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roboflow/supervision

We write your reusable computer vision tools. πŸ’œ

PythonsurgingLive GitHub sample
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Current momentum

85capped sample

Stars

51.3K

+33 since sync

Commits

37

in period

Active contributors

14

+0%

Issues closed

4

74.5h avg. resolution

Recorded trajectory

Movement you can verify over time.

Loading daily momentum history.

Behind the number

A score you can inspect.

Each signal contributes points to the total. These are capped GitHub samples, not complete activity counts or proof of adoption.

Development activity40.0 / 40 pts

37 commits Β· 43 opened PRs Β· 4 closed issues in 7 days

24 + 22 Γ— ln(1 + weekly weighted activity). Activity = commits + 2 Γ— opened PRs + closed issues. Signal bounded to 0–100, then weighted 40%.

Contributor breadth22.9 / 25 pts

14 sampled contributors in 7 days Β· 0% contributor-growth input

24 + 25 Γ— ln(1 + contributors Γ— √(7 / days)) + 0.15 Γ— growth. Growth uses the 30-day windows; capped samples use a neutral 0 input. Signal bounded to 0–100, then weighted 25%.

Issue resolution9.8 / 20 pts

74.5 hours resolution input

105 βˆ’ 9 Γ— logβ‚‚(1 + hours). Uses sampled closed issues, not first-response time. With no resolved issues, the model uses 168 hours. Signal bounded to 0–100, then weighted 20%.

Release cadence12.0 / 15 pts

5 days cadence input

105 βˆ’ 5 Γ— cadence days. With fewer than two published releases, the model uses time since the last push as a proxy. Signal bounded to 0–100, then weighted 15%.

Weighted total Β· rounded once85 / 100

What changed in the recorded sample?

Loading recorded observations…

Inspect commits β†—Read releases β†—Check issues β†—

4.9K forks

Downstream adoption

79 open issues

Community demand

5 day cadence

Shipping consistency