Again, we can improve results by filtering on a minimum probability to remove the extraneous detections. With decades of experience in electronics manufacturing at a global scale, ASUS IoT will provide Coral with the resources to meet our growth demands while we continue to develop new products for edge computing.

To learn how to configure your Google Coral USB Accelerator (and perform classification + object detection), just keep reading! So yeah… it’s not only doable but with the example schematics provided by Coral and Framework, it is 15 min work.You’ll then learn how to perform classification and object detection using Google Coral’s USB Accelerator. 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. In the future, we will learn how to perform custom object detection during which time you can draw a thicker detection box. When you start Frigate, it will disconnect and you have to add it once again in VMM but this time it will have Google Coral name.

Best of all, you can manage the Python packages inside your your virtual environment inside with pip (Python’s package manager). Using the guides, I created an image classifier and object detector for the Google Coral USB Accelerator. The Edge TPU API also includes APIs to perform on-device transfer-learning with either weight imprinting or backpropagation.Since the RPi 3B+ doesn’t have USB 3, that’s not much we can do about that until the RPi 4 comes out — once it does, we’ll have even faster inference on the Pi using the Coral USB Accelerator.