Публічний реєстр
Звіт про здоров'я програмного забезпеченнясхема 0.11.0 · метрики 1.13.0 · 2026-07-14 16:16 UTC

roboflow / inference

Turn any computer or edge device into a command center for your computer vision projects.

PythonВласна ліцензія★ 2 374 зірки⑂ 286 форківз лип. 2023 р.Переглянути на GitHub ↗
#agents#classification#computer-vision#deployment#docker#inference#inference-api#inference-server#instance-segmentation#jetson#machine-learning#object-detection#onnx#python#tensorrt#vit#yolo11#yolov12#yolov5#yolov8

roboflow/inference має індекс здоров’я 71 зі 100, що відповідає смузі «Добрий». Найвищий показник — Vitality (100/100), найнижчий — Security (1/100). Останнє оновлення — сьогодні. Більшість нещодавньої роботи виконують 2 учасники.

71
загалом / 100
Добрий

Індекс здоров'я програмного забезпечення

Метрики згруповано у зважені категорії на шкалі 1–100. Загальна оцінка починається як їхнє середнє; коли публічні дані активують Політику юрисдикцій високого ризику, рейтинг коригується й отримує верхню межу 49 («Під ризиком»). Готовність до ШІ не входить до індексу.

71
Відмінний85-100Зразковий; відповідає практично всім перевіреним критеріям
Добрий70-84Здоровий; незначні прогалини
Помірний50-69Прийнятний, але з помітними прогалинами; рекомендовано перевірку
У зоні ризику30-49Суттєві слабкі місця; впровадження потребує обережності
Критичний1-29Серйозні проблеми (покинутий, єдиний мейнтейнер, без базової гігієни)
ЖиттєздатністьСпільнота тавпровадженняСталість таврядуванняІнженернаякістьБезпекаГотовність доШІ

Профіль оцінок

Кожна вісь — окрема категорія. Форма важить більше, ніж середнє: здоровий об'єкт заповнює всю фігуру, тоді як профіль із піками та провалами означає, що сила в одному вимірі маскує ризик в іншому.

Власність

RoboflowОрганізація
5 137 підписників168 публічних репозиторіївз лип. 2019 р.

За цим репозиторієм стоїть організація — спільна, підзвітна опіка, здатна пережити будь-якого окремого мейнтейнера.

Пакетні екосистеми

РеєстрПакетВерсіяЗавантажень / місВерсіїОстання публікація
PyPIinference-models0.31.087 6481013 дні тому

Метрики за категоріями

Життєздатність

Чи живий проєкт — чи пишеться код і чи виходять релізи?

100Відмінний · 22% загального індексу

Активність розробки

100Відмінний
Як обчислюється оцінка
36/36Свіжість push — останній push 0 дн. тому
36/36Ритм комітів — 52/52 тижнів із комітами
18/18Обсяг комітів — 3 484 комітів за останній рік
0/10OpenSSF Scorecard: Maintained — немає даних
Використані вхідні дані
commits_last_year3 484
human_commit_share—
days_since_last_push0
active_weeks_last_year52

Дисципліна релізів

100Відмінний
Як обчислюється оцінка
27/27Випускає релізи — опубліковано 100 релізів
36/36Свіжість релізів — останній реліз 3 дн. тому
27/27Ритм релізів — реліз кожні ~5,5 дн.
0/10OpenSSF Scorecard: Signed-Releases — немає даних
Використані вхідні дані
releases_count100
latest_release_tagv1.3.5
releases_from_tagsні
days_since_latest_release3
mean_days_between_releases5,5

Спільнота та впровадження

Чи має проєкт користувачів, завантаження, увагу та влаштовані умови для контриб’юторів?

79Добрий · 18% загального індексу

Популярність і впровадження

83Добрий
Як обчислюється оцінка
54.8/60Зірки — 2 374 зірок
20.5/25Форки — 286 форків
8/15Спостерігачі — 29 спостерігачів
Використані вхідні дані
forks286
stars2 374
watchers29
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Здоров'я спільноти

71Добрий
Як обчислюється оцінка
22.5/22.5README
16.9/22.5Ліцензія — файл ліцензії наявний, не є визнаною ліцензією
18/18Настанови CONTRIBUTING
0/13.5Кодекс поведінки
0/7.2Шаблон issue
6.3/6.3Шаблон PR
Використані вхідні дані
has_readmeтак
has_licenseтак
has_contributingтак
has_issue_templateні
has_code_of_conductні
has_pull_request_templateтак

