=True)
main(config, device, logger, vdl_writer)
# test_reader(config, device, logger)
doc/doc_ch/FAQ.md
浏览文件 @ d977e550
......@@ -9,14 +9,14 @@
## PaddleOCR常见问题汇总(持续更新)
* [近期更新（2021.5.17）](#近期更新)
* [近期更新（2021.5.24）](#近期更新)
* [【精选】OCR精选10个问题](#OCR精选10个问题)
* [【理论篇】OCR通用43个问题](#OCR通用问题)
* [【理论篇】OCR通用44个问题](#OCR通用问题)
* [基础知识13题](#基础知识)
* [数据集9题](#数据集2)
* [模型训练调优21题](#模型训练调优2)
* [【实战篇】PaddleOCR实战170个问题](#PaddleOCR实战问题)
* [使用咨询68题](#使用咨询)
* [模型训练调优22题](#模型训练调优2)
* [【实战篇】PaddleOCR实战174个问题](#PaddleOCR实战问题)
* [使用咨询72题](#使用咨询)
* [数据集18题](#数据集3)
* [模型训练调优36题](#模型训练调优3)
* [预测部署48题](#预测部署3)
......@@ -24,38 +24,34 @@
<a name="近期更新"></a>
## 近期更新（2021.5.17）
### Q3.1.66: iaa里面添加的数据增强方式，是每张图像训练都会做增强还是随机的？如何添加一个数据增强方法？
### Q2.3.22: 目前知识蒸馏有哪些主要的实践思路？
**A**：iaa增强的训练配置参考：[链接](https://github.com/PaddlePaddle/PaddleOCR/blob/0ccc1720c252beb277b9e522a1b228eb6abffb8a/configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml#L82)
其中{ 'type': Fliplr, 'args': { 'p': 0.5 } } p是概率。新增数据增强，可以参考这个[方法](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.1/doc/doc_ch/add_new_algorithm.md#%E6%95%B0%E6%8D%AE%E5%8A%A0%E8%BD%BD%E5%92%8C%E5%A4%84%E7%90%86)
**A**：知识蒸馏即利用教师模型指导学生模型的训练，目前有3种主要的蒸馏思路：
1. 基于输出结果的蒸馏，即让学生模型学习教师模型的软标签（分类或者OCR识别等任务中）或者概率热度图（分割等任务中）。
2. 基于特征图的蒸馏，即让学生模型学习教师模型中间层的特征图，拟合中间层的一些特征。
3. 基于关系的蒸馏，针对不同的样本（假设个数为N），教师模型会有不同的输出，那么可以基于不同样本的输出，计算一个NxN的相关性矩阵，可以让学生模型去学习教师模型关于不同样本的相关性矩阵。
### Q3.1.67: PGNet训练中文弯曲数据集，可视化时弯曲文本无法显示。
当然，知识蒸馏方法日新月异，也欢迎大家提出更多的总结与建议。
**A**: 可能是因为安装的OpenCV里，cv2.putText不能显示中文的原因，可以尝试用Pillow来添加显示中文，需要改draw_e2e_res函数里面的代码，可以参考如下代码：
```
box = box.astype(np.int32).reshape((-1, 1, 2))
cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
### Q3.1.69: 怎么加速训练过程呢？
from PIL import ImageFont, ImageDraw, Image
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(img)
fontStyle = ImageFont.truetype(
"font/msyh.ttc", 16, encoding="utf-8")
draw.text((int(box[0, 0, 0]), int(box[0, 0, 1])), text, (0, 255, 0), font=fontStyle)
**A**：OCR模型训练过程中一般包含大量的数据增广，这些数据增广是比较耗时的，因此可以离线生成大量增广后的图像，直接送入网络进行训练，机器资源充足的情况下，也可以使用分布式训练的方法，可以参考[分布式训练教程文档](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/distributed_training.md)。
src_im= cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
```
### Q3.1.68: 用PGNet做进行端到端训练时，数据集标注的点的个数必须都是统一一样的吗? 能不能随意标点数，只要能够按顺时针从左上角开始标这样?
**A**: 目前代码要求标注为统一的点数。
### Q3.1.70: 文字识别模型模型的输出矩阵需要进行解码才能得到识别的文本。代码中实现为preds_idx = preds.argmax(axis=2)，也就是最佳路径解码法。这是一种贪心算法，是每一个时间步只将最大概率的字符作为当前时间步的预测输出，但得到的结果不一定是最好的。为什么不使用beam search这种方式进行解码呢？
