// readme

Annotation-free Multi-Animal Direction Estimation Using Self-supervised learning

AMADEUS is a markerless multi-animal tracking system for laboratory videos. It estimates an oriented bounding box (OBB) and head direction for each animal while maintaining individual identities over time. No manual training annotation or physical marking is required.

After you configure foreground segmentation, AMADEUS extracts single-animal blobs, assigns direction classes using movement direction, and synthesizes interaction images by copy-paste augmentation. A detector trained on these images estimates OBBs and direction classes during crossings and crowding. Staged association and refinement produce individual tracks. When almost complete occlusion is expected, contrastive learning is additionally used for identity verification and correction.

Install and launch

AMADEUS supports NVIDIA GPUs on Windows and Linux, Apple Silicon on macOS, and AMD GPUs via ROCm on Linux and Windows 11.

For NVIDIA GPUs, 8 GB or more of VRAM is recommended, although the required memory depends on factors such as video resolution and the number of animals. AMADEUS…

↑ The Undervalued Score +

How much a project earns versus how much attention it actually gets. Above 50 means the work is outrunning its audience. Recomputed nightly from commit velocity, contributor effort, issue resolution, fork utility, release cadence, and project maturity — divided by a logarithmic reach factor.

score  = signal / reach

signal = 0.25·commit_velocity   // commits in last 90 days (cap 30)
       + 0.20·contributor_work  // unique authors × velocity (cap 100)
       + 0.20·issue_resolution  // closed ÷ total issues
       + 0.20·fork_ratio        // forks ÷ stars (proxy for real usage)
       + 0.10·release_cadence   // releases in 90 days (cap 3)
       + age_bonus              // +0 to +0.30 after 6 months
       + homepage_bonus         // +0.05 if homepage is set

reach  = log₁₀(stars + watchers + 10)█
♥ The Health Score +

Is the project alive and maintained right now? A 0–100 pulse recomputed nightly from commit recency, rhythm, how fast issues close, and how quickly PRs get merged.

health = 0.35·recency       // days since last commit (90d decay)
       + 0.25·cadence       // commit rhythm consistency
       + 0.20·issue_health  // closed ÷ total issues
       + 0.20·pr_health     // merged ÷ total PRs█
◷ Health bands +

The colour and label on every card come straight from the health score.

Healthy   80 – 100   active, responsive, regular releases
Stable    60 – 79    maintained, steady, no alarms
Quiet     40 – 59    slowing down — watch this one
At Risk    0 – 39    going dark · candidate for rescue█
// Tags — what each label means +

Tags are independent behavioral signals computed nightly. A project can hold multiple at once. They drive the home page sections.

solo_builder      one person holds > 80% of commits (last 180d)
needs_contributors has open "help wanted" or "good first issue" labels
hidden_gem        < 100 stars · active in last 3 months · documented
legacy_hero       repo > 5 years old · committed this year
fork_magnet       forks/stars > 0.5 · used as template or dependency
release_machine   5+ releases in the last 90 days
under_pressure    > 10 open issues · ≤ 2 contributors · health ≥ 60
community_watch   watchers > stars · devs tracking before the public
community_hub     GitHub Discussions enabled · > 20 discussions
funded            maintainer has active funding channel█
★ Why rank against stars at all? +

Stars are an outcome, not effort. A project with 8 stars and daily commits is doing more interesting work than one coasting on 8k. We measure the building, then divide by the attention already received — so the genuinely undervalued rise to the top.

// stars   = lagging indicator
// commits = leading indicator
// we rank by the leading one█