Examples

This example shows how to use the high-performance MoveNet model to detect human poses from images, and can be used with the high-speed "lighting" model or high-accuracy "thunder" model.

Multiple examples showing how to stream images from a camera and run classification or detection models with the TensorFlow Lite API. Each example uses a different camera library, such as GStreamer, OpenCV, PyGame, and PiCamera.

This example takes a camera feed and tracks each uniquely identified object, assigning each object with a persistent ID. The example detection script allows you to specify the tracker program you want to use (the Sort tracker is included).

This example takes in a camera feed and performs body-part segmentation using the BodyPix model (with both MobileNet v1 and ResNet50 backbones). In addition to identifying different body parts, it can anonymize people from images.

Multiple examples showing how to use the PoseNet model to detect human poses from images and video, such as locating the position of someone’s elbow, shoulder or foot.

This example performs semantic segmentation on an image. It takes an image as input and creates a new version of that image showing which pixels correspond to each recognized object.

A few examples using a keyphrase detection model that can detect over 140 short phrases such as "start game" and "next song." Includes a snake game and a YouTube player that respond to voice commands.

An example showing how to pipeline a model across multiple Edge TPUs, allowing you to significantly increase throughput for large models such as Inception.

An example that performs image classification with a single photo.

An example that performs object detection with a photo and draws a square around each object. Also works with face detection models.

This tutorial shows you how to take a trained model built in PerceptiLabs and export it for compatibility with Coral.

This tutorial teaches you how to deploy a Coral Dev Board with a pre-trained sound categorization model, and use a balenaCloud backend to manually review the classifications.

This example uses balenaCloud to deploy an object detection model to a Dev Board and view live inferencing from a web page.

This uses MediaPipe to perform real-time object detection with your camera.

This uses MediaPipe to perform real-time face detection with your camera.

An open source AI experiment that introduces the basics of machine learning by helping you build a teachable object using a Raspberry Pi Zero and the Coral USB Accelerator.

A physical machine that you can teach to rapidly recognize and sort objects using your own custom machine learning models.

A smart bird feeder that uses an image classification model to identify birds, record animal visits, and deter squirrels from stealing bird seed.

A Raspberry Pi-based robot that chases bananas. This project uses some basic object tracking software and a pre-trained object detection model.

A machine that can quickly learn to recognize new objects by re-training a vision classification model directly on your device.

An implementation of AlphaGo Zero called Minigo, which uses machine learning to play the strategy board game "go" at expert levels.