Local AI Just Got Easy (and Cheap)

I love that you relate Frigate to golden eye, I do the same thing with relating facilities to bathrooms because of that game.

Person detection. Training the AI to hunt us more efficiently. lol This is an amazing little thing. I do have an issue with the AI online. Having a local device that can be use to monitor and do what it has to do on an offline local network is amazing.

Thanks for the demo and info, have a great day

I'm blown away you were able to get this piece of Google abandonware up

Thank you for this. ;-) Have a zimaboard 832, with coral tpu USB & PCI

gains sentience as you plugged in the TPU made me lol

I don't know how you do your video editing, but I really like the zoom and other effects thrown in here. Nice walkthrough.

Did you test to see if the USB bus speed is causing a bottleneck as is the premise of the video? 600MB/s is a huge amount of image data to pass over to the accelerator. Is the bus latency an issue? Would be nice to see a use case that shows where the “faster” you mention matters.

nice work, binge-watching your stuff! what's the screen recording software you're using with the rounded-borders face cam bottom right?

PS there is nothing wrong with using an older version of python. Use conda or venv to properly version your pip deps

The Coral TPU m.2 / mini-PCIe version itself is PCIe 2.0 x1 aka 500MB/s, so theoretically it's "slower" than the USB3 version in every case. Obviously the answer isn't as simple as that, but saying the PCIe version is faster because USB3 bandwith is only 625MB/s isn't the right answer either. You have to take into account various overheads, driver optimizations, etc...

Conda (anaconda or miniconda) resolves python dependency issues by creating a separate environment with the specified version of python, and isolates the environment along with anything loaded into the environment related to Python (ie if you install with pip or conda, it stays with the environment and leaves the systems python alone) This allows me to use alpha versions of ubuntu with any python programs i want by having separated environments for each program

Given the keying of your M2 module is should have a max transfer rate of 250MB/s or maybe 500MB/s if using both PCIE channels.

So you state "cheap" but fail to cover the actual costs of all the hardware! 'sup with that? I'd at least expect that in the description, or a pinned comment, or covered in the video and a summary at the end with some chapter markers. Pretty please? I did the work for you: USD$119.90 to $199.90 - Zima Board USD$64 to $96 - Zima Blade, dual-core and quad-core, respectively (unsure of availability) USD$24.99 - Coral AI Mini PCIe Accelerator USD$10 - Mini PCIe Adapter Card (eBay) So Cheap = about USD$125 for the Blade, AI, Adapter, and $25 for power supply, wires, etc (maybe less) Questionable Availability though Still cool, but holy hell you really need to be a BOFH SYSOP to get this working. NOT EASY

Maybe I missed it, but what card did you plug your TPU into? to be able to plug it into a PCIE slot?

so freaking cool, if you were to deploy a setup like this would you stick with the tpu person detection, or would you opt more for a general purpose motion detection. that would pick up cars, dogs, people, birds, or whatever, and wouldn't require a designated tpu

Would love to see this in the pi 5 since it has pcie to see what it’s capable of

which screen display, mouse and keyboard are you using for the raspberry pi in this video?

So much room for activities

Good thing you have in your hand think 2~3 years ago went to buy a older Coral w/ priced $60 nxt thing every direction scooped up & vendors were selling’ like @ $190 even $300 even if one could afford it was they were hard find a/ lead time was spaced like months for the next available batch {same thing w/ the older Versions of Raspberries