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How does Google Coral help human in our world?

Coral technology is enabling a new generation of intelligent devices
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.
How does Google Coral help human and make the world more better? Here are some examples:
✅Virgo uses AI to fight colon cancer
Colorectal cancer is the second most lethal cancer after breast cancer. Virgo uses Coral in its endoscopic video system to help doctors spot abnormalities in colonoscopies.
✅Olea Edge Analytics helps conserve water with Coral
Olea Edge, is helping water utilities and businesses conserve water by adding smarts to commercial water meters using Coral’s AI-at-the-edge platform.
✅Keeping loved ones safe with Coral and care.ai
Care.ai deploys computer vision to detect falls and other dangers. A vital factor in its success; Coral's AI platform, which keeps processing local, providing safety while preserving patient privacy.
Google Coral Performance
The Edge TPU is capable of performing 4 trillion operations (tera-operations) per second (TOPS), using 0.5 watts for each TOPS (2 TOPS per watt).
Flexibility and scalability
Google offers the Edge TPU in multiple form factors to suit various prototyping and production environments—from embedded systems deployed in the field, to network systems operating on-premise.
For example, our USB Accelerator simply plugs into a desktop, laptop, or embedded system such as a Raspberry Pi so you can quickly prototype your application. From there, you can scale to production systems by adding our Mini PCIe or M.2 Accelerator to your hardware system.
If you're looking for a fully-integrated system, you can get started with our Dev Board—a single-board computer based on NXP's i.MX 8M system-on-chip. Then you can scale to production by connecting our System-on-Module (included on the Dev Board) to your own baseboard.

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How to Retrain an object detection model

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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...
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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...
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