This is the first of a planned occasional series of posts on playing with some of the current AI specific add-on processors for Intenet of Things (IoT). In the series, it is planned that some experiments with the Google Coral adapter and the Development Board; as well the NVIDIA Jetson Nano will be shown.
Why bother? Basic reason is I love playing with AI and hardware - so it is kind of fun. Another reason is AI, IoT and edge computing, are important and growing technologies, and I want to start getting my head around them a bit.
In this post, I look at starting to use Coral Accelerator with a Raspberry Pi. The Coral environment is related to Google's earlier AIY Edge Tensor Processing Unit (TPU) range https://aiyprojects.withgoogle.com/edge-tpu/ and designed to work with TensorFlow Lite.
Good place to start is Google's Get started with the USB Accelerator pretty much all you need to do to get going is in it, it also mentions Raspberry Pi. It makes a good point, if you are using Python 3.7 on Raspberry Pi, at the time of writing the TensorFlow Lite API is up to Python 3.5. Not a problem but just need to be aware of it and Get started with the USB Accelerator offers a solution.
The Coral site has a number of examples you can try out at https://coral.withgoogle.com/examples/ . If you do try the Face detection example within the Object Detection example on a Raspberry Pi, you need to install feh to see the images; sudo apt-get install feh sorts this.
Some other good sources:
https://medium.com/@aallan/hands-on-with-the-coral-usb-accelerator-a37fcb323553
https://www.raspberrypi.org/magpi/teachable-machine-coral-usb-accelerator/
Related products, but for the Development Board and AIY
All opinions in this blog are the Author's and should not in any way be seen as reflecting the views of any organisation the Author has any association with. Twitter @scottturneruon
Why bother? Basic reason is I love playing with AI and hardware - so it is kind of fun. Another reason is AI, IoT and edge computing, are important and growing technologies, and I want to start getting my head around them a bit.
In this post, I look at starting to use Coral Accelerator with a Raspberry Pi. The Coral environment is related to Google's earlier AIY Edge Tensor Processing Unit (TPU) range https://aiyprojects.withgoogle.com/edge-tpu/ and designed to work with TensorFlow Lite.
Good place to start is Google's Get started with the USB Accelerator pretty much all you need to do to get going is in it, it also mentions Raspberry Pi. It makes a good point, if you are using Python 3.7 on Raspberry Pi, at the time of writing the TensorFlow Lite API is up to Python 3.5. Not a problem but just need to be aware of it and Get started with the USB Accelerator offers a solution.
The Coral site has a number of examples you can try out at https://coral.withgoogle.com/examples/ . If you do try the Face detection example within the Object Detection example on a Raspberry Pi, you need to install feh to see the images; sudo apt-get install feh sorts this.
Some other good sources:
https://medium.com/@aallan/hands-on-with-the-coral-usb-accelerator-a37fcb323553
https://www.raspberrypi.org/magpi/teachable-machine-coral-usb-accelerator/
Related products, but for the Development Board and AIY
All opinions in this blog are the Author's and should not in any way be seen as reflecting the views of any organisation the Author has any association with. Twitter @scottturneruon
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