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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 u
by Stammon 8y ago
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.
- zozbot123 8y ago> 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.
- coolspot 8y agoThere is PlaidML[1] - a cross-platform deep learning framework that works, among others, on Raspberry Pi's GPU. [1] - https://github.com/plaidml/plaidml https://github.com/plaidml/plaidml Edit: they don't support RPI GPU yet - https://github.com/plaidml/plaidml/issues/141 https://github.com/plaidml/plaidml/issues/141
- bko 8y agoI 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. https://medium.com/ml-everything/offline-object-detection-and-tracking-on-a-raspberry-pi-fddb3bde130 https://medium.com/ml-everything/offline-object-detection-an...