Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-21 17:40 UTC

qualcomm / ai-hub-models

Qualcomm® AI Hub Models is our collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.

PythonBSD-3-Clause★ 1,233 stars⑂ 214 forkssince Dec 2023View on GitHub ↗

KindCommand-line toolLibraryhow this is determined

#deeplearning #demos #inference #inference-api #inference-engine #machine-learning #machinelearning #onnx #pytorch #qnn #tensorflow-lite

qualcomm/ai-hub-models holds a health index of 89 out of 100, placing it in the Excellent band. It scores highest on Vitality (96/100) and lowest on Security (40/100). It was last updated today. 2 contributors account for most of its recent work.

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.

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)

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 75 is calibrated to 89 on the published index scale (record calibration 2026-08-02).

Ownership

Qualcomm Technologies, Inc.Organization

1,010 followers163 public repossince Sep 2019

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

Package ecosystems

Registry	Package	Version	Downloads / mo	Versions	Last publish
PyPI	qai_hub_models_cli	0.65.0	6,732	25	0 days ago
PyPI	qai_hub_modelspoints to another repo — not scored	0.65.0	5,662	122	0 days ago

Metrics by category

Vitality

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

96Exceptional · 21% of overall

Development activity

93Exceptional

How it's scored

36/36	Push recency — last push 0 days ago
29.8/36	Commit cadence — 43/52 weeks with commits
18/18	Commit volume — 1,330 commits in the last year
0/10	OpenSSF Scorecard: Maintained — no data

Inputs used

commits_last_year	1,330
human_commit_share	0.98
days_since_last_push	0
active_weeks_last_year	43

Release discipline

100Exceptional

How it's scored

27/27	Ships releases — 80 releases published
36/36	Release recency — latest release 14 days ago
27/27	Release cadence — a release every ~10.7 days
0/10	OpenSSF Scorecard: Signed-Releases — no data

Inputs used

releases_count	80
latest_release_tag	v0.64.0
releases_from_tags	no
days_since_latest_release	14
mean_days_between_releases	10.7

Community & Adoption

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

79Good · 17% of overall

Popularity & adoption

78Good

How it's scored

50.1/60	Stars — 1,233 stars
19.4/25	Forks — 214 forks
8/15	Watchers — 29 watchers

Inputs used

forks	214
stars	1,233
watchers	29
growth_state	unverified
growth_factor_pct	100
growth_unverified_reason	no_history

Community health

92Excellent

How it's scored

22.5/22.5	README
22.5/22.5	License — recognized license (BSD-3-Clause)
18/18	CONTRIBUTING guide
13.5/13.5	Code of conduct
0/7.2	Issue template
6.3/6.3	PR template

Inputs used

has_readme	yes
has_license	yes
readme_badges	4
has_contributing	yes
has_issue_template	no
has_code_of_conduct	yes
readme_badge_services	shields.io
has_pull_request_template	yes

Ecosystem adoption (downloads)

64Moderate

How it's scored

51/80	Monthly downloads — 6,732 downloads/month across pypi
0/20	Registry dependents — not reported by this ecosystem

Inputs used

packages	qai_hub_models_cli
dependents	—
ecosystems	pypi
total_downloads	—
monthly_downloads	6,732
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?

71Good · 23% of overall

Maintainer resilience (bus factor)

61Moderate

How it's scored

25.2/54	Bus factor — 2 contributor(s) cover half of all commits
16.1/22.5	Commit distribution — top contributor authored 28% of commits
13.5/13.5	Contributor breadth — 29 contributors
0/10	OpenSSF Scorecard: Contributors — no data

Inputs used

bus_factor	2
contributors_sampled	29
top_contributor_share	0.285

Issue & PR responsiveness

53Moderate

How it's scored

41.3/42	Issue resolution — 98% of issues closed
3.8/30	PR acceptance — 6/47 decided PRs merged
0/13	Newcomer PR acceptance — 0/11 first-time contributors' PRs merged in 30d
0/15	OpenSSF Scorecard: Code-Review — no data

Inputs used

merged_prs	6
open_issues	5
closed_issues	294
prs_merged_7d	0
prs_decided_7d	10
prs_merged_30d	0
prs_decided_30d	11
issue_closed_ratio	0.983
closed_unmerged_prs	41
first_time_authors_30d	4
first_time_prs_merged_30d	0
first_time_prs_decided_30d	11

