google-coral · GitHub
https://github.com/google-coralSmall code snippets that show how to stream camera images to a Coral device. Python 224 73. project-bodypix Public. BodyPix model demo application for Google Coral. Python 195 40. tflite Public. Examples using TensorFlow Lite API to run inference on …
Coral
https://coral.aiCoral is a complete toolkit to build products with local AI. Our on-device inferencing capabilities allow you to build products that are efficient, private, ...
USB Accelerator | Coral
https://coral.ai/products/acceleratorThe Coral USB Accelerator adds an Edge TPU coprocessor to your system, enabling high-speed machine learning inferencing on a wide range of systems, simply by connecting it to a USB port. The on-board Edge TPU coprocessor is capable of performing 4 trillion operations (tera-operations) per second (TOPS), using 0.5 watts for each TOPS (2 TOPS per ...
Google Coral for Computer Vision Applications in 2022 ...
https://viso.ai/edge-ai/google-coral15.02.2021 · Coral AI USB Accelerator (Source: Google Coral 2021) Advantages and Benefits. The Coral Edge TPU boards and self-contained AI accelerators are used to build and power a wide range of on-device AI applications. When using Google Coral for Computer Vision projects, many benefits come with its Edge TPU Technology.. Overall, the scalability is based on an excellent …
Coral
https://www.coral.ai07.09.2021 · 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. Efficient. Balance power and performance with local, embedded applications. Private. Keep user data private by performing all inferences locally.