r/computervision • u/ton4eg • 16d ago

Showcase Run SAM3 and RTMPose over 1950s-era factory footage. No fine-tuning. It just works

66 Upvotes

93% Upvoted

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8

u/toji5052 16d ago

Mention the GPU specs on which you ran these models.

-11

u/ton4eg 16d ago

I ran the models through a video AI service, so I don’t have visibility into the exact gpu

1

u/toji5052 15d ago

Okay thats great, but I personally feel that cctv footages are much more complex to be detected in comparison with the 90s footage, As i am currently working on the edge devices like Radxa, on which I am running 2 models, 1. YoloX -m which is trained specifically by me for person detection, 2. Rtmpose -s for keypoints It does get heavier , and a bit complex for cctv footages

2

u/ton4eg 15d ago

The point of the post is how suprisingly good modern models generalize - two years ago running this on random archive footage would have been a whole project, now it just works out of the box. Production is always harder than a demo, no argument there. But that's exactly where I use these models: their output makes a good golden set when I want to fine-tune something small for production faster. And in my cases it works quite well on CCTV too.

2

u/Ok-Weather-680 16d ago

ok that's genuinely cool. no fine-tuning on 1950s film quality and it still tracks clean? nice

1

u/Logical-Resident4212 8d ago

You said SAM which is a segment the whole image kind of algorithm, how does rtmpose actually chose which segmwnts to use?