Ownership & stewardship

77Good

How it's scored

30/30	Ownership backing — organization-owned
0/20	Verified domain
21.6/25	Owner reach — 1,010 followers of qualcomm
25/25	Track record — 163 public repos, account ~7 yr old

Inputs used

followers	1,010
owner_type	Organization
is_verified	no
owner_login	qualcomm
public_repos	163
account_age_days	2,594

Package maintenance

100Exceptional

How it's scored

25/25	Published & resolvable — 1 package(s) on pypi
35/35	Publish recency — latest publish 0 days ago
20/20	Version history — 25 published versions
20/20	Not deprecated — active, not deprecated or yanked

Inputs used

packages	qai_hub_models_cli
ecosystems	pypi
any_deprecated	no
min_days_since_publish	0

Engineering Quality

Are baseline engineering and documentation practices in place?

81Excellent · 19% of overall

Engineering practices

92Excellent

How it's scored

24/24	CI workflows — 40 workflow(s)
24/24	Tests present
16/16	Linter config — ruff.toml
9.6/9.6	Pre-commit hooks
0/6.4	.editorconfig
0/20	OpenSSF Scorecard: CI-Tests — no data

Inputs used

has_ci	yes
has_tests	yes
has_editorconfig	no
has_linter_config	yes
has_precommit_config	yes

Documentation

65Good

How it's scored

30/30	README
0/25	Documentation directory
15/15	Documentation / homepage site — https://aihub.qualcomm.com/models
10/10	Repository description
10/10	Topics — 11 topics
0/10	Wiki

Inputs used

topics	deeplearning, demos, inference, inference-api, inference-engine, machine-learning, machinelearning, onnx, pytorch, qnn, tensorflow-lite
has_wiki	no
homepage	https://aihub.qualcomm.com/models
docs_site	https://aihub.qualcomm.com/models
has_readme	yes
has_docs_dir	no
has_description	yes

Security

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

40Weak · 16% of overall

Security posture

25At Risk

How it's scored

0/30	Security policy (SECURITY.md)
25/25	Dependabot config
0/25	Dependency lockfiles
0/20	CodeQL workflow

Inputs used

source	file_signals
lockfiles	—
manifests	cli/pyproject.toml, src/pyproject.toml, src/setup.py
has_codeql_workflow	no
has_security_policy	no
has_dependabot_config	yes

Dependency advisories

100Exceptional

How it's scored

35/35	Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25	Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40	No advisories left outstanding — no advisory carries a publication date

Inputs used

source	osv
advisories	610
affected_packages	39
assessed_packages	229
unassessed_packages	57
affected_by_severity	critical 16, high 20, moderate 3
direct_affected_packages	0

Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 229 resolved dependencies against OSV. 57 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

Agent context & guidance

85Excellent

How it's scored

45/45	Agent instructions — CLAUDE.md
0/15	Machine-readable docs (llms.txt)
40/40	Legible commit history — 98 of 98 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	no
llms_txt_url	—
legible_history_share	1
agent_instruction_files	CLAUDE.md
agent_instruction_max_bytes	14,150

Verify loop (build / test / typecheck)

57Moderate

How it's scored

0/18	One-command bootstrap
22/22	Automated tests
11/11	Lint / format config — ruff.toml
0/11	Static type checking
0/10	Reproducible environment
10/10	Demonstrated agent practice — 48 of the last 100 commits agent-authored or agent-credited
8/8	Automated maintenance — 1 of the last 100 commits are automated dependency updates
0/10	OpenSSF Scorecard: Pinned-Dependencies — no data

Inputs used

has_nix	no
has_tests	yes
lockfiles	—
has_dockerfile	no
typed_language	no
bootstrap_files	—
has_devcontainer	no
has_linter_config	yes
typecheck_configs	—
agent_commit_share	0.48
toolchain_manifests	—
dependency_bot_commit_share	0.01

Code legibility for models

55Moderate

How it's scored

0/45	Type-checkable code — Python without a type-check config
54.9/55	Manageable file sizes — 10/3,710 source files over 60KB

Inputs used

primary_language	Python
largest_source_bytes	159,821
source_files_sampled	3,710
oversized_source_files	10

