Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-16 21:51 UTC

wkentaro / labelme

Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation.

PythonGPL-3.0★ 16,173 stars⑂ 3,708 forkssince May 2016View on GitHub ↗
KindCommand-line toolLibraryDesktop applicationhow this is determined
#image-annotation#python#computer-vision#annotations#deep-learning#semantic-segmentation#instance-segmentation#video-annotation#classification

wkentaro/labelme holds a health index of 93 out of 100, placing it in the Exceptional band. It scores highest on Vitality (98/100) and lowest on Security (65/100). It was last updated today. A single contributor accounts for most of its recent work.

93
overall / 100
Exceptional

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.

93
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 79 is calibrated to 93 on the published index scale (record calibration 2026-08-02).

Ownership

Kentaro WadaPersonal account
2,551 followers358 public repossince May 2013

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
PyPIlabelme7.5.030,2642392 days ago

Metrics by category

Vitality

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

98Exceptional · 21% of overall

Development activity

96Exceptional
How it's scored
36/36Push recency — last push 0 days ago
32.5/36Commit cadence — 47/52 weeks with commits
18/18Commit volume — 1,990 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 29 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year1,990
human_commit_share1
days_since_last_push0
active_weeks_last_year47

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 2 days ago
27/27Release cadence — a release every ~8.1 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv7.5.0
releases_from_tagsno
days_since_latest_release2
mean_days_between_releases8.1
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

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

75Good · 17% of overall

Popularity & adoption

97Exceptional
How it's scored
60/60Stars — 16,173 stars
25/25Forks — 3,708 forks
12.1/15Watchers — 152 watchers
Inputs used
forks3,708
stars16,173
watchers152
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

50Moderate
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (GPL-3.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno

Sustainability & Governance

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

67Good · 23% of overall

Maintainer resilience (bus factor)

35Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
2.2/22.5Commit distribution — top contributor authored 90% of commits
13.5/13.5Contributor breadth — 66 contributors
10/10OpenSSF Scorecard: Contributors — project has 21 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled66
top_contributor_share0.903

Issue & PR responsiveness

73Good
How it's scored
34.9/42Issue resolution — 83% of issues closed
23.8/30PR acceptance — 1,109/1,399 decided PRs merged
13/13Newcomer PR acceptance — 2/2 first-time contributors' PRs merged in 30d
1.5/15OpenSSF Scorecard: Code-Review — Found 1/10 approved changesets -- score normalized to 1
Inputs used
merged_prs1,109
open_issues166
closed_issues819
prs_merged_7d31
prs_decided_7d31
prs_merged_30d58
prs_decided_30d60
issue_closed_ratio0.831
closed_unmerged_prs290
first_time_authors_30d2
first_time_prs_merged_30d2
first_time_prs_decided_30d2

Ownership & stewardship

74Good
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
24.5/25Owner reach — 2,551 followers of wkentaro
25/25Track record — 358 public repos, account ~13 yr old
Inputs used
followers2,551
owner_typeUser
is_verified—
owner_loginwkentaro
public_repos358
account_age_days4,886
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 2 days ago
20/20Version history — 239 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packageslabelme
ecosystemspypi
any_deprecatedno
min_days_since_publish2

Engineering Quality

Are baseline engineering and documentation practices in place?

86Excellent · 19% of overall

Engineering practices

84Excellent
How it's scored
24/24CI workflows — 5 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 10 out of 10 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://labelme.io
10/10Repository description
10/10Topics — 9 topics
0/10Wiki
Inputs used
topicsimage-annotation, python, computer-vision, annotations, deep-learning, semantic-segmentation, instance-segmentation, video-annotation, classification
has_wikino
homepagehttps://labelme.io
docs_sitehttps://labelme.io
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

65Good · 16% of overall

Security posture

56Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
2.2/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 10 out of 10 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0.8/7.5Code-Review — Found 1/10 approved changesets -- score normalized to 1
2.5/2.5Contributors — project has 21 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 29 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
6.8/7.5Vulnerabilities — 1 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate5.6
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages47
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:labelme@7.5.0 runtime dependency closure — what installing the published package pulls in — 47 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.

78Good · 4% of overall

Agent context & guidance

100Exceptional
How it's scored
45/45Agent instructions — AGENTS.md, CLAUDE.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://labelme.io/llms.txt)
40/40Legible commit history — 100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://labelme.io/llms.txt
legible_history_share1
agent_instruction_filesAGENTS.md, CLAUDE.md
agent_instruction_max_bytes1,180

Verify loop (build / test / typecheck)

61Moderate
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environment — lockfile
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 — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

54Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
54.1/55Manageable file sizes — 3/188 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes135,069
source_files_sampled188
oversized_source_files3

Machine-readable interfaces

100Exceptional
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 — examples
Inputs used
example_dirsexamples
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

16,173GitHub stars
66contributors
1,990commits, last 12 months
0days since last push
100releases
1bus factor
166open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 3,708 ⇿
0Stars
3,708Forks
49Releases

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.

2,5002,7503,0003,2503,5003,7503,708112023-022024-112026-09
Major 2Minor 17Patch 24

Each point covers 4 days.

