Digital Meter Reader with Grove - Vision AI
This tutorial will demonstrate the development process of digital meter reader using SSCMA based on Grove - Vision AI module.
TIP
Before starting, we recommend that you should read Grove - Deploy first.
Preparation
Please refer to Grove - Deploy - Prerequisites.
Train Model
The meter reading feature is based on the SWIFT-YOLO model, in this step you need a SWIFT-YOLO model weight with the suffix .pth, you have two ways to get the model weight.
Download the pre-trained model from our Model Zoo.
Refer to Training - SWIFT-YOLO Models to train the SWIFT-YOLO model and get the model weights using PyTorch and SSCMA by yourself.
Export Model
Since the trained model is not suitable for running directly on edge computing devices, we need to export it to a TFLite format with a .tflite suffix, and you have two ways to get the exported model (with model weights contained).
Download the exported TFLite model from our Model Zoo.
Refer to Export - PyTorch to TFLite to convert the SWIFT-YOLO model from PyTorch format to TFLite format by yourself.
Deploy Model
This is the last and most important step to complete the meter reading, in this step you need to compile and flash the firmware to the Grove - Vision AI modules. Please refer to Grove - Deployment - Compile and Deploy to complete the deployment of the model.
Run Example
After completing the Grove - Deployment Tutorial - Compile and Deploy - Deployment Routines, you need to open Grove Vision AI Console.
The above steps are graphically indicated in the console, and finally, you can see the real-time meter reading results as shown in the figure below.
