Models

Machine learning models for AIY kits

Machine learning is a technique for building software models that can make predictions based on patterns and relationships that have been discovered in data. Experiment with these models to see machine learning in action.

Did you build your own model? We’re accepting contributions for future projects. Please share with us.

Face Detector

The Face Detector model locates and identifies faces from an image. It also provides a “joy score” for each face.

Supported device: Vision Kit
Learn More

Dog / Cat / Human Detector

The Dog / Cat / Human Detector can identify whether there’s a dog, cat, or person in an image and draw a box around the identified objects. It’s based on the MobileNet model architecture.

Supported device: Vision Kit
Learn More

Dish Classifier

The Dish Classifier model is designed to identify food in an image. It’s based on the MobileNet model architecture and trained to recognize over 2,000 types of food.

Supported device: Vision Kit
Learn More

Image Classifier

The Image Classifier demo is designed to identify 1,000 different types of objects. This demo can use either the SqueezeNet model or Google's MobileNet model architecture.

Supported device: Vision Kit
Learn More

Nature Explorer

Nature explorer has 3 machine learning models based on MobileNet, trained on photos contributed by the iNaturalist community. These models are built to recognize 4,080 different species (~960 birds, ~1020 insects, ~2100 plants).

In collaboration with iNaturalist
Supported device: Vision Kit
Learn More