OpenSSF Scorecard 5.6 / 10
5.6aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-09-16 21:51 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests10 out of 10 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 1/10 approved changesets -- score normalized to 1
10Contributorsproject has 21 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 29 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
9Vulnerabilities1 existing vulnerabilities detected
Direct dependencies 13
RegistryPackageVersion constraintManifest
PyPIimgviz>=2.0.0pyproject.toml
PyPIloguru>=0.7pyproject.toml
PyPImatplotlib>=3.7pyproject.toml
PyPInatsort>=7.1.0pyproject.toml
PyPInumpy>=1.24pyproject.toml
PyPIonnxruntime>=1.23.2pyproject.toml
PyPIosam>=0.4.0pyproject.toml
PyPIpillow>=2.8pyproject.toml
PyPIpyside6>=6.8pyproject.toml
PyPIscipy>=1.8pyproject.toml
PyPIscikit-image>=0.21pyproject.toml
PyPItifffile>=2024.1.30pyproject.toml
PyPIruamel-yaml>=0.18pyproject.toml
All dependencies 117

Full resolved dependency set from the GitHub dependency graph: 13 direct and 104 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIimgviz2.0.0direct
PyPIloguru0.7.3direct
PyPImatplotlib3.10.8direct
PyPInatsort8.4.0direct
PyPInumpy2.4.4direct
PyPIonnxruntime1.24.4direct
PyPIosam0.4.0direct
PyPIpillow12.3.0direct
PyPIpyside66.11.1direct
PyPIruamel-yaml0.19.1direct
PyPIscikit-image0.26.0direct
PyPIscipy1.17.1direct
PyPItifffile2026.4.11direct
PyPIannotated-doc0.0.4indirect
PyPIannotated-types0.7.0indirect
PyPIasttokens3.0.1indirect
PyPIbeautifulsoup44.14.3indirect
PyPIcertifi2026.2.25indirect
PyPIcffi2.0.0indirect
PyPIcharset-normalizer3.4.7indirect
PyPIclick8.3.2indirect
PyPIcmap0.7.2indirect
PyPIcolorama0.4.6indirect
PyPIcontourpy1.3.3indirect
PyPIcoverage7.13.5indirect
PyPIcryptography50.0.0indirect
PyPIcycler0.12.1indirect
PyPIdecorator5.2.1indirect
PyPIdistro1.9.0indirect
PyPIdocutils0.22.4indirect
PyPIexecnet2.1.2indirect
PyPIexecuting2.2.1indirect
PyPIfilelock3.28.0indirect
PyPIflatbuffers25.12.19indirect
PyPIfonttools4.62.1indirect
PyPIgdown6.0.0indirect
PyPIgruff0.0.10indirect
PyPIid1.6.1indirect
PyPIidna3.15indirect
PyPIimageio2.37.3indirect
PyPIiniconfig2.3.0indirect
PyPIipython9.12.0indirect
PyPIipython-pygments-lexers1.1.1indirect
PyPIjaraco-classes3.4.0indirect
PyPIjaraco-context6.1.2indirect
PyPIjaraco-functools4.4.0indirect
PyPIjedi0.19.2indirect
PyPIjeepney0.9.0indirect
PyPIjinja23.1.6indirect
PyPIkeyring25.7.0indirect
PyPIkiwisolver1.5.0indirect
PyPIlazy-loader0.5indirect
PyPImaison2.0.2indirect
PyPImarkdown-it-py4.0.0indirect
PyPImarkupsafe3.0.3indirect
PyPImatplotlib-inline0.2.1indirect
PyPImdformat1.0.0indirect
PyPImdformat-frontmatter2.0.10indirect
PyPImdit-py-plugins0.5.0indirect
PyPImdurl0.1.2indirect
PyPImore-itertools11.0.2indirect
PyPImpmath1.3.0indirect
PyPInetworkx3.6.1indirect
PyPInh30.3.4indirect
PyPIpackaging26.1indirect
PyPIparso0.8.6indirect
PyPIpexpect4.9.0indirect
PyPIplatformdirs4.9.6indirect
PyPIpluggy1.6.0indirect
PyPIprompt-toolkit3.0.52indirect
PyPIprotobuf7.34.1indirect
PyPIptyprocess0.7.0indirect
PyPIpure-eval0.2.3indirect
PyPIpycparser3.0indirect
PyPIpydantic2.13.2indirect
PyPIpydantic-core2.46.2indirect
PyPIpygments2.20.0indirect
PyPIpyparsing3.3.2indirect
PyPIpyside6-addons6.11.1indirect
PyPIpyside6-essentials6.11.1indirect
PyPIpysocks1.7.1indirect
PyPIpytest9.0.3indirect
PyPIpytest-cov7.1.0indirect
PyPIpytest-qt4.5.0indirect
PyPIpytest-xdist3.8.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpywin32-ctypes0.2.3indirect
PyPIreadme-renderer44.0indirect
PyPIrequests2.33.1indirect
PyPIrequests-toolbelt1.0.0indirect
PyPIrfc39862.0.0indirect
PyPIrich15.0.0indirect
PyPIruff0.12.12indirect
PyPIruyaml0.91.0indirect
PyPIsecretstorage3.5.0indirect
PyPIsetuptools83.0.0indirect
PyPIshellingham1.5.4indirect
PyPIshiboken66.11.1indirect
PyPIsix1.17.0indirect
PyPIsoupsieve2.8.4indirect
PyPIstack-data0.6.3indirect
PyPIsympy1.14.0indirect
PyPItaplo0.9.3indirect
PyPItowncrier25.8.0indirect
PyPItqdm4.67.3indirect
PyPItraitlets5.14.3indirect
PyPItwine6.2.0indirect
PyPIty0.0.31indirect
PyPItyper0.24.1indirect
PyPItypes-pillow10.2.0.20240822indirect
PyPItyping-extensions4.15.0indirect
PyPItyping-inspection0.4.2indirect
PyPItypos1.45.1indirect
PyPIurllib32.7.0indirect
PyPIwcwidth0.6.0indirect
PyPIwin32-setctime1.2.0indirect
PyPIyamlfix1.19.1indirect
Dependency advisories 0

Installing pypi:labelme@7.5.0 pulls in 47 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

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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.