Mask Detection with Grove - Vision AI
This tutorial will demonstrate the development process of mask detection 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 mask detection feature is based on the FOMO model, in this step you need a FOMO 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 - FOMO Models to train the FOMO 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 FOMO model from PyTorch format to TFLite format by yourself.
Deploy Model
This is the last and most important step to complete the mask detection, in this step you need to compile and flash the firmware to the Grove - Vision AI module. Please refer to Grove - Deployment - Compile and Deploy to complete the deployment of the model.