This section provides hands-on instructions for you to deploy pre-trained PaddlePaddle models on the Corstone-300 Fixed Virtual Platform (FVP) included with Arm Virtual Hardware.
The steps involved in the model deployment are shown in the figure below:
Start by launching the Arm Virtual Hardware AMI .
Start by cloning the code repository on your running AVH AMI instance:
git clone https://github.com/ArmDeveloperEcosystem/Paddle-examples-for-AVH.git cd Paddle-examples-for-AVH/OCR-example/Text-recognition-example
In this directory, there is a script named run_demo.sh that automates the entire process described in the End-to-end workflow diagram.
run_demo.sh script automatically builds and executes the English text recognition application on the Corstone-300 platform included with Arm Virtual Hardware. Here is a list of steps performed by this script:
Training the model usually takes a lot of time. In step 2, an already trained English text recognition model named ocr_en.tar is used.
By default, the script uses the image shown below (QBHOUSE) as an example to verify the inference results on the Corstone-300 FVP with Arm Cortex-M55.
You can now run the trained PaddleOCR text recognition model on the Corstone-300 FVP included on the AVH AMI with the following command:
The output from running the application on the Corstone-300 FVP is shown below:
Ethos-U rev 136b7d75 --- Feb 16 2022 15:47:15 (C) COPYRIGHT 2019-2022 Arm Limited ALL RIGHTS RESERVED Starting ocr rec inference text: QBHOUSE, score: 0.986746 EXITTHESIM Info: /OSCI/SystemC: Simulation stopped by user.
The text recognition results are consistent with the input image text content
QBHOUSE and has a high confidence score of 0.986746.
You have successfully deployed a PP-OCRv3 English recognition model directly on the Corstone-300 FVP with the Arm Cortex-M55.