Machine-readable interfaces

100Exceptional

How it's scored

40/40	API schema (OpenAPI/GraphQL/proto) — proto/info.proto, proto/manifest.proto, proto/model_metadata.proto, proto/numerics.proto, proto/perf.proto, proto/platform.proto, proto/release_assets.proto, proto/shared/precision.proto, proto/shared/range.proto, proto/shared/runtime.proto, proto/shared/tensor_spec.proto, proto/shared/tool_versions.proto
0/20	MCP server — not applicable to this kind of software
40/40	Runnable examples — examples

Inputs used

example_dirs	examples
has_mcp_signal	no
api_schema_files	proto/info.proto, proto/manifest.proto, proto/model_metadata.proto, proto/numerics.proto, proto/perf.proto, proto/platform.proto, proto/release_assets.proto, proto/shared/precision.proto, proto/shared/range.proto, proto/shared/runtime.proto, proto/shared/tensor_spec.proto, proto/shared/tool_versions.proto
interfaces_expected_of	—

Excluded from scoring (no data or not applicable): MCP server. Remaining weights renormalized.

Key facts

1,233GitHub stars

29contributors

1,330commits, last 12 months

0days since last push

80releases

2bus factor

5open issues

PyPIpackage ecosystems

Data collection warnings

Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
pypi package 'qai_hub_models' points at a different repository (https://github.com/quic/ai-hub-models); excluded from ecosystem scoring
deps.dev does not index pypi:qai_hub_models_cli@0.65.0; advisories assessed against the repository dependency graph instead
OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit; 2026/10/21 17:39:39 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 ★ / 214 ⇿

0Stars

214Forks

80Releases

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.

Major 0Minor 41Patch 37

Each point covers 3 days.

Direct dependencies 7

Registry	Package	Version constraint	Manifest
PyPI	filelock	`>=3.16.1,<=3.29.0`	cli/pyproject.toml
PyPI	packaging	`>=24.2,<=26.2.0`	cli/pyproject.toml
PyPI	platformdirs	`>=4.0,<=4.9.6`	cli/pyproject.toml
PyPI	prettytable	`>=3.10,<=3.16.0`	cli/pyproject.toml
PyPI	protobuf	`>=4.25.9,<=7.34.1`	cli/pyproject.toml
PyPI	requests	`>=2.25,<=2.33.1`	cli/pyproject.toml
PyPI	tqdm	`>=4.66,<=4.67.3`	cli/pyproject.toml