#### Q3.4.47: 请教如何优化检测阶段时长?
**A**：实验发现，使用贪心的方法去做解码，识别精度影响不大，但是速度方面的优势比较明显，因此PaddleOCR中使用贪心算法去做识别的解码。
**A**: 预测单张图会慢一点，如果批量预测，第一张图比较慢，后面就快了，因为最开始一些初始化操作比较耗时。服务部署的话，访问一次后，后面再访问就不会初始化了，推理的话每次都需要初始化的。
### Q3.1.71: 遇到中英文识别模型不支持的字符，该如何对模型做微调？
### Q3.4.48: paddle serving 本地启动调用失败,怎么判断是否正常工作？
**A**：如果希望识别中英文识别模型中不支持的字符，需要更新识别的字典，并完成微调过程。比如说如果希望模型能够进一步识别罗马数字，可以按照以下步骤完成模型微调过程。
1. 准备中英文识别数据以及罗马数字的识别数据，用于训练，同时保证罗马数字和中英文识别数字的效果；
2. 修改默认的字典文件，在后面添加罗马数字的字符；
3. 下载PaddleOCR提供的预训练模型，配置预训练模型和数据的路径，开始训练。
**A**：没有打印出预测结果，说明启动失败。可以参考这篇文档重新配置下动态图的paddle serving：https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/deploy/pdserving/README_CN.md
### Q3.1.72: 文字识别主要有CRNN和Attention两种方式，但是在我们的说明文档中，CRNN有对应的论文，但是Attention没看到，这个具体在哪里呢？
**A**：文字识别主要有CTC和Attention两种方式，基于CTC的算法有CRNN、Rosetta、StarNet，基于Attention的方法有RARE、其他的算法PaddleOCR里没有提供复现代码。论文的链接可以参考：[PaddleOCR文本识别算法教程文档](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/algorithm_overview.md#%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95)
......@@ -337,9 +333,19 @@ src_im= cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
#### Q2.3.20: 如何根据不同的硬件平台选用不同的backbone？
**A**：在不同的硬件上，不同的backbone的速度优势不同，可以根据不同平台的速度-精度图来确定backbone，这里可以参考[PaddleClas模型速度-精度图](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.0/docs/zh_CN/models)。
#### Q2.3.21: 端到端算法PGNet是否支持中文识别，速度会很慢嘛？
#### Q2.3.21: 端到端算法PGNet是否支持中文识别，速度会很慢嘛？
**A**：目前开源的PGNet算法模型主要是用于检测英文数字，对于中文的识别需要自己训练，大家可以使用开源的端到端中文数据集，而对于复杂文本（弯曲文本）的识别，也可以自己构造一批数据集针对进行训练，对于推理速度，可以先将模型转换为inference再进行预测，速度应该会相当可观。
### Q2.3.22: 目前知识蒸馏有哪些主要的实践思路？
**A**：知识蒸馏即利用教师模型指导学生模型的训练，目前有3种主要的蒸馏思路：
1. 基于输出结果的蒸馏，即让学生模型学习教师模型的软标签（分类或者OCR识别等任务中）或者概率热度图（分割等任务中）。
2. 基于特征图的蒸馏，即让学生模型学习教师模型中间层的特征图，拟合中间层的一些特征。
3. 基于关系的蒸馏，针对不同的样本（假设个数为N），教师模型会有不同的输出，那么可以基于不同样本的输出，计算一个NxN的相关性矩阵，可以让学生模型去学习教师模型关于不同样本的相关性矩阵。
当然，知识蒸馏方法日新月异，也欢迎大家提出更多的总结与建议。
<a name="PaddleOCR实战问题"></a>
## 【实战篇】PaddleOCR实战问题
......@@ -693,6 +699,27 @@ src_im= cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
**A**: 目前代码要求标注为统一的点数。
### Q3.1.69: 怎么加速训练过程呢？
**A**：OCR模型训练过程中一般包含大量的数据增广，这些数据增广是比较耗时的，因此可以离线生成大量增广后的图像，直接送入网络进行训练，机器资源充足的情况下，也可以使用分布式训练的方法，可以参考[分布式训练教程文档](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/distributed_training.md)。
### Q3.1.70: 文字识别模型模型的输出矩阵需要进行解码才能得到识别的文本。代码中实现为preds_idx = preds.argmax(axis=2)，也就是最佳路径解码法。这是一种贪心算法，是每一个时间步只将最大概率的字符作为当前时间步的预测输出，但得到的结果不一定是最好的。为什么不使用beam search这种方式进行解码呢？
**A**：实验发现，使用贪心的方法去做解码，识别精度影响不大，但是速度方面的优势比较明显，因此PaddleOCR中使用贪心算法去做识别的解码。
### Q3.1.71: 遇到中英文识别模型不支持的字符，该如何对模型做微调？
**A**：如果希望识别中英文识别模型中不支持的字符，需要更新识别的字典，并完成微调过程。比如说如果希望模型能够进一步识别罗马数字，可以按照以下步骤完成模型微调过程。
1. 准备中英文识别数据以及罗马数字的识别数据，用于训练，同时保证罗马数字和中英文识别数字的效果；
2. 修改默认的字典文件，在后面添加罗马数字的字符；
3. 下载PaddleOCR提供的预训练模型，配置预训练模型和数据的路径，开始训练。
### Q3.1.72: 文字识别主要有CRNN和Attention两种方式，但是在我们的说明文档中，CRNN有对应的论文，但是Attention没看到，这个具体在哪里呢？
**A**：文字识别主要有CTC和Attention两种方式，基于CTC的算法有CRNN、Rosetta、StarNet，基于Attention的方法有RARE、其他的算法PaddleOCR里没有提供复现代码。论文的链接可以参考：[PaddleOCR文本识别算法教程文档](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/algorithm_overview.md#%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95)
<a name="数据集3"></a>
### 数据集
......
doc/doc_ch/models_list.md
浏览文件 @ d977e550
## OCR模型列表（V2.0，2021年1月20日更新）
> **说明**
> 1. 2.0版模型和[1.1版模型](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md)的主要区别在于动态图训练vs.静态图训练，模型性能上无明显差距。
> 1. 2.0版模型和[1.1版模型](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md) 的主要区别在于动态图训练vs.静态图训练，模型性能上无明显差距。
