Public record
Software health reportschema 0.13.0 · metrics 2.10.0 · 2026-07-18 16:21 UTC

nvidia / nvimagecodec

A nvImageCodec library of GPU- and CPU- accelerated codecs featuring a unified interface

Jupyter Notebook · C++Apache-2.0★ 152 stars⑂ 18 forkssince Oct 2023View on GitHub ↗
#computer-vision#cpp#cuda#dali#data-processing#deep-learning#fast-data-pipeline#gpu#image-processing#machine-learning#nvidia#python#pytorch

nvidia/nvimagecodec holds a health index of 50 out of 100, placing it in the Moderate band. It scores highest on Vitality (67/100) and lowest on Security (22/100). It was last updated 3 days ago. A single contributor accounts for most of its recent work.

50
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.

50
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 50 is calibrated to 50 on the published index scale (record calibration 2026-08-02).

Ownership

NVIDIA CorporationOrganization
28,104 followers768 public repossince May 2012

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Metrics by category

Vitality

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

67Good · 21% of overall

Development activity

50Moderate
How it's scored
36/36Push recency — last push 3 days ago
3.5/36Commit cadence — 5/52 weeks with commits
9.4/18Commit volume — 10 commits in the last year
1/10OpenSSF Scorecard: Maintained — 2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
Inputs used
commits_last_year10
human_commit_share—
days_since_last_push3
active_weeks_last_year5

Release discipline

92Excellent
How it's scored
27/27Ships releases — 9 releases published
36/36Release recency — latest release 3 days ago
19.8/27Release cadence — a release every ~111.5 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count9
latest_release_tagv0.9.0
releases_from_tagsno
days_since_latest_release3
mean_days_between_releases111.5
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?

59Moderate · 17% of overall

Popularity & adoption

50Moderate
How it's scored
35.3/60Stars — 152 stars
10.3/25Forks — 18 forks
4.7/15Watchers — 8 watchers
Inputs used
forks18
stars152
watchers8
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

70Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges—
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateno

Sustainability & Governance

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

52Moderate · 23% of overall

Maintainer resilience (bus factor)

26At Risk
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
9.4/22.5Commit distribution — top contributor authored 58% of commits
4.1/13.5Contributor breadth — 3 contributors
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled3
top_contributor_share0.583

Issue & PR responsiveness

34At Risk
How it's scored
20.5/42Issue resolution — 49% of issues closed
9/30PR acceptance — 3/10 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 1/21 approved changesets -- score normalized to 0
Inputs used
merged_prs3
open_issues20
closed_issues19
prs_merged_7d—
prs_decided_7d—
prs_merged_30d—
prs_decided_30d—
issue_closed_ratio0.487
closed_unmerged_prs7
first_time_authors_30d—
first_time_prs_merged_30d—
first_time_prs_decided_30d—
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

100Exceptional
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain — verified-domain status not read for this organization
25/25Owner reach — 28,104 followers of NVIDIA
25/25Track record — 768 public repos, account ~14 yr old
Inputs used
followers28,104
owner_typeOrganization
is_verified—
owner_loginNVIDIA
public_repos768
account_age_days5,181
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

44Weak · 19% of overall

Engineering practices

24At Risk
How it's scored
0/24CI workflows
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — 0 out of 3 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_cino
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

75Good
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://docs.nvidia.com/cuda/nvimagecodec/index.html
10/10Repository description
10/10Topics — 13 topics
10/10Wiki
Inputs used
topicscomputer-vision, cpp, cuda, dali, data-processing, deep-learning, fast-data-pipeline, gpu, image-processing, machine-learning, nvidia, python, pytorch
has_wikiyes
homepagehttps://docs.nvidia.com/cuda/nvimagecodec/index.html
docs_sitehttps://docs.nvidia.com/cuda/nvimagecodec/index.html
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

22At Risk · 16% of overall

Security posture

22At Risk
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — branch protection not enabled on development/release branches
0/2.5CI-Tests — 0 out of 3 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 1/21 approved changesets -- score normalized to 0
0.8/2.5Contributors — project has 1 contributing companies or organizations -- score normalized to 3
0/10Dangerous-Workflow — no data
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
0.8/7.5Maintained — 2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
0/5Packaging — no data
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
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — no data
0/7.5Vulnerabilities — 40 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated14
scorecard_versionv5.5.0
checks_inconclusive4
scorecard_aggregate2.2
Excluded from scoring (no data or not applicable): Dangerous-Workflow, Packaging, Signed-Releases, Token-Permissions. Remaining weights renormalized.

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.

39Weak · 4% of overall

Agent context & guidance

1Critical
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit history — no data
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share—
agent_instruction_files—
agent_instruction_max_bytes—
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.

Verify loop (build / test / typecheck)

39Weak
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no data
0/8Automated maintenance — no data
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configno
typecheck_configs—
agent_commit_share—
toolchain_manifests—
dependency_bot_commit_share—
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.

Code legibility for models

54Moderate
How it's scored
0/45Type-checkable code — Jupyter Notebook without a type-check config
54.5/55Manageable file sizes — 4/436 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes136,564
source_files_sampled436
oversized_source_files4

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 — example, notebooks, recipes
Inputs used
example_dirsexample, notebooks, recipes
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

152GitHub stars
3contributors
10commits, last 12 months
3days since last push
9releases
1bus factor
20open issues
PyPIpackage ecosystems

More detail

OpenSSF Scorecard 2.2 / 10
2.2aggregate

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 · 2026-07-18 16:21 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
0CI-Tests0 out of 3 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 1/21 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
n/aDangerous-Workflowno workflows found
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
1Maintained2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
n/aToken-PermissionsNo tokens found
0Vulnerabilities40 existing vulnerabilities detected
All dependencies 29

Full resolved dependency set from the GitHub dependency graph: 0 direct and 29 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIcuda-python—indirect
PyPIcvcuda-cu12—indirect
PyPIcvcuda-cu13—indirect
PyPIhighdicom—indirect
PyPIipython—indirect
PyPInose—indirect
PyPInose2—indirect
PyPInumpy2.3.4indirect
PyPInvidia-cuda-runtime—indirect
PyPInvidia-cuda-runtime-cu12—indirect
PyPInvidia-libnvcomp-cu12—indirect
PyPInvidia-libnvcomp-cu13—indirect
PyPInvidia-nvjpeg—indirect
PyPInvidia-nvjpeg-cu12—indirect
PyPInvidia-nvjpeg2k-cu12—indirect
PyPInvidia-nvjpeg2k-cu13—indirect
PyPInvidia-nvtiff-cu12—indirect
PyPInvidia-nvtiff-cu13—indirect
PyPIopencv-python4.11.0.86indirect
PyPIpillow12.0.0indirect
PyPIpydicom3.0.2indirect
PyPIpydicom-data1.0.0indirect
PyPIpylibjpeg—indirect
PyPIpylibjpeg-libjpeg—indirect
PyPIpylibjpeg-openjpeg—indirect
PyPIpynvml11.5.3indirect
PyPIpytest8.3.4indirect
PyPIpytest-xdist—indirect
PyPItcia-utils—indirect
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 v0.13.0 — full methodology · metrics wiki.

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