All dependencies 286

Full resolved dependency set from the GitHub dependency graph: 12 direct and 274 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	filelock	`—`	direct
PyPI	packaging	`—`	direct
PyPI	packaging	`21.3`	direct
PyPI	packaging	`26.0`	direct
PyPI	platformdirs	`—`	direct
PyPI	prettytable	`—`	direct
PyPI	prettytable	`3.11.0`	direct
PyPI	prettytable	`3.17.0`	direct
PyPI	protobuf	`—`	direct
PyPI	requests	`—`	direct
PyPI	requests	`2.33.1`	direct
PyPI	tqdm	`—`	direct
PyPI	accelerate	`—`	indirect
PyPI	accelerate	`1.12.0`	indirect
PyPI	accelerate	`1.2.1`	indirect
PyPI	adbutils	`—`	indirect
PyPI	addict	`2.4.0`	indirect
PyPI	aimet-onnx	`—`	indirect
PyPI	aimet-onnx	`2.28.0`	indirect
PyPI	aimet-onnx	`2.33.0`	indirect
PyPI	aimet-onnx	`2.34.0`	indirect
PyPI	aimet-onnx	`2.35.0`	indirect
PyPI	aimet-onnx	`2.37.0`	indirect
PyPI	aimet-onnx	`2.38.0`	indirect
PyPI	aimet-onnx	`2.41`	indirect
PyPI	aioboto3	`12.3.0`	indirect
PyPI	aiofiles	`24.1.0`	indirect
PyPI	aiofiles	`25.1.0`	indirect
PyPI	aiohttp	`3.13.3`	indirect
PyPI	albumentations	`1.4.18`	indirect
PyPI	anyascii	`0.3.2`	indirect
PyPI	appium-python-client	`2.7.0`	indirect
PyPI	appium-python-client	`5.2.4`	indirect
PyPI	asteroid-filterbanks	`0.4.0`	indirect
PyPI	async-generator	`1.10`	indirect
PyPI	attrs	`22.1.0`	indirect
PyPI	audio2numpy	`0.1.2`	indirect
PyPI	av	`12.3.0`	indirect
PyPI	av	`14.0.1`	indirect
PyPI	boto3	`—`	indirect
PyPI	boto3-stubs	`—`	indirect
PyPI	boto3-stubs	`1.36.9`	indirect
PyPI	botocore	`—`	indirect
PyPI	build	`—`	indirect
PyPI	cached-path	`1.8.10`	indirect
PyPI	cachetools	`6.2.1`	indirect
PyPI	certifi	`2022.9.24`	indirect
PyPI	cffi	`1.15.1`	indirect
PyPI	cloudpickle	`3.1.1`	indirect
PyPI	cn2an	`0.5.22`	indirect
PyPI	colorama	`0.4.5`	indirect
PyPI	coverage	`—`	indirect
PyPI	datasets	`4.1.1`	indirect
PyPI	decord	`0.6.0`	indirect
PyPI	diffusers	`0.30.2`	indirect
PyPI	diffusers	`0.35.2`	indirect
PyPI	dm-env	`1.6`	indirect
PyPI	dm-tree	`0.1.8`	indirect
PyPI	easydict	`1.13`	indirect
PyPI	easyocr	`1.7.2`	indirect
PyPI	efficientnet-pytorch	`0.7.1`	indirect
PyPI	einops	`—`	indirect
PyPI	einops	`0.3.2`	indirect
PyPI	einops	`0.8.1`	indirect
PyPI	einops	`0.8.2`	indirect
PyPI	eng-to-ipa	`0.0.2`	indirect
PyPI	exceptiongroup	`1.0.0rc9`	indirect
PyPI	ffmpeg	`1.4`	indirect
PyPI	flash-linear-attention	`—`	indirect
PyPI	fugashi	`1.5.2`	indirect
PyPI	funasr	`1.3.12`	indirect
PyPI	fvcore	`0.1.5.post20210825`	indirect
PyPI	fvcore	`0.1.5.post20221221`	indirect
PyPI	g2p-en	`2.1.0`	indirect
PyPI	g2pkk	`—`	indirect
PyPI	gdown	`4.7.1`	indirect
PyPI	geffnet	`1.0.2`	indirect
PyPI	geopandas	`1.1.0`	indirect
PyPI	gitpython	`3.1.42`	indirect
PyPI	gitpython	`3.1.49`	indirect
PyPI	google-cloud-storage	`—`	indirect
PyPI	gradio	`6.14.0`	indirect
PyPI	grpcio-tools	`1.62.3`	indirect
PyPI	gruut	`2.4.0`	indirect
PyPI	gymnasium	`1.0.0`	indirect
PyPI	h11	`0.14.0`	indirect
PyPI	h5py	`3.12.1`	indirect
PyPI	hdf5storage	`0.2.2`	indirect
PyPI	httpx	`—`	indirect
PyPI	huggingface-hub	`—`	indirect
PyPI	huggingface-hub	`0.36.2`	indirect
PyPI	hydra-core	`—`	indirect
PyPI	hydra-core	`1.3.2`	indirect