Впровадження в екосистемі (завантаження)

82Добрий
Як обчислюється оцінка
65.9/80Щомісячні завантаження — 87 648 завантажень/місяць у pypi
0/20Залежні пакети в реєстрі — ця екосистема цього не повідомляє
Використані вхідні дані
packagesinference-models
dependents—
ecosystemspypi
total_downloads—
monthly_downloads87 648
Виключено з оцінювання (немає даних або не застосовно): Залежні пакети в реєстрі. Залишкові ваги перенормовано.

Сталість та врядування

Чи переживе проєкт своїх людей — бас-фактор, реактивність, хто за ним стоїть і як супроводжуються пакети?

75Добрий · 24% загального індексу

Стійкість мейнтейнерів (бас-фактор)

59Помірний
Як обчислюється оцінка
25.2/54Бас-фактор — на 2 контриб’ютор(ів) припадає половина всіх комітів
14.6/22.5Розподіл комітів — головний контриб’ютор — автор 35% комітів
13.5/13.5Широта контриб’юторів — 100 контриб’юторів
0/10OpenSSF Scorecard: Contributors — немає даних
Використані вхідні дані
bus_factor2
contributors_sampled100
top_contributor_share0,349

Реактивність на issue та PR

71Добрий
Як обчислюється оцінка
28.1/46.8Вирішення issue — закрито 60% issue
32.2/38.3Прийняття PR — злито 1 999/2 371 вирішених PR
0/15OpenSSF Scorecard: Code-Review — немає даних
Використані вхідні дані
merged_prs1 999
open_issues78
closed_issues117
issue_closed_ratio0,6
closed_unmerged_prs372

Власність та опіка

80Добрий
Як обчислюється оцінка
30/30Підтримка власника — у власності організації
0/20Верифікований домен
25/25Охоплення власника — 5 137 підписників у roboflow
25/25Послужний список — 168 публічних репозиторіїв, вік облікового запису ~6 р.
Використані вхідні дані
followers5 137
owner_typeOrganization
is_verified—
owner_loginroboflow
public_repos168
account_age_days2 551

Супровід пакетів

100Відмінний
Як обчислюється оцінка
25/25Опубліковано й доступно — 1 пакет(ів) у pypi
35/35Свіжість публікацій — остання публікація 3 дн. тому
20/20Історія версій — 101 опублікованих версій
20/20Не застарілий — активний, не deprecated і не yanked
Використані вхідні дані
packagesinference-models
ecosystemspypi
any_deprecatedні
min_days_since_publish3

Інженерна якість

Чи наявні базові інженерні практики та документація?

84Добрий · 20% загального індексу

Інженерні практики

80Добрий
Як обчислюється оцінка
24/24Процеси CI — 63 процес(ів) CI
24/24Наявні тести
16/16Конфігурація лінтера — .eslintrc.json
0/9.6Pre-commit-хуки
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — немає даних
Використані вхідні дані
has_ciтак
has_testsтак
has_editorconfigні
has_linter_configтак
has_precommit_configні

Документація

90Відмінний
Як обчислюється оцінка
30/30README
25/25Каталог документації
15/15Сайт документації / домашня сторінка — https://inference.roboflow.com
10/10Опис репозиторію
10/10Теми — 20 тем
0/10Wiki
Використані вхідні дані
topicsagents, classification, computer-vision, deployment, docker, inference, inference-api, inference-server, instance-segmentation, jetson, machine-learning, object-detection, onnx, python, tensorrt, vit, yolo11, yolov12, yolov5, yolov8
has_wikiні
homepagehttps://inference.roboflow.com
has_readmeтак
has_docs_dirтак
has_descriptionтак

Безпека

Чи міцні видимі практики безпеки й ланцюга постачання, без непослабленої пов’язаності з юрисдикціями високого ризику?

1Критичний · 16% загального індексу

Стан безпеки

1Критичний
Як обчислюється оцінка
0/30Політика безпеки (SECURITY.md)
0/25Конфігурація Dependabot
0/25Файли фіксації залежностей — опублікована бібліотека — lockfile є справою застосунків, не очікується
0/20Процес CodeQL
Використані вхідні дані
sourcefile_signals
lockfilespackage-lock