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The Next Generation
of Computer Vision

Teaching machines to understand our visual world autonomously, starting with the dynamic complexity of soccer - where every movement, every play, and every moment trains our AI to decode the world around us.

Decentralized Computer Vision

Our subnet processes football videos, optimizing for speed,accuracy, and cost efficiencythrough advanced vision‎ ‎ ‎ ‎
models.

Unsupervised Validation

Our validation mechanism usesVLLMs for selective framesampling and automated visual
understanding, eliminatingmanual annotation.

Market Applications

Our subnet builds a strong datamoat, powering AI agents onuse cases from AI live
commentary, betting to
professional scouting.

  • Ball

  • Team A

  • Team B

  • Referees

  • Goal

Football terrain
Football game

Minutes Processed

6,371,277

Frame Processing Speed

99ms/frame

System Load

0%

Best Model
Accuracy

Benchmark vs
State of The Art

Benchmark: 0%

VS

State of The A.: 0%

Loading testnet data • Numbers shown are temporary

Top Miners Shaping
the Future of Vision

Subnet Initialization in Progress
Synchronizing Decentralized Vision Data

We invite you to return in the coming days to explore our metagraph and subnet performance. Thank you for your patience.

GSR Tasks
Analysis In Progress

City vs. Tottenham

67%

Chelsea vs. Shamrock Rovers

32%

Nottm Forest vs. Crystal Palace

11%
Done

City vs. Tottenham

67%

Chelsea vs. Shamrock Rovers

32%

Nottm Forest vs. Crystal Palace

11%
Action Recognition Tasks

City vs. Tottenham

Score
Speed Tracker
Benchmark
Distribution
Leaderboard

Top Scorer

RankHotkeyMiner IDAverage SpeedAverage ScoreNumber of TasksLast Active
1

f31C6905E248d840b8d840b8d840b1

sepa
75.2s
55.684,67811/12/2024, 16:49:18
2

f31C6905E248d840b8d840b8d840b2

cain
38.4s
54.624,58611/12/2024, 16:56:48
3

f31C6905E248d840b8d840b8d840b3

curious
10.5s
54.125,09111/12/2024, 16:12:01
4

f31C6905E248d840b8d840b8d840b4

beautiful
12.1s
53.96,81311/12/2024, 17:01:09
5

f31C6905E248d840b8d840b8d840b5

marry
88.3s
51.225,92311/12/2024, 12:01:01
6

f31C6905E248d840b8d840b8d840b6

robert
23.8s
47.745,27810/12/2024, 16:38:59
7

f31C6905E248d840b8d840b8d840b7

relaxed
43.2s
48.987,09509/12/2024, 15:12:27
8

f31C6905E248d840b8d840b8d840b8

athlete
67.8s
53.854,93210/12/2024, 22:13:55
9

f31C6905E248d840b8d840b8d840b9

focused
18.6s
49.883,85708/12/2024, 14:21:09
10

f31C6905E248d840b8d840b8d840b0

hunter
32.1s
50.256,12411/12/2024, 14:35:45
11

f31C6905E248d840b8d840b8d840c1

lion
45.7s
52.895,72309/12/2024, 10:55:03
12

f31C6905E248d840b8d840b8d840c2

tiger
26.3s
48.424,62910/12/2024, 11:47:32
13

f31C6905E248d840b8d840b8d840c3

alpha
11.9s
53.127,29008/12/2024, 20:14:20
14

f31C6905E248d840b8d840b8d840c4

zeta
19.4s
49.563,98707/12/2024, 18:22:10
15

f31C6905E248d840b8d840b8d840c5

delta
36.7s
51.785,12311/12/2024, 19:41:30
16

f31C6905E248d840b8d840b8d840c6

brave
28.5s
50.924,33110/12/2024, 17:09:20
17

f31C6905E248d840b8d840b8d840c7

maverick
40.2s
54.316,01109/12/2024, 09:18:10
18

f31C6905E248d840b8d840b8d840c8

hunterX
50.1s
55.125,12008/12/2024, 21:47:35
19

f31C6905E248d840b8d840b8d840c9

speedster
70.3s
56.236,90211/12/2024, 08:33:25
20

f31C6905E248d840b8d840b8d840d0

legend
21.8s
50.995,43810/12/2024, 10:01:19

Meet the team

Max's photo

Max

CEO

Twitter
Tim's photo

Tim

CTO

Twitter
Nigel's photo

Nigel

CSO

Mog's photo

Mog

DeAI Advisor

Twitter
Brian's photo

Brian

Football Advisor

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