> 2. 本文档提供的是PPOCR自研模型列表，更多基于公开数据集的算法介绍与预训练模型可以参考：[算法概览文档](./algorithm_overview.md)。
......@@ -99,20 +99,23 @@ python3 generate_multi_language_configs.py -l it \
-o Global.use_gpu=False
```
|模型名称|模型简介|配置文件|推理模型大小|下载地址|
| --- | --- | --- | --- | --- |
| french_mobile_v2.0_rec |法文识别|[rec_french_lite_train.yml](../../configs/rec/multi_language/rec_french_lite_train.yml)|2.65M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar) |
| german_mobile_v2.0_rec |德文识别|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
| korean_mobile_v2.0_rec |韩文识别|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
| japan_mobile_v2.0_rec |日文识别|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
| chinese_cht_mobile_v2.0_rec |中文繁体识别|rec_chinese_cht_lite_train.yml|5.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) |
| te_mobile_v2.0_rec |泰卢固文识别|rec_te_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_train.tar) |
| ka_mobile_v2.0_rec |卡纳达文识别|rec_ka_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_train.tar) |
| ta_mobile_v2.0_rec |泰米尔文识别|rec_ta_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_train.tar) |
| latin_mobile_v2.0_rec | 拉丁文识别 | [rec_latin_lite_train.yml](../../configs/rec/multi_language/rec_latin_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_train.tar) |
| arabic_mobile_v2.0_rec | 阿拉伯字母 | [rec_arabic_lite_train.yml](../../configs/rec/multi_language/rec_arabic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_train.tar) |
| cyrillic_mobile_v2.0_rec | 斯拉夫字母 | [rec_cyrillic_lite_train.yml](../../configs/rec/multi_language/rec_cyrillic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_train.tar) |
| devanagari_mobile_v2.0_rec | 梵文字母 | [rec_devanagari_lite_train.yml](../../configs/rec/multi_language/rec_devanagari_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_train.tar) |
<a name="多语言模型与配置文件"></a>
##### 3. 多语言模型与配置文件
|模型名称|字典文件|模型简介|配置文件|推理模型大小|下载地址|
| --- | --- | --- | --- |--- | --- |
| french_mobile_v2.0_rec | ppocr/utils/dict/french_dict.txt |法文识别|[rec_french_lite_train.yml](../../configs/rec/multi_language/rec_french_lite_train.yml)|2.65M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar) |
| german_mobile_v2.0_rec | ppocr/utils/dict/german_dict.txt |德文识别|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
| korean_mobile_v2.0_rec | ppocr/utils/dict/korean_dict.txt |韩文识别|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
| japan_mobile_v2.0_rec | ppocr/utils/dict/japan_dict.txt |日文识别|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
| chinese_cht_mobile_v2.0_rec | ppocr/utils/dict/chinese_cht_dict.txt | 中文繁体识别|rec_chinese_cht_lite_train.yml|5.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) |
| te_mobile_v2.0_rec | ppocr/utils/dict/te_dict.txt | 泰卢固文识别|rec_te_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_train.tar) |
| ka_mobile_v2.0_rec | ppocr/utils/dict/ka_dict.txt |卡纳达文识别|rec_ka_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_train.tar) |
| ta_mobile_v2.0_rec | ppocr/utils/dict/ta_dict.txt |泰米尔文识别|rec_ta_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_train.tar) |
| latin_mobile_v2.0_rec | ppocr/utils/dict/latin_dict.txt | 拉丁文识别 | [rec_latin_lite_train.yml](../../configs/rec/multi_language/rec_latin_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_train.tar) |
