Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 14:47 UTC

serengil / deepface

A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

PythonMIT★ 23,453 stars⑂ 3,174 forkssince Feb 2020View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

serengil/deepface holds a health index of 81 out of 100, placing it in the Excellent band. It scores highest on Community & Adoption (86/100) and lowest on Security (21/100). It was last updated 1 day ago. A single contributor accounts for most of its recent work.

81
overall / 100
Excellent

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.

81
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 69 is calibrated to 81 on the published index scale (record calibration 2026-08-02).

Ownership

Sefik Ilkin SerengilPersonal account
2,163 followers36 public repossince Apr 2016@Neo4j

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
PyPIdeepface0.0.101180,918931 day ago

Metrics by category

Vitality

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

83Excellent · 21% of overall
How it's scored
36/36Push recency — last push 1 days ago
16.6/36Commit cadence — 24/52 weeks with commits
16.6/18Commit volume — 70 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year70
human_commit_share1
days_since_last_push1
active_weeks_last_year24
How it's scored
27/27Ships releases — 21 releases published
36/36Release recency — latest release 1 days ago
19.8/27Release cadence — a release every ~92.2 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count21
latest_release_tagv0.0.101
releases_from_tagsno
days_since_latest_release1
mean_days_between_releases92.2

Community & Adoption

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

86Excellent · 17% of overall
How it's scored
60/60Stars — 23,453 stars
25/25Forks — 3,174 forks
12.6/15Watchers — 189 watchers
Inputs used
forks3,174
stars23,453
watchers189
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
0/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges14
has_contributingno
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateyes
How it's scored
70.1/80Monthly downloads — 180,918 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesdeepface
dependents—
ecosystemspypi
total_downloads—
monthly_downloads180,918
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

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

68Good · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
4.9/22.5Commit distribution — top contributor authored 78% of commits
13.5/13.5Contributor breadth — 92 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled92
top_contributor_share0.78
How it's scored
41.8/42Issue resolution — 100% of issues closed
21.8/30PR acceptance — 266/366 decided PRs merged
8.7/13Newcomer PR acceptance — 2/3 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs266
open_issues6
closed_issues1,238
prs_merged_7d1
prs_decided_7d2
prs_merged_30d4
prs_decided_30d5
issue_closed_ratio0.995
closed_unmerged_prs100
first_time_authors_30d3
first_time_prs_merged_30d2
first_time_prs_decided_30d3
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
24/25Owner reach — 2,163 followers of serengil
23.4/25Track record — 36 public repos, account ~10 yr old
Inputs used
followers2,163
owner_typeUser
is_verified—
owner_loginserengil
public_repos36
account_age_days3,806
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 1 days ago
20/20Version history — 93 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesdeepface
ecosystemspypi
any_deprecatedno
min_days_since_publish1

Engineering Quality

Are baseline engineering and documentation practices in place?

78Good · 19% of overall
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
16/16Linter config — .pylintrc
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://www.youtube.com/watch?v=WnUVYQP4h44&list=PLsS_1RYmYQQFdWqxQggXHynP1rqaYXv_E
10/10Repository description
10/10Topics — 17 topics
10/10Wiki
Inputs used
topicsface-recognition, vgg-face, facenet, openface, facial-expression-recognition, age-prediction, gender-prediction, race-classification, emotion-recognition, deep-learning, machine-learning, deepface, face-analysis, python, deepid, arcface, facial-recognition
has_wikiyes
homepagehttps://www.youtube.com/watch?v=WnUVYQP4h44&list=PLsS_1RYmYQQFdWqxQggXHynP1rqaYXv_E
docs_sitehttps://www.youtube.com/watch?v=WnUVYQP4h44&list=PLsS_1RYmYQQFdWqxQggXHynP1rqaYXv_E
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

21At Risk · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsrequirements-dev.txt, requirements.txt, requirements_additional.txt, requirements_base.txt, requirements_pytorch.txt, requirements_tf.txt, setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories450
affected_packages7
assessed_packages11
unassessed_packages31
affected_by_severitycritical 3, high 3, low 1
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 11 resolved dependencies against OSV. 31 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. 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.

72Good · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.deepface.dev/llms.txt)
27.2/40Legible commit history — 51 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.deepface.dev/llms.txt
legible_history_share0.51
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — .pylintrc
11/11Static type checking — mypy.ini
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configsmypy.ini
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Python with type-check config (mypy.ini)
54.6/55Manageable file sizes — 1/127 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes61,035
source_files_sampled127
oversized_source_files1
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — notebooks
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

23,453GitHub stars
92contributors
70commits, last 12 months
1days since last push
21releases
1bus factor
6open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • deps.dev does not index pypi:deepface@0.0.101; advisories assessed against the repository dependency graph instead
  • Advisory severity resolved for 120 of 185 advisories (lookup cap); the remainder are reported as unknown severity
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit; 2026/09/17 14:46:46 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 ★ / 3,174 ⇿
0Stars
3,174Forks
7Releases

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.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

2,0002,2002,4002,6002,8003,0003,2003,174362024-122025-102026-09
Major 0Minor 0Patch 7

Each point covers 2 days.

All dependencies 42

Full resolved dependency set from the GitHub dependency graph: 0 direct and 42 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIalbumentations—indirect
PyPIdlib—indirect
PyPIfacenet-pytorch—indirect
PyPIfire—indirect
PyPIfire0.4.0indirect
PyPIflask—indirect
PyPIflask2.0.2indirect
PyPIflask-cors—indirect
PyPIgdown—indirect
PyPIgdown4.2.0indirect
PyPIgunicorn—indirect
PyPIgunicorn20.1.0indirect
PyPIinsightface—indirect
PyPIkeras—indirect
PyPIkeras2.7.0indirect
PyPIlightdsa—indirect
PyPIlightphe—indirect
PyPImediapipe—indirect
PyPImtcnn—indirect
PyPInumpy—indirect
PyPInumpy1.22.3indirect
PyPIonnxruntime—indirect
PyPIopencv-contrib-python—indirect
PyPIopencv-python—indirect
PyPIopencv-python4.9.0.80indirect
PyPIpandas—indirect
PyPIpandas2.0.3indirect
PyPIpillow—indirect
PyPIpillow9.0.0indirect
PyPIpydantic—indirect
PyPIpytest—indirect
PyPIpython-dotenv—indirect
PyPIrequests—indirect
PyPIretina-face—indirect
PyPItensorflow—indirect
PyPItensorflow2.7.0indirect
PyPItf-keras—indirect
PyPItorch—indirect
PyPItqdm—indirect
PyPItqdm4.66.1indirect
PyPItyping-extensions—indirect
PyPIultralytics—indirect
Dependency advisories 7

This repository publishes no package the index resolves, so its own dependency graph was assessed — 11 packages, which also include development and test pins that never ship: 7 carry known advisories, of which 0 are direct. 31 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

PackageVersionRelationSeverityAdvisoriesFixed in
keras2.7.0indirectcritical273.15.0
pillow9.0.0indirectcritical4327.0.0-beta.2
tensorflow2.7.0indirectcritical3682.12.1
flask2.0.2indirecthigh43.1.3
gdown4.2.0indirecthigh25.2.2
gunicorn20.1.0indirecthigh422.0.0
tqdm4.66.1indirectlow24.66.3

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 v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.