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Google Coral USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers

£41.275£82.55Clearance
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PyCoral is built on top of Tensorflow Lite and allows you to run Tensorflow Lite models on the Edge TPU without writing lots of boilerplate. Google Coral is an edge AI hardware and software platform for intelligent edge devices with fast neural network inferencing. Even though Google offers many precompiled models that can be used with the USB Accelerator, you might want to run your custom models.

Pose estimation: Estimate the poses of people or objects based on the detection and tracking of key points. Earlier this year, Pratexo began working to bring the "electricity grid edge" to HKN, starting with putting intelligent computing nodes running Pratexo software enabled with Coral intelligence at transformer stations. Benchmarking and Profiling your Scripts” inside Raspberry Pi for Computer Vision to learn how to benchmark your deep learning scripts on the Raspberry Pi.On the hardware side, it contains an Edge Tensor Processing Unit (TPU), which provides fast inference for deep learning models at comparably low power consumption. It's build on top of the TensorFlow Lite C++ API and abstracts-away a lot of the code required to handle input tensors and output tensors.

It features a removable system-on-module (SOM) that contains eMMC, SOC, wireless radios and Coral Edge TPU on board.

Mine was pre-production hardware, and the box came with sticker’ed up saying that “ Marking and packaging not final,” which may well explain the AIY Projects branding on my USB Accelerator. The example detection script allows you to specify the tracker program you want to use (the Sort tracker is included). Coral is a department of the Google brand that helps to create intelligent designs based on artificial intelligence. Note: Be careful with the line-wrapping and ensure that you copy each full command + enter in your terminal as shown. This cookie is installed by Google Universal Analytics to restrain request rate and thus limit the collection of data on high traffic sites.

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