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Google Coral Serial Products are in stock at Gravitylink!

Google Coral provides a complete platform for accelerating neural networks on embedded devices. At the heart of our accelerators is the Edge TPU coprocessor. It's a small-yet-mighty, low-power ASIC that provides high performance neutral net inferencing.



For startups and large-scale enterprises: 
Google Coral Products flexible for prototyping and production

Prototype
Imagine what’s possible with local AI, then move right into proof-of-concept. Google Coral Edge TPU devices make it easy to take your idea from sketch pad to workbench.

Scale
Google Coral Edge TPU devices are designed to grow with you, integrating seamlessly into production processes at any scale.

Deploy
Robust modular components bring reliable AI to more applications than you ever dreamed possible.

Coral is a complete toolkit to build products with local AI. Our on-device inferencing capabilities allow you to build products that are efficient, private, fast and offline.




Google Coral all hardware is available at Gravitylink online store: https://store.gravitylink.com/global

Gravitylink is a global volume distributor of Google Coral.


If you are interested in Google Coral products, and more details about large volume or bulk sales (Volume Discounts) , welcome to contact our sales team via email: sales@gravitylink.com or market@gravitylink.com, and we'll get back to you with our best quotation ASAP. 


Coral’s local AI technology enables new possibilities across almost any kind of industry

Coral for smart cities: Building smarter cities


In order to reach the true potential of a “smart city,” AI processing will have to move from the cloud onto secure local devices, maintaining individual privacy, reducing data transfer rates, and enabling quicker reaction times for critical systems.

Coral for industry & manufacturing: Making manufacturing work better


Edge AI use cases in industries are wide ranging, from quality control in manufacturing lines to safety monitoring of human-machine interaction. These applications need fast, low latency inference without compromising on accuracy.

Coral for automotive: Helping people get around



Local AI not only helps vehicles drive more safely, it is also used to ensure driver attentiveness, provide seamless control of on-board systems, and verify the smooth operation of countless moving parts under the hood.

Coral for healthcare: Keeping people healthy


From patients to providers, local AI can help reduce healthcare costs by enabling more accurate diagnoses, reducing preventable accidents, and improving compliance. Edge devices can also extend healthcare to underserved populations where medical infrastructure might not exist.

Coral for agriculture: Feeding the world’s population


Local AI provides high performance, offline analysis for agriculture challenges that can improve soil quality, plant health, and crop yield in order to produce more food, reduce environmental impact, and enable sustainable farming practices.


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How to Retrain an object detection model

This tutorial shows you how to retrain an object detection model to recognize a new set of classes. You'll use a technique called transfer learning to retrain an existing model and then compile it to run on an Edge TPU device—you can use the retrained model with either the Coral Dev Board or the Coral USB Accelerator. Specifically, this tutorial shows you how to retrain a MobileNet V1 SSD model (originally trained to detect 90 objects from the COCO dataset) so that it detects two pets: Abyssinian cats and American Bulldogs (from the Oxford-IIIT Pets Dataset). But you can reuse these procedures with your own image dataset, and with a different pre-trained model. The steps below show you how to perform transfer-learning using either last-layers-only or full-model retraining. Most of the steps are the same; just keep an eye out for the different commands depending on the technique you desire. Note: These instructions do not require deep experience with TensorFlow o...

How to retrain an image classification model?

Got a tutorial from Google Coral Team: This tutorial shows you how to retrain an image classification model to recognize a new set of classes. You'll use a technique called transfer learning to retrain an existing model and then compile it to run on an Edge TPU device—you can use the retrained model with either the Coral Dev Board or the Coral USB Accelerator. Specifically, this tutorial shows you how to retrain a  quantized  MobileNet V1 model to recognize different types of flowers (adopted from TensorFlow's docs). But you can reuse these procedures with your own image dataset, and with a different pre-trained model. Tip:  If you want a shortcut to train an image classification model, try Cloud AutoML Vision. It's a web-based tool that allows you to train a model with your own images, optimize it, and then export it for the Edge TPU. Set up the Docker container Prepare your dataset Retrain your classification model Compile the model for the Edge TPU Run...

Introducing Google Coral Edge TPU Device--Mini PCIe Accelerator

Mini PCIe Accelerator A PCIe device that enables easy integration of the Edge TPU into existing systems. Supported host OS: Debian Linux Half-size Mini PCIe form factor Supported Framework: TensorFlow Lite Works with AutoML Vision Edge https://store.gravitylink.com/global/product/miniPcIe The Coral Mini PCIe Accelerator is a PCIe module that brings the Edge TPU coprocessor to existing systems and products. The Mini PCIe Accelerator is a half-size Mini PCIe card designed to fit in any standard Mini PCIe slot. This form-factor enables easy integration into ARM and x86 platforms so you can add local ML acceleration to products such as embedded platforms, mini-PCs, and industrial gateways. https://store.gravitylink.com/global/product/miniPcIe Features Google Edge TPU ML accelerator Standard Half-Mini PCIe card Supports Debian Linux and other variants on host CPU About Edge TPU  The Edge TPU is a small ASIC designed by Google that provi...