Why would I use this rather than Snips? Edge computing is great, but in a closed source, hard to audit form it will be a tough sell to get 3rd party developers onboard.
I think the NCS2 has potential.. it's still early though. One thing on their roadmap is to support ARM, which I view as a must-have for low powered edge applications. Right now it only works with OpenVINO and certain x86 systems. So I view the real use cases coming when it can be used in settings with limited power (and limited or variable network connectivity).
We don't need more proprietary machine learning devices in our homes. I'd appreciate this so much more, if it was open source, so I can reshape it to whatever use case I have.
There are plenty of viable business models, that give you your well earned money and us the option to customize and understand our devices.
> We don't need more proprietary machine learning devices in our homes. I'd appreciate this so much more, if it was open source
It doesn't seem to do anything special? You can probably run your favorite machine learning framework on the Raspberry Pi, and it will work - albeit using the ARM cores and NEON only. Now, machine learning and inference _using the Raspberry Pi's GPU part_ (which is broadly documented, unlike most GPU hardware) would be a gamechanger, if only for educational scenarios.
I agree, so I looked at running image detection offline on a raspberry pi and wrote a post about it. It can't do anywhere near real time object detection on the larger YOLO models, but real time detection is often unnecessary. Also there are smaller models (e.g. yolo-mini) that can likely give you an acceptable frame rate.
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[ 1.8 ms ] story [ 34.9 ms ] threadI got one recently and I am not convinced, but I suppose someone on HN might have legitimate use cases.
There are plenty of viable business models, that give you your well earned money and us the option to customize and understand our devices.
It doesn't seem to do anything special? You can probably run your favorite machine learning framework on the Raspberry Pi, and it will work - albeit using the ARM cores and NEON only. Now, machine learning and inference _using the Raspberry Pi's GPU part_ (which is broadly documented, unlike most GPU hardware) would be a gamechanger, if only for educational scenarios.
[1] - https://github.com/plaidml/plaidml
Edit: they don't support RPI GPU yet - https://github.com/plaidml/plaidml/issues/141
https://medium.com/ml-everything/offline-object-detection-an...
Source: wrote a tutorial doing this for Arm (https://github.com/ARM-software/ML-examples/blob/master/mult...)
Non-open source? would it make it harder to deal with security and deal with as we do not know it is hacked or not.