Botroniq

Coral USB Accelerator

Coral USB Accelerator

₹12,649 (incl. GST) · Out of stock

Brand: Botroniq · Category: Raspberry Pi Accessories

Description

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.

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. 7

Specifications

Brand
Botroniq
SKU
DTIFC00179

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