Appliances

Agriculture

Food recognition

Image classification on high-performance MCU.

Food recognition Food recognition
Food recognition Food recognition
Start with the Function Pack Read the tutorial

Appliances

Agriculture

STM32Cube.AI

Image classification

Vision

Start with the Function Pack Read the tutorial
Food recognition can be used in a wide range of applications, such as home appliances (smart fridges, microwave ovens), restaurants, hospitals, or in the food industry. Based on a FD-MobileNet model, the application can recognize 18 different types of food and beverages including pizza, beer, and fries, among many others.

Approach

- We used of a camera module (B-CAMS-OMV) to capture the scene 
- We selected a pre-trained FD-Mobilenet NN model to perform food recognition
- This model is already integrated in the function pack FP-AI-VISION1 (made for STM32H747 discovery kit)
- The model was then optimized using STM32Cube.AI

Sensor

Vision: camera module bundle (reference: B-CAMS-OMV)

Data

Data format
- 18 classes: "Apple Pie", "Beer", "Caesar Salad", "Cappuccino", "Cheesecake", "Chicken Wings", "Chocolate Cake", "Coke", "Cup Cakes", "Donuts", "French Fries", "Hamburger", "Hot Dog", "Lasagna", "Pizza", "Risotto", "Spaghetti Bolognese", "Steak" 
- RGB color image 

Results

We provide two different networks, which offer a specific trade-off between inference time and accuracy. 
Model: Standard Convolutional Neural Network quantized
Input size: 224x224x3
Memory footprint:
132 KB Flash for weights
148 KBRAM for activations
Accuracy: 72.8%
Performance on STM32H747 (High-Perf) @ 400 MHz
Inference time: 79 ms
Frame rate: 11.8 fps
Model: Optimized Convolutional Neural Network quantized
Input size: 224x224x3
Memory footprint:
148 KB Flash for weights
199 KBRAM for activations
Accuracy: 77,5%
Performance on STM32H747 (High-Perf) @ 400 MHz
Inference time: 145 ms
Frame rate: 6.6 fps
On-board validation summary of information for a food recognition example 

Optimized with

STM32Cube.AI

Optimized with

Compatible with

STM32H7 series

Compatible with

Resources

Optimized with STM32Cube.AI

A free STM32Cube expansion package, X-CUBE-AI allows developers to convert pretrained AI algorithms automatically, such as neural network and machine learning models, into optimized C code for STM32.

Optimized with STM32Cube.AI

Compatible with STM32H7 series

The STM32 family of 32-bit microcontrollers based on the Arm Cortex®-M processor is designed to offer new degrees of freedom to MCU users. It offers products combining very high performance, real-time capabilities, digital signal processing, low-power / low-voltage operation, and connectivity, while maintaining full integration and ease of development.

Compatible with STM32H7 series

You also might be interested by

Agriculture | Appliances

Coffee bean recognition

Classify coffee beans thanks to an image classification model from STM32 Model Zoo running on a STM32H7 microcontroller.

Agriculture

Monitoring quality in a food production line

Create an AI model that predicts the quality of processed food instead of measuring it.

Appliances

Accurately measure the weight of clothes inside a washing machine

Using AI to make your home appliances “smarter” and more energy efficient for a sustainable future.