Coral USB Accelerator - Buy Online in India

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.

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: easily build and deploy fast, high-accuracy custom image classification models at the edge.
• Compatible with Google Cloud

Application

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, and motion detection, and supports multiprocessing.

Code examples and project tutorials to build intelligent devices with Coral

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).

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

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

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 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