| arabic_mobile_v2.0_rec | ppocr/utils/dict/arabic_dict.txt | 阿拉伯字母 | [rec_arabic_lite_train.yml](../../configs/rec/multi_language/rec_arabic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_train.tar) |
| cyrillic_mobile_v2.0_rec | ppocr/utils/dict/cyrillic_dict.txt | 斯拉夫字母 | [rec_cyrillic_lite_train.yml](../../configs/rec/multi_language/rec_cyrillic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_train.tar) |
| devanagari_mobile_v2.0_rec | ppocr/utils/dict/devanagari_dict.txt |梵文字母 | [rec_devanagari_lite_train.yml](../../configs/rec/multi_language/rec_devanagari_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_train.tar) |
更多支持语种请参考: [多语言模型](./multi_languages.md)
......
doc/doc_ch/multi_languages.md
浏览文件 @ d977e550
......@@ -183,11 +183,28 @@ ppocr 支持使用自己的数据进行自定义训练或finetune, 其中识别
## 4 预测部署
除了安装whl包进行快速预测，ppocr 也提供了多种预测部署方式，如有需求可阅读相关文档：
部署教程默认使用中文模型，如需使用其他语种模型，请自行替换模型文件和字典：
|模型名称|字典文件|模型简介|配置文件|下载地址|
| --- | --- | --- | --- | --- |
| french_mobile_v2.0_rec | ppocr/utils/dict/french_dict.txt | 法文识别 | [rec_french_lite_train.yml](../../configs/rec/multi_language/rec_french_lite_train.yml) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar) |
| german_mobile_v2.0_rec | ppocr/utils/dict/german_dict.txt |德文识别|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
| korean_mobile_v2.0_rec | ppocr/utils/dict/korean_dict.txt |韩文识别|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
| japan_mobile_v2.0_rec | ppocr/utils/dict/japan_dict.txt |日文识别|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
| chinese_cht_mobile_v2.0_rec | ppocr/utils/dict/chinese_cht_dict.txt | 中文繁体识别|rec_chinese_cht_lite_train.yml|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) |
| te_mobile_v2.0_rec | ppocr/utils/dict/te_dict.txt | 泰卢固文识别|rec_te_lite_train.yml|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_train.tar) |
| ka_mobile_v2.0_rec | ppocr/utils/dict/ka_dict.txt |卡纳达文识别|rec_ka_lite_train.yml|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_train.tar) |
| ta_mobile_v2.0_rec | ppocr/utils/dict/ta_dict.txt |泰米尔文识别|rec_ta_lite_train.yml|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_train.tar) |
| latin_mobile_v2.0_rec | ppocr/utils/dict/latin_dict.txt | 拉丁文识别 | [rec_latin_lite_train.yml](../../configs/rec/multi_language/rec_latin_lite_train.yml) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_train.tar) |
| arabic_mobile_v2.0_rec | ppocr/utils/dict/arabic_dict.txt | 阿拉伯字母 | [rec_arabic_lite_train.yml](../../configs/rec/multi_language/rec_arabic_lite_train.yml) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_train.tar) |
| cyrillic_mobile_v2.0_rec | ppocr/utils/dict/cyrillic_dict.txt | 斯拉夫字母 | [rec_cyrillic_lite_train.yml](../../configs/rec/multi_language/rec_cyrillic_lite_train.yml) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_train.tar) |
| devanagari_mobile_v2.0_rec | ppocr/utils/dict/devanagari_dict.txt |梵文字母 | [rec_devanagari_lite_train.yml](../../configs/rec/multi_language/rec_devanagari_lite_train.yml) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_train.tar) |
- [基于Python脚本预测引擎推理](./inference.md)
- [基于C++预测引擎推理](../../deploy/cpp_infer/readme.md)
- [服务化部署](../../deploy/hubserving/readme.md)
- [端侧部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md)
- [Benchmark](./benchmark.md)
......
