Coral USB Accelerator
Note: Considering the very limited supply of Coral USB Accelerator at this stage, only the Frigate NVR bundle kit is available online now. We will open the sale of Coral USB Accelerator when the supply is back to normal.
Coral USB accelerator is a USB accessory that brings machine learning inferencing to existing systems. Works with Raspberry Pi and other Linux systems.
Considering the very limited supply of Coral USB Accelerator at this stage, only the Frigate NVR bundle kit is available online now. We will open the sale of the Coral USB Accelerator when the supply is back to normal.
Coral is a division of Google, that helps you build intelligent ideas with TensorFlow Lite for local AI. The Coral USB Accelerator brings powerful ML inferencing capabilities to existing systems. Works with Linux, Mac, and Windows systems simply by connecting it to a USB port.
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: 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.
AI-enabled NVR system
If you are planning to use Coral USB Accelerator for Home Assistant of home automation applications, we recommend Odyssey Blue, 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.
Frigate 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, motion detection, and supports multiprocessing.
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).
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.
PoseNet pose estimation with video
- 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 we 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
Google Edge TPU coprocessor:
4 TOPS (int8); 2 TOPS per watt
USB 3.0 Type-C* (data/power)
65 mm x 30 mm
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1 x Coral USB Accelerator