Rust · AGPL-3.0
ultralytics/inference
High-performance Ultralytics YOLO inference in Rust with ONNX Runtime, GPU backends, CLI, and WebGPU/WASM.
★ 212
Stars
31 forks · 1 open issues
+6.0/wk
Growth · measured
Stars counted between two of our own snapshots roughly a week apart. The repository page gives the exact span.
27.1
Momentum / 100
see the breakdown below
11mo ago
Created
last push 3d ago
Star history
How this score was built
| Component | Weight | Earned | Why |
|---|---|---|---|
| Velocity | 55 | 5.1 | +6.0/wk, log-scaled against a 750/day ceiling |
| Acceleration | 15 | 0.2 | last week ran 1.01× the monthly pace |
| Freshness | 15 | 15.0 | last push 3d ago; decays to zero at 180 days |
| Scale | 15 | 6.8 | 212 stars, log-scaled against 150k — stops a 3-star repo topping the board |
| Momentum | 100 | 27.1 | sum of the above |
Detail
| Topics | clicomputer-visioncoremlcudadepth-estimationimage-classificationinferenceobject-detectiononnxonnxruntimeopenvinopose-estimationrustsegmentationsemantic-segmentationtensorrtultralyticswasmwebgpuyolo |
| Matched on | Computer Vision (computer-vision, object-detection) |
| Short window | +6 stars over 7 days = 0.9/day |
| Long window | +22 stars over 26 days = 0.8/day |
| Lifetime average | 0.62 stars/day over 344 days |
| First seen here | 2026-10-11 |
| Last read | 2d ago |