# OpnTec/mvisc

Mobile Visual Classification (MVISC) is a project to identify and classify animals.

Repository: https://github.com/OpnTec/mvisc
Canonical: https://ross.abutalabs.com/products/mvisc
Language: HTML
License: GPL-3.0
License Family: copyleft
Last push: 2022-05-20T22:02:05+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3960, "days_push": 1566, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1384, forks 0 (observed 2026-08-28T04:04:34.694320+00:00)

## What it is
MVISC (Mobile Visual Classification) is an application that identifies and classifies individual animals from photos using computer vision, matching visual patterns against a local database to determine if an animal is new or already known. It is a FOSSASIA project based on the former Zooracle project, intended for both online and offline use.

## Use cases
- identify individual animals from photos
- match a photographed animal against a database of known individuals
- wildlife research without invasive marking
- manage and export a database of animal sightings
- run offline animal recognition in the field

## When to choose
- you need non-invasive individual animal identification from photos
- you work in wildlife conservation or field research with limited connectivity
- you want an open-source, GPL-licensed recognition tool you can extend

## When to avoid
- you need general object detection or species classification rather than individual matching
- you need a production-ready, actively maintained product
- you need large-scale cloud-based recognition infrastructure

## Facets
- artifact type: application
- maturity: experimental
- function: computer-vision, image-processing, machine-learning
- domain: computer-vision, image-processing, machine-learning, mobile-development
- platform: cross-platform
- tags: animal-recognition, individual-identification, wildlife, conservation, image-matching, keypoints, mobile

## Member repositories
- OpnTec/mvisc (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:34.694320+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:39:58.277383+00:00, confidence not recorded.
  - readme: https://github.com/OpnTec/mvisc (fetched 2026-08-28T04:04:34.694320+00:00, sha f574776c99ac)
- Data as of 2026-08-30T08:39:29.467469+00:00.