Into:Score
Vision AI on every camera, decentralized.
As of · Jun 4, 10:37 UTC
Score turns raw camera feeds into structured analytics in real time, and the network has pushed beyond a sports-only focus into retail security, venue ops, and live broadcast tooling.
What is Score
Score ( 44) is a Bittensor subnet for computer vision. The network takes video, runs models contributed by , and outputs structured insights like player positions, ball trajectories, or detected objects.
The simple version: It is like having a stadium full of analysts watching every camera you point at it, except the analysts are AI models competing for the cleanest output.
Centralized equivalent: Closest analogues are Stats Perform, Hawk-Eye, and Genius Sports for sports vision, plus enterprise platforms like Verkada for venue and retail vision.
How it works:
- Miners submit computer vision models that solve specific tasks ("Elements") defined in an on-chain manifest, from player tracking to object detection.
- Validators score those models against ground truth, either real datasets or pseudo-ground-truth generated locally with SAM3, then submit per-element weights on chain.
Why This Matters
- The problem it solves: Specialised computer vision is expensive, brittle, and locked behind a few centralised providers. Sports leagues, retailers, and venue operators pay heavily for tooling that only handles narrow scenarios.
Other research from the same neighborhood of the network.
