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I indexed 669 GB of my GoPro videos using my M1 Max computer and local ML models
114 points by iliashad 3 hours ago | hide | past | favorite | 24 comments
TLDR: I had 2,207 GoPro videos, and I need to rewatch them to find interesting moments from my cycling journey. I built a project to index them locally on my M1 Max using open-source ML models, search for those moments, and send the best clips straight to my DaVinci Resolve timeline. I indexed 628 videos (668.68 GB, 15h 13m 18s of footage duration), more details in the metrics table in the last section of this article.

Full article: https://iliashaddad.com/blog/i-indexed-669-gb-of-my-gopro-videos-using-my-m1-max-computer

 help



DaVinci 21 has indexing built-in (AI IntelliSearch). Not to diminish the work you did, but this is now available to many users (probably only Studio users since it has AI in the name)

Yes, I didn’t look at it. But does it upload your videos to the cloud or process them locally? And does it allow to provide custom faces data to help labeling faces in your videos ?

I think Adobe premiere pro have it as well but cloud processed


I was surprised to learn that the

    M1 Max CPU is an ARM/SoC, comparable to an 11th gen Intel i9
Do I have it right? Would Windows ARM performance be similar for those cpu?

ref: https://www.cpubenchmark.net/compare/4585vs4245/Apple-M1-Max...


It's also a bit apples (heh) to oranges for a handful of reasons, but most impactful

- "unified" ram makes all the system ram available as VRAM - dedicated ai coaccelerator thingy

Both of these reasons allow the apple silicon chips to crush conventional cpus in these kind of AI model workload stuffs

No idea about what the windows arm stuff is capable of. I know they use Qualcomm snapdragon chips though.


To your question, I can’t deny or confirm that because I didn’t tried it this project over a Windows machine yet or a machine with this config

“Comparable” is maybe true if we are talking about single core performance, but for memory bandwidth, the M1 Max is about 8 times faster. Wider bus, lower latency, not even close.

Does it work for porn collections too?

You'll need a lora for this, porn content rejection is heavy. Or you'll need a abliterated model, not sure if vision also works.

You might want to add something like yolo finetune to detect scenes + face recognition too.


Vision still works perfectly fine in abliterated models.

Why it’s always the same question? Hahah. I posted my project over Reddit and I got the same one hahah

Asking the important questions


Last time I tried whisper, it hallucinated an elaborate conversation from sounds of slapping and moaning and it took minutes to spit every single line of it.

Parakeet has been trained to detect non-voice sounds and exclude that from identification, so you might have better luck with that family.

Not sure if you’re being sarcastic but I think this is an interesting question. Would deep seek be useful here since it is local?

it is possible to use apple gpu with containers. either with podman + runkit + recent mesa or with recent vllm-metal from docker https://www.docker.com/blog/docker-model-runner-vllm-metal-m...

I was looking for a solution for this issue of running docker containers over MPS and utilizing their GPU power. I think this project will be the solution for it, I’ll try it very soon and add support for it. Thank you, much appreciated


Thank you

I would love your feedback and suggestions for new improvements or features you wanna have, either in the source available version, the desktop app or blog post itself?

This would fit most best as a “Show HN:” post :)

The title should link to the "full article". I wonder if OP's domain name is banned or something and they're doing this to get around it

I tried to edit it and add Show HN, but it doesn't show the edited version. Thank you!

Grab frames, lower res, classify, combine meta data. Write to sql



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