Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-13 02:04 UTC

ipazc / mtcnn

MTCNN face detection implementation for TensorFlow, as a PIP package.

Jupyter NotebookMIT★ 2,482 stars⑂ 527 forkssince Jan 2018View on GitHub ↗

ipazc/mtcnn holds a health index of 60 out of 100, placing it in the Moderate band. It scores highest on Community & Adoption (82/100) and lowest on Vitality (21/100). It was last updated 673 days ago. A single contributor accounts for most of its recent work.

60
overall / 100
Moderate

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

60
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 57 is calibrated to 60 on the published index scale (record calibration 2026-08-02).

Ownership

Iván de Paz CentenoPersonal account
116 followers29 public repossince Mar 2014@relev-ai

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPImtcnn1.0.0-12674 days ago

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

21At Risk · 21% of overall
How it's scored
0/36Push recencylast push 673 days ago
0/36Commit cadence0/52 weeks with commits
0/18Commit volume0 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year0
human_commit_share1
days_since_last_push673
active_weeks_last_year0
How it's scored
27/27Ships releases1 releases published
7.2/36Release recencylatest release 674 days ago
12.6/27Release cadencecadence unknown (single release)
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count1
latest_release_tagv1.0.0
releases_from_tagsno
days_since_latest_release674
mean_days_between_releases

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

82Excellent · 17% of overall
How it's scored
55.1/60Stars2,482 stars
22.7/25Forks527 forks
9/15Watchers43 watchers
Inputs used
forks527
stars2,482
watchers43
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesbadge.fury.io, github.com, readthedocs.org, shields.io
has_pull_request_templateyes

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

58Moderate · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
8.3/22.5Commit distributiontop contributor authored 63% of commits
13.5/13.5Contributor breadth12 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor1
contributors_sampled12
top_contributor_share0.629
How it's scored
31.2/42Issue resolution74% of issues closed
16.6/30PR acceptance16/29 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Reviewno data
Inputs used
merged_prs16
open_issues28
closed_issues81
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.743
closed_unmerged_prs13
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
14.9/25Owner reach116 followers of ipazc
22.8/25Track record29 public repos, account ~12 yr old
Inputs used
followers116
owner_typeUser
is_verified
owner_loginipazc
public_repos29
account_age_days4,545
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.
How it's scored
25/25Published & resolvable1 package(s) on pypi
14/35Publish recencylatest publish 674 days ago
20/20Version history12 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesmtcnn
ecosystemspypi
any_deprecatedno
min_days_since_publish674

Engineering Quality

Are baseline engineering and documentation practices in place?

82Excellent · 19% of overall
How it's scored
24/24CI workflows3 workflow(s)
24/24Tests present
16/16Linter config.pylintrc
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

85Excellent
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics8 topics
10/10Wiki
Inputs used
topicsmtcnn, face, detection, tensorflow, pip, package, python3, landmark
has_wikiyes
homepage
docs_site
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

52Moderate · 16% of overall
How it's scored
30/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfilespublished library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestsrequirements-dev.txt, requirements-tf.txt, requirements.txt, setup.py
has_codeql_workflowno
has_security_policyyes
has_dependabot_configno
Excluded from scoring (no data or not applicable): Dependency lockfiles. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages2
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:mtcnn@1.0.0 runtime dependency closure — what installing the published package pulls in — 2 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

44Weak · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
19.8/40Legible commit history23 of 62 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.371
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config.pylintrc
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno agent-authored commits among the last 62
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codeJupyter Notebook without a type-check config
55/55Manageable file sizes0/21 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes26,634
source_files_sampled21
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesnotebooks
Inputs used
example_dirsnotebooks
has_mcp_signalno
api_schema_files
interfaces_expected_of
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

2,482GitHub stars
12contributors
0commits, last 12 months
673days since last push
1releases
1bus factor
28open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard did not return a usable result (2026/08/13 02:04:20 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 527 ⇿
0Stars
527Forks
1Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

010020030040050060050982018-012022-042026-07
Major 1Minor 0Patch 0

Each point covers 8 days.

All dependencies 8

Full resolved dependency set from the GitHub dependency graph: 0 direct and 8 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIjoblibindirect
PyPIlz4indirect
PyPImkdocsindirect
PyPImkdocs-jupyterindirect
PyPImkdocs-materialindirect
PyPIpytestindirect
PyPIpytest-covindirect
PyPItensorflowindirect
Dependency advisories 0

Installing pypi:mtcnn@1.0.0 pulls in 2 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.