PyPI	idna	`3.4`	indirect
PyPI	imageio	`2.34.2`	indirect
PyPI	imageio	`2.37.2`	indirect
PyPI	imgaug	`0.4.0`	indirect
PyPI	inflect	`7.0.0`	indirect
PyPI	iniconfig	`1.1.1`	indirect
PyPI	inputimeout	`—`	indirect
PyPI	ipython	`8.12.3`	indirect
PyPI	jamo	`0.4.1`	indirect
PyPI	jieba	`0.42.1`	indirect
PyPI	jinja2	`—`	indirect
PyPI	jiwer	`—`	indirect
PyPI	jiwer	`3.0.3`	indirect
PyPI	junitparser	`3.2.0`	indirect
PyPI	kornia	`0.5.0`	indirect
PyPI	kornia	`0.7.4`	indirect
PyPI	langid	`1.1.6`	indirect
PyPI	lerobot	`0.4.1`	indirect
PyPI	levenshtein	`—`	indirect
PyPI	librosa	`0.10.1`	indirect
PyPI	librosa	`0.11.0`	indirect
PyPI	llvmlite	`—`	indirect
PyPI	lmdb	`2.1.1`	indirect
PyPI	loguru	`—`	indirect
PyPI	lxml	`6.0.2`	indirect
PyPI	lyft-dataset-sdk	`0.0.8`	indirect
PyPI	matplotlib	`3.10.7`	indirect
PyPI	mecab-python3	`1.0.12`	indirect
PyPI	mmcv	`1.7.0`	indirect
PyPI	mmcv-lite	`2.1.0`	indirect
PyPI	mmdet	`2.27.0`	indirect
PyPI	mmdet	`3.3.0`	indirect
PyPI	mmengine	`0.10.7`	indirect
PyPI	mmsegmentation	`0.30.0`	indirect
PyPI	mujoco	`3.4.0`	indirect
PyPI	mypy	`1.19.1`	indirect
PyPI	mypy-protobuf	`3.6.0`	indirect
PyPI	nltk	`3.9.2`	indirect
PyPI	num2words	`0.5.12`	indirect
PyPI	numba	`—`	indirect
PyPI	numba	`0.59.1`	indirect
PyPI	numba	`0.62.1`	indirect
PyPI	numpy	`—`	indirect
PyPI	numpy	`1.26.4`	indirect
PyPI	numpydantic	`1.6.7`	indirect
PyPI	numpydoc	`1.9.0`	indirect
PyPI	nuscenes-devkit	`1.2.0`	indirect
PyPI	object-detection-metrics	`0.4.post1`	indirect
PyPI	omegaconf	`—`	indirect
PyPI	omegaconf	`2.3.0`	indirect
PyPI	onnx	`—`	indirect
PyPI	onnx	`1.17.0`	indirect
PyPI	onnx	`1.18.0`	indirect
PyPI	onnx-ir	`0.2.1`	indirect
PyPI	onnxruntime	`—`	indirect
PyPI	onnxruntime	`1.22`	indirect
PyPI	onnxruntime	`1.22.1`	indirect
PyPI	onnxscript	`0.7.0`	indirect
PyPI	onnxsim	`0.5.0`	indirect
PyPI	onnxsim	`0.6.5`	indirect
PyPI	onnxsim-prebuilt	`0.4.36.post1`	indirect
PyPI	onnxsim-prebuilt	`0.4.39.post2`	indirect
PyPI	opencv-python	`—`	indirect
PyPI	outcome	`1.2.0`	indirect
PyPI	pandas	`—`	indirect
PyPI	peft	`0.14.0`	indirect
PyPI	peft	`0.19.1`	indirect
PyPI	pillow	`—`	indirect
PyPI	pipablepytorch3d	`0.7.6`	indirect
PyPI	piper-phonemize	`1.1.0`	indirect
PyPI	piper-phonemize-cross	`1.2.1`	indirect
PyPI	pluggy	`1.0.0`	indirect
PyPI	plyfile	`—`	indirect
PyPI	pre-commit	`4.0.1`	indirect
PyPI	psutil	`—`	indirect
PyPI	psutil	`6.1.1`	indirect
PyPI	py	`1.11.0`	indirect
PyPI	pyannote-audio	`3.3.2`	indirect
PyPI	pyarrow	`21.0.0`	indirect
PyPI	pyaudio	`0.2.14`	indirect
PyPI	pycocotools	`2.0.11`	indirect
PyPI	pycparser	`2.21`	indirect
PyPI	pydantic	`—`	indirect
PyPI	pydantic	`2.10.6`	indirect
PyPI	pydantic-yaml	`1.4.0`	indirect
PyPI	pydub	`0.25.1`	indirect
PyPI	pykakasi	`2.3.0`	indirect
PyPI	pyogrio	`0.12.1`	indirect
PyPI	pyopengl	`3.1.10`	indirect
PyPI	pyparsing	`3.0.9`	indirect
PyPI	pypinyin	`0.50.0`	indirect
PyPI	pyquaternion	`0.9.9`	indirect
PyPI	pysocks	`1.7.1`	indirect
PyPI	pytest	`—`	indirect
PyPI	pytest	`7.1.3`	indirect
PyPI	pytest	`8.4.2`	indirect
PyPI	pytest-cov	`5.0.0`	indirect
PyPI	pytest-xdist	`—`	indirect
PyPI	pytorch-lightning	`—`	indirect