doc/doc_en/inference_en.md
浏览文件 @ d977e550
# Reasoning based on Python prediction engine
# INFERING BASED ON PYTHON PREDICTION ENGINE
The inference model (the model saved by `paddle.jit.save`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
......@@ -15,34 +15,33 @@ Next, we first introduce how to convert a trained model into an inference model,
- [Convert recognition model to inference model](#Convert_recognition_model)
- [Convert angle classification model to inference model](#Convert_angle_class_model)
- [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
- [1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE](#LIGHTWEIGHT_DETECTION)
- [2. DB TEXT DETECTION MODEL INFERENCE](#DB_DETECTION)
- [3. EAST TEXT DETECTION MODEL INFERENCE](#EAST_DETECTION)
- [4. SAST TEXT DETECTION MODEL INFERENCE](#SAST_DETECTION)
- [5. Multilingual model inference](#Multilingual model inference)
- [1. Lightweight Chinese Detection Model Inference](#LIGHTWEIGHT_DETECTION)
- [2. DB Text Detection Model Inference](#DB_DETECTION)
- [3. EAST Text Detection Model Inference](#EAST_DETECTION)
- [4. SAST Text Detection Model Inference](#SAST_DETECTION)
- [5. Multilingual Model Inference](#MULTILINGUAL_MODEL_INFERENCE)
- [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
- [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
- [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
- [3. SRN-BASED TEXT RECOGNITION MODEL INFERENCE](#SRN-BASED_RECOGNITION)
- [3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
- [4. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
- [1. Lightweight Chinese Model](#LIGHTWEIGHT_RECOGNITION)
- [2. CTC-BASED Text Recognition Model Inference](#CTC-BASED_RECOGNITION)
- [3. SRN-BASED Text Recognition Model Inference](#SRN-BASED_RECOGNITION)
- [3. Text Recognition Model Inference Using Custom Characters Dictionary](#USING_CUSTOM_CHARACTERS)
- [4. Multilingual Model Inference](#MULTILINGUAL_MODEL_INFERENCE)
- [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
- [1. ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
- [1. Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE)
- [TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
- [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_CHINESE_MODEL)
- [2. OTHER MODELS](#OTHER_MODELS)
- [1. Lightweight Chinese Model](#LIGHTWEIGHT_CHINESE_MODEL)
- [2. Other Models](#OTHER_MODELS)
<a name="CONVERT"></a>
## CONVERT TRAINING MODEL TO INFERENCE MODEL
<a name="Convert_detection_model"></a>
### Convert detection model to inference model
Download the lightweight Chinese detection model:
Download the lightweight Chinese_en detection model:
```
wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar -C ./ch_lite/
```
......@@ -69,9 +68,9 @@ inference/det_db/
<a name="Convert_recognition_model"></a>
### Convert recognition model to inference model
Download the lightweight Chinese recognition model:
Download the lightweight English recognition model:
```
wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar -C ./ch_lite/
wget -P ./en_lite/ ttps://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_train.tar && tar xf ./en_lite/en_number_mobile_v2.0_rec_train.tar -C ./en_lite/
```
The recognition model is converted to the inference model in the same way as the detection, as follows:
......@@ -81,14 +80,14 @@ The recognition model is converted to the inference model in the same way as the
# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
# Global.save_inference_dir Set the address where the converted model will be saved.
python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn/
python3 tools/export_model.py -c configs/rec/multi_language/rec_en_number_lite_train.yml -o Global.pretrained_model=./en_lite/en_number_mobile_v2.0_rec_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn/
```
If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
After the conversion is successful, there are three files in the model save directory:
```
inference/det_db/
inference/rec_crnn/
├── inference.pdiparams # The parameter file of recognition inference model
├── inference.pdiparams.info # The parameter information of recognition inference model, which can be ignored
└── inference.pdmodel # The program file of recognition model
......@@ -137,7 +136,7 @@ For lightweight Chinese detection model inference, you can execute the following
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
tar xf ch_ppocr_mobile_v2.0_det_infer.tar
# predict
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/"
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="ch_ppocr_mobile_v2.0_det_infer/"
```
The visual 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:
......@@ -145,12 +144,12 @@ The visual text detection results are saved to the ./inference_results folder by
![](../imgs_results/det_res_00018069.jpg)
You can use the parameters `limit_type` and `det_limit_side_len` to limit the size of the input image,
The optional parameters of `limit_type` are [`max`, `min`], and
The optional parameters of `limit_type` is `max` or `min` and
`det_limit_size_len` is a positive integer, generally set to a multiple of 32, such as 960.
The default setting of the parameters is `limit_type='max', det_limit_side_len=960`. Indicates that the longest side of the network input image cannot exceed 960,
If this value is exceeded, the image will be resized with the same width ratio to ensure that the longest side is `det_limit_side_len`.
Set as `limit_type='min', det_limit_side_len=960`, it means that the shortest side of the image is limited to 960.
Set as `limit_type='min', det_limit_side_len=960` it means that the shortest side of the image is limited to 960.
If the resolution of the input picture is relatively large and you want to use a larger resolution prediction, you can set det_limit_side_len to the desired value, such as 1216:
```
......@@ -249,15 +248,15 @@ The following will introduce the lightweight Chinese recognition model inference
<a name="LIGHTWEIGHT_RECOGNITION"></a>
### 1. LIGHTWEIGHT CHINESE TEXT RECOGNITION MODEL REFERENCE
### 1. LIGHTWEIGHT ENGLISH TEXT RECOGNITION MODEL REFERENCE
For lightweight Chinese recognition model inference, you can execute the following commands:
```
# download CRNN text recognition inference model
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar
tar xf en_number_mobile_v2.0_rec_infer.tar
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="en_number_mobile_v2.0_rec_infer" --rec_char_dict_path='ppocr/utils/en_dict.txt'
```
![](../imgs_words_en/word_10.png)
......@@ -388,7 +387,6 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --de
# use multi-process
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
```
```
After executing the command, the recognition result image is as follows:
......
doc/doc_en/models_list_en.md
浏览文件 @ d977e550
......@@ -97,20 +97,20 @@ python3 generate_multi_language_configs.py -l it \
--data_dir {path/to/data_dir} \
-o Global.use_gpu=False
```
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
| french_mobile_v2.0_rec |Lightweight model for French recognition|[rec_french_lite_train.yml](../../configs/rec/multi_language/rec_french_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar) |
| german_mobile_v2.0_rec |Lightweight model for German recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
| korean_mobile_v2.0_rec |Lightweight model for Korean recognition|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
| japan_mobile_v2.0_rec |Lightweight model for Japanese recognition|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
| chinese_cht_mobile_v2.0_rec |Lightweight model for chinese cht recognition|rec_chinese_cht_lite_train.yml|5.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) |
| te_mobile_v2.0_rec |Lightweight model for Telugu recognition|rec_te_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_train.tar) |
| ka_mobile_v2.0_rec |Lightweight model for Kannada recognition|rec_ka_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_train.tar) |
| ta_mobile_v2.0_rec |Lightweight model for Tamil recognition|rec_ta_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_train.tar) |
| latin_mobile_v2.0_rec | Lightweight model for latin recognition | [rec_latin_lite_train.yml](../../configs/rec/multi_language/rec_latin_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_train.tar) |
| arabic_mobile_v2.0_rec | Lightweight model for arabic recognition | [rec_arabic_lite_train.yml](../../configs/rec/multi_language/rec_arabic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_train.tar) |
