- Improve user experience: prompt for the number of files and labels, optimize interaction, and fix bugs such as only use CPU when inference
- New functions: Support using `C` or `X` to rotate box.
- 2021.11.17:
- Support install and start PPOCRLabel through the whl package (by [d2623587501](https://github.com/d2623587501))
- Dataset segmentation: Divide the annotation file into training, verification and testing parts (refer to section 3.5 below, by [MrCuiHao](https://github.com/MrCuiHao))
@@ -33,6 +33,8 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
PaddleOCR support a variety of cutting-edge algorithms related to OCR, and developed industrial featured models/solution [PP-OCR](./doc/doc_en/ppocr_introduction_en.md) and [PP-Structure](./ppstructure/README.md) on this basis, and get through the whole process of data production, model training, compression, inference and deployment.
PaddleOCR also supports metric and model logging during training to [VisualDL](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/03_VisualDL/visualdl_usage_en.html) and [Weights & Biases](https://docs.wandb.ai/).
> It is recommended to start with the “quick experience” in the document tutorial
You need to prepare a Jetson development hardware. If you need TensorRT, you need to prepare the TensorRT environment. It is recommended to use TensorRT version 7.1.3;
The PaddlePaddle download [link](https://www.paddlepaddle.org.cn/inference/user_guides/download_lib.html#python)
Please select the appropriate installation package for your Jetpack version, cuda version, and trt version. Here, we download paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl.
*Note: Jetson hardware CPU is poor, dependency installation is slow, please wait patiently*
## 2. Perform prediction
## 2. 执行预测
Obtain the PPOCR model from the [document](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/ppocr_introduction_en.md#6-model-zoo) model library. The following takes the PP-OCRv3 model as an example to introduce the use of the PPOCR model on Jetson:
After executing the command, the predicted information will be printed out in the terminal, and the visualization results will be saved in the`./inference_results/`directory.
After executing the command, the predicted information will be printed out in the terminal, and the visualization results will be saved in the`./inference_results/`directory.
开启TRT预测只需要在以上命令基础上设置`--use_tensorrt=True`即可：
To enable TRT prediction, you only need to set `--use_tensorrt=True` on the basis of the above command:
This section introduces the deployment of PaddleOCR on Jetson NX, TX2, nano, AGX and other series of hardware.
本节介绍PaddleOCR在Jetson NX、TX2、nano、AGX等系列硬件的部署。
## 1. Prepare Environment
## 1. 环境准备
You need to prepare a Jetson development hardware. If you need TensorRT, you need to prepare the TensorRT environment. It is recommended to use TensorRT version 7.1.3;
The PaddlePaddle download [link](https://www.paddlepaddle.org.cn/inference/user_guides/download_lib.html#python)
Please select the appropriate installation package for your Jetpack version, cuda version, and trt version. Here, we download paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl.
*Note: Jetson hardware CPU is poor, dependency installation is slow, please wait patiently*
*注：jetson硬件CPU较差，依赖安装较慢，请耐心等待*
## 2. Perform prediction
Obtain the PPOCR model from the [document](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/ppocr_introduction_en.md#6-model-zoo) model library. The following takes the PP-OCRv3 model as an example to introduce the use of the PPOCR model on Jetson:
After executing the command, the predicted information will be printed out in the terminal, and the visualization results will be saved in the`./inference_results/`directory.
After executing the command, the predicted information will be printed out in the terminal, and the visualization results will be saved in the`./inference_results/`directory.
If you need the deployment tutorial of academic algorithm models other than PP-OCR, please directly enter the main page of corresponding algorithms, [entrance](../doc/doc_en/algorithm_overview_en.md)。