PyPI	pytorch-lightning	`1.2.10`	indirect
PyPI	pytorch-lightning	`2.6.0`	indirect
PyPI	pyyaml	`6.0.2`	indirect
PyPI	qai-hub	`—`	indirect
PyPI	qai-hub	`0.49.0`	indirect
PyPI	rasterio	`1.4.3`	indirect
PyPI	requests-toolbelt	`1.0.0`	indirect
PyPI	resampy	`0.4.3`	indirect
PyPI	retry	`0.9.2`	indirect
PyPI	rfdetr	`1.6.5`	indirect
PyPI	ruamel-yaml	`0.18.10`	indirect
PyPI	ruamel-yaml	`0.19.1`	indirect
PyPI	sacremoses	`0.2.0`	indirect
PyPI	safetensors	`0.6.2`	indirect
PyPI	schema	`0.7.5`	indirect
PyPI	scikit-image	`—`	indirect
PyPI	scikit-image	`0.22.0`	indirect
PyPI	scikit-learn	`—`	indirect
PyPI	scipy	`—`	indirect
PyPI	seaborn	`0.13.2`	indirect
PyPI	segment-anything	`1.0`	indirect
PyPI	selenium	`4.36.0`	indirect
PyPI	selenium	`4.5.0`	indirect
PyPI	sentencepiece	`0.2.1`	indirect
PyPI	sentencepiece	`0.2.2`	indirect
PyPI	setuptools	`—`	indirect
PyPI	setuptools-scm	`—`	indirect
PyPI	shapely	`2.1.2`	indirect
PyPI	silero-vad	`6.2.1`	indirect
PyPI	sniffio	`1.3.0`	indirect
PyPI	sortedcontainers	`2.4.0`	indirect
PyPI	sounddevice	`0.5.2`	indirect
PyPI	soundfile	`—`	indirect
PyPI	soundfile	`0.13.1`	indirect
PyPI	soundfile	`0.14.0`	indirect
PyPI	spconv	`2.3.8`	indirect
PyPI	supervision	`0.25.1`	indirect
PyPI	tabulate	`0.9.0`	indirect
PyPI	tensorboard	`2.13.0`	indirect
PyPI	tflite	`2.18.0`	indirect
PyPI	thop	`0.1.1.post2209072238`	indirect
PyPI	timm	`1.0.14`	indirect
PyPI	timm	`1.0.15`	indirect
PyPI	timm	`1.0.28`	indirect
PyPI	tomli	`2.0.1`	indirect
PyPI	torch	`—`	indirect
PyPI	torch	`2.10.0`	indirect
PyPI	torch	`2.4.1`	indirect
PyPI	torch	`2.6.0`	indirect
PyPI	torch	`2.9.0`	indirect
PyPI	torchaudio	`—`	indirect
PyPI	torchaudio	`2.4.1`	indirect
PyPI	torchaudio	`2.9.0`	indirect
PyPI	torchcodec	`—`	indirect
PyPI	torchmetrics	`—`	indirect
PyPI	torchmetrics	`1.4.0.post0`	indirect
PyPI	torchpack	`0.3.1`	indirect
PyPI	torchvision	`—`	indirect
PyPI	torchvision	`0.19.1`	indirect
PyPI	torchvision	`0.21.0`	indirect
PyPI	torchvision	`0.24.0`	indirect
PyPI	torchvision	`0.25.0`	indirect
PyPI	transformers	`—`	indirect
PyPI	transformers	`4.45`	indirect
PyPI	transformers	`4.45.0`	indirect
PyPI	transformers	`4.51.0`	indirect
PyPI	transformers	`4.51.3`	indirect
PyPI	transformers	`4.56.2`	indirect
PyPI	transformers	`4.57.6`	indirect
PyPI	transformers	`5.7.0`	indirect
PyPI	transformers	`5.9.0`	indirect
PyPI	trimesh	`2.35.39`	indirect
PyPI	trio	`0.22.0`	indirect
PyPI	trio-websocket	`0.9.2`	indirect
PyPI	triton	`3.5.1`	indirect
PyPI	txtsplit	`1.0.0`	indirect
PyPI	types-aiofiles	`25.1.0.20251011`	indirect
PyPI	types-pillow	`10.2.0.20240213`	indirect
PyPI	types-protobuf	`—`	indirect
PyPI	types-pyyaml	`6.0.12`	indirect
PyPI	types-requests	`2.31.0.6`	indirect
PyPI	types-tabulate	`0.9.0.20240106`	indirect
PyPI	typing-extensions	`—`	indirect
PyPI	ultralytics	`8.4.45`	indirect
PyPI	unidecode	`1.3.7`	indirect
PyPI	unidic	`1.1.0`	indirect
PyPI	unidic-lite	`1.0.8`	indirect
PyPI	urllib3	`1.26.12`	indirect
PyPI	wandb	`0.22.3`	indirect
PyPI	wcwidth	`0.5.3`	indirect
PyPI	webdataset	`0.2.86`	indirect
PyPI	wmi	`1.5.1`	indirect
PyPI	wsproto	`1.2.0`	indirect
PyPI	yacs	`0.1.8`	indirect