| cyrillic_mobile_v2.0_rec | Lightweight model for cyrillic recognition | [rec_cyrillic_lite_train.yml](../../configs/rec/multi_language/rec_cyrillic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_train.tar) |
| devanagari_mobile_v2.0_rec | Lightweight model for devanagari recognition | [rec_devanagari_lite_train.yml](../../configs/rec/multi_language/rec_devanagari_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_train.tar) |
|model name| dict file | description|config|model size|download|
| --- | --- | --- |--- | --- | --- |
| french_mobile_v2.0_rec | ppocr/utils/dict/french_dict.txt | Lightweight model for French recognition|[rec_french_lite_train.yml](../../configs/rec/multi_language/rec_french_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar) |
| german_mobile_v2.0_rec | ppocr/utils/dict/german_dict.txt | Lightweight model for German recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
| korean_mobile_v2.0_rec | ppocr/utils/dict/korean_dict.txt | Lightweight model for Korean recognition|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
| japan_mobile_v2.0_rec | ppocr/utils/dict/japan_dict.txt | Lightweight model for Japanese recognition|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
| chinese_cht_mobile_v2.0_rec | ppocr/utils/dict/chinese_cht_dict.txt | Lightweight model for chinese cht recognition|rec_chinese_cht_lite_train.yml|5.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) |
| te_mobile_v2.0_rec | ppocr/utils/dict/te_dict.txt | Lightweight model for Telugu recognition|rec_te_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_train.tar) |
| ka_mobile_v2.0_rec | ppocr/utils/dict/ka_dict.txt | Lightweight model for Kannada recognition|rec_ka_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_train.tar) |
| ta_mobile_v2.0_rec | ppocr/utils/dict/ta_dict.txt | Lightweight model for Tamil recognition|rec_ta_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_train.tar) |
| latin_mobile_v2.0_rec | ppocr/utils/dict/latin_dict.txt | Lightweight model for latin recognition | [rec_latin_lite_train.yml](../../configs/rec/multi_language/rec_latin_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_train.tar) |
| arabic_mobile_v2.0_rec | ppocr/utils/dict/arabic_dict.txt | Lightweight model for arabic recognition | [rec_arabic_lite_train.yml](../../configs/rec/multi_language/rec_arabic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_train.tar) |
| cyrillic_mobile_v2.0_rec | ppocr/utils/dict/cyrillic_dict.txt | Lightweight model for cyrillic recognition | [rec_cyrillic_lite_train.yml](../../configs/rec/multi_language/rec_cyrillic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_train.tar) |
| devanagari_mobile_v2.0_rec | ppocr/utils/dict/devanagari_dict.txt | Lightweight model for devanagari recognition | [rec_devanagari_lite_train.yml](../../configs/rec/multi_language/rec_devanagari_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_train.tar) |
For more supported languages, please refer to : [Multi-language model](./multi_languages_en.md)
......
doc/doc_en/multi_languages_en.md
浏览文件 @ d977e550
......@@ -41,6 +41,7 @@ This document will briefly introduce how to use the multilingual model.
<a name="Install"></a>
## 1 Installation
PaddleOCR provides two installation methods
<a name="paddle_install"></a>
### 1.1 paddle installation
```
......@@ -66,12 +67,12 @@ pip3 install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x is the version number
```
<a name="Quick_use"></a>
## 2 Quick use
## 2 Quick Use
<a name="Command_line_operation"></a>
### 2.1 Command line operation
### 2.1 Command Line Operation
View help information
* View help information
```
paddleocr -h
......@@ -79,8 +80,8 @@ paddleocr -h
* Whole image prediction (detection + recognition)
Paddleocr currently supports 80 languages, which can be switched by modifying the --lang parameter.
The specific supported [language] (#language_abbreviations) can be viewed in the table.
PaddleOCR currently supports 80 languages, which can be switched by modifying the --lang parameter.
The specific supported [language](#language_abbreviations) can be viewed in the table.
``` bash
paddleocr --image_dir doc/imgs_en/254.jpg --lang=en
......@@ -132,9 +133,9 @@ The result is a list, each item contains only text boxes
```
<a name="python_script_running"></a>
### 2.2 python script running
### 2.2 Python Script Running
ppocr also supports running in python scripts for easy embedding in your own code:
PaddleOCR also supports running in python scripts for easy embedding in your own code:
* Whole image prediction (detection + recognition)
......@@ -158,7 +159,7 @@ image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/korean.ttf')
im_show = draw_ocr(image, boxes, txts, scores, font_path='doc/fonts/korean.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
......@@ -167,12 +168,12 @@ Visualization of results:
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.1/doc/imgs_results/korean.jpg)
ppocr also supports direction classification. For more usage methods, please refer to: [whl package instructions](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.0/doc/doc_ch/whl.md).
ppocr also supports direction classification. For more usage methods, please refer to: [whl package instructions](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.0/doc/doc_en/whl_en.md).
<a name="Custom_training"></a>
## 3 Custom training
## 3 Custom Training
ppocr supports using your own data for custom training or finetune, where the recognition model can refer to [French configuration file](../../configs/rec/multi_language/rec_french_lite_train.yml)
PaddleOCR supports using your own data for custom training or finetune, where the configuration file can refer to [French model](../../configs/rec/multi_language/rec_french_lite_train.yml)
Modify the training data path, dictionary and other parameters.
For specific data preparation and training process, please refer to: [Text Detection](../doc_en/detection_en.md), [Text Recognition](../doc_en/recognition_en.md), more functions such as predictive deployment,
......@@ -185,12 +186,29 @@ For functions such as data annotation, you can read the complete [Document Tutor
In addition to installing the whl package for quick forecasting,
ppocr also provides a variety of forecasting deployment methods.
If necessary, you can read related documents:
- [Python Inference](./inference_en.md)
- [C++ Inference](../../deploy/cpp_infer/readme_en.md)
- [Serving](../../deploy/hubserving/readme_en.md)
- [Mobile](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme_en.md)
- [Benchmark](./benchmark_en.md)
The deployment tutorial uses the Chinese model by default. If you need to use other language models,
please replace the model files and dictionaries yourself:
|model name| dict file | description|config|model size|download|
| --- | --- | --- |--- | --- | --- |
| french_mobile_v2.0_rec | ppocr/utils/dict/french_dict.txt | Lightweight model for French recognition|[rec_french_lite_train.yml](../../configs/rec/multi_language/rec_french_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar) |
| german_mobile_v2.0_rec | ppocr/utils/dict/german_dict.txt | Lightweight model for German recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
| korean_mobile_v2.0_rec | ppocr/utils/dict/korean_dict.txt | Lightweight model for Korean recognition|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
| japan_mobile_v2.0_rec | ppocr/utils/dict/japan_dict.txt | Lightweight model for Japanese recognition|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
| chinese_cht_mobile_v2.0_rec | ppocr/utils/dict/chinese_cht_dict.txt | Lightweight model for chinese cht recognition|rec_chinese_cht_lite_train.yml|5.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) |
| te_mobile_v2.0_rec | ppocr/utils/dict/te_dict.txt | Lightweight model for Telugu recognition|rec_te_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_train.tar) |
| ka_mobile_v2.0_rec | ppocr/utils/dict/ka_dict.txt | Lightweight model for Kannada recognition|rec_ka_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_train.tar) |
| ta_mobile_v2.0_rec | ppocr/utils/dict/ta_dict.txt | Lightweight model for Tamil recognition|rec_ta_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_train.tar) |
| latin_mobile_v2.0_rec | ppocr/utils/dict/latin_dict.txt | Lightweight model for latin recognition | [rec_latin_lite_train.yml](../../configs/rec/multi_language/rec_latin_lite_train.yml) |2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_train.tar) |
| arabic_mobile_v2.0_rec | ppocr/utils/dict/arabic_dict.txt | Lightweight model for arabic recognition | [rec_arabic_lite_train.yml](../../configs/rec/multi_language/rec_arabic_lite_train.yml) |2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_train.tar) |
| cyrillic_mobile_v2.0_rec | ppocr/utils/dict/cyrillic_dict.txt | Lightweight model for cyrillic recognition | [rec_cyrillic_lite_train.yml](../../configs/rec/multi_language/rec_cyrillic_lite_train.yml) |2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_train.tar) |
| devanagari_mobile_v2.0_rec | ppocr/utils/dict/devanagari_dict.txt | Lightweight model for devanagari recognition | [rec_devanagari_lite_train.yml](../../configs/rec/multi_language/rec_devanagari_lite_train.yml) |2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_train.tar) |
<a name="language_abbreviations"></a>
......
doc/doc_en/quickstart_en.md
浏览文件 @ d977e550
......@@ -9,7 +9,7 @@ Please refer to [quick installation](./installation_en.md) to configure the Padd
## 2. Inference Models
The detection and recognition models on the mobile and server sides are as follows. For more models (including multiple languages), please refer to [PP-OCR v2.0 series model list](../doc_en/models_list.md)
The detection and recognition models on the mobile and server sides are as follows. For more models (including multiple languages), please refer to [PP-OCR v2.0 series model list](./models_list_en.md)
| Model introduction | Model name | Recommended scene | Detection model | Direction Classifier | Recognition model |
| ------------ | --------------- | ----------------|---- | ---------- | -------- |
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或