# Coral USB Accelerator

**Type:** Product page · **SKU:** SS114991790 · **Brand:** [Seeed Studio](https://core-electronics.com.au/brands/seeed-studio-australia)
**Page:** https://core-electronics.com.au/coral-usb-accelerator.html ([markdown](https://core-electronics.com.au/coral-usb-accelerator.html.md))

## Retired product

This product is retired and no longer available for purchase. Replaced by: WS-27812.

**Replacement:** https://core-electronics.com.au/catalog/product/view/sku/WS-27812

## Description

The Coral USB accelerator brings machine learning inferencing to existing systems. Works with Raspberry Pi and other Linux systems.

**Features**

- **Performs high-speed ML inferencing:** High-speed TensorFlow Lite inferencing with low power, small footprint, local inferencing
- **Supports all major platforms:** Connects via USB 3.0 Type-C to any system running Debian Linux (including Raspberry Pi), macOS, or Windows 10
- **Supports TensorFlow Lite:** no need to build models from the ground up. Tensorflow Lite models can be compiled to run on the edge TPE
- **Supports [AutoML Vision Edge](https://web.archive.org/web/20230315130625/https://cloud.google.com/vision/automl/docs/edge-quickstart)**: easily build and deploy fast, high-accuracy custom image classification models at the edge.
- **Compatible with Google Cloud**

The on-board Edge TPU is a small ASIC designed by Google that accelerates TensorFlow Lite models in a power-efficient manner: it's capable of performing 4 trillion operations per second (4 TOPS), using 2 watts of power—that's 2 TOPS per watt. For example, one Edge TPU can execute state-of-the-art mobile vision models such as MobileNet v2 at almost 400 frames per second. This on-device ML processing reduces latency, increases data privacy, and removes the need for a constant internet connection.

This allows you to add fast ML inferencing to your embedded AI devices in a power-efficient and privacy-preserving way. Models can be developed in TensorFlow Lite and then compiled to run on the USB Accelerator.

**Application**

**AI-enabled NVR system**

If you are planning to use Coral USB Accelerator for [Home Assistant](https://web.archive.org/web/20230315130625/https://www.home-assistant.io/) of home automation applications, we recommend [Odyssey Blue](https://web.archive.org/web/20230315130625/https://www.seeedstudio.com/Odyssey-Blue-J4125-128GB-p-4921.html), an Intel Celeron J4125 powered X86 Windows/Linux mini PC, you can set them together with ip cameras for **[a local AI processed NVR system. ](https://web.archive.org/web/20230315130625/https://www.seeedstudio.com/Frigate-NVR-with-Odyssey-Blue-and-Coral-USB-Accelerator.html)**

[Frigate](https://web.archive.org/web/20230315130625/https://frigate.video/) is a completely open source and local NVR designed for Home Assistant with AI-powered object detection. It uses OpenCV and Tensorflow to perform real-time object detection locally for IP cameras. It brings a rich set of features including **video recording, re-streaming, and motion detection, and supports multiprocessing.**

**Object tracking with video**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).

[View on GitHub](https://web.archive.org/web/20230315130625/https://github.com/google-coral/example-object-tracker)

**Image recognition with video**

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.

[View on GitHub](https://web.archive.org/web/20230315130625/https://github.com/google-coral/examples-camera)

**PoseNet pose estimation with video**

Use the PoseNet model to detect human poses from images and video, such as locating the position of someone’s elbow, shoulder, or foot.[View on GitHub](https://web.archive.org/web/20230315130625/https://github.com/google-coral/project-posenet)

**System requirements**

- A computer with one of the following operating systems:
    - Linux Debian 10, or a derivative thereof (such as Ubuntu 18.04), and system architecture of either x86-64, Armv7 (32-bit), or Armv8 (64-bit) (Raspberry Pi is supported, but Seeed have only tested Raspberry Pi 3 Model B+ and Raspberry Pi 4)
    - macOS 10.15, with either MacPorts or Homebrew installed
    - Windows 10
- One available USB port (for the best performance, use a USB 3.0 port)
- Python 3.5, 3.6, or 3.7

**Resources**

- [**Datasheet**](https://coral.withgoogle.com/tutorials/accelerator-datasheet/)
- [**Get started guide**](https://coral.withgoogle.com/tutorials/accelerator/)

## Images

- [Product image 1](https://core-electronics.com.au/media/catalog/product/S/S/SS114991790-1.jpg)