Dependency advisories 39

This repository publishes no package the index resolves, so its own dependency graph was assessed — 229 packages, which also include development and test pins that never ship: 39 carry known advisories, of which 0 are direct. 57 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

Package	Version	Relation	Severity	Advisories	Fixed in
aiohttp	`3.13.3`	indirect	critical	56	`3.14.4`
gitpython	`3.1.42`	indirect	critical	59	`3.1.62`
gitpython	`3.1.49`	indirect	critical	51	`3.1.62`
h11	`0.14.0`	indirect	critical	2	`0.16.0`
imgaug	`0.4.0`	indirect	critical	2	—
nltk	`3.9.2`	indirect	critical	86	`3.10.3`
onnx	`1.17.0`	indirect	critical	17	`1.22.0`
onnx	`1.18.0`	indirect	critical	16	`1.22.0`
pytorch-lightning	`1.2.10`	indirect	critical	13	`2.6.6`
torch	`2.4.1`	indirect	critical	23	`2.13.0`
transformers	`4.45`	indirect	critical	44	`5.10.0`
transformers	`4.45.0`	indirect	critical	44	`5.10.0`
transformers	`4.51.0`	indirect	critical	30	`5.10.0`
transformers	`4.51.3`	indirect	critical	30	`5.10.0`
transformers	`4.56.2`	indirect	critical	15	`5.10.0`
transformers	`4.57.6`	indirect	critical	11	`5.10.0`
accelerate	`1.12.0`	indirect	high	2	—
accelerate	`1.2.1`	indirect	high	2	—
certifi	`2022.9.24`	indirect	high	8	`2026.10.19`
diffusers	`0.30.2`	indirect	high	5	`0.38.0`
diffusers	`0.35.2`	indirect	high	5	`0.38.0`
gdown	`4.7.1`	indirect	high	2	`5.2.2`
geopandas	`1.1.0`	indirect	high	2	`1.1.2`
gradio	`6.14.0`	indirect	high	6	`6.16.0`
hydra-core	`1.3.2`	indirect	high	4	`1.4.0.dev9`
idna	`3.4`	indirect	high	4	`3.15`
lxml	`6.0.2`	indirect	high	2	`6.1.0`
pyarrow	`21.0.0`	indirect	high	2	`23.0.1`
pytorch-lightning	`2.6.0`	indirect	high	3	`2.6.6`
selenium	`4.5.0`	indirect	high	1	`4.14.0`
torch	`2.10.0`	indirect	high	2	`2.13.0`
torch	`2.6.0`	indirect	high	24	`2.13.0`
torch	`2.9.0`	indirect	high	5	`2.13.0`
transformers	`5.7.0`	indirect	high	4	`5.10.0`
transformers	`5.9.0`	indirect	high	2	`5.10.0`
urllib3	`1.26.12`	indirect	high	20	`2.8.0`
datasets	`4.1.1`	indirect	moderate	2	`5.0.1`
pytest	`7.1.3`	indirect	moderate	2	`9.0.3`
pytest	`8.4.2`	indirect	moderate	2	`9.0.3`

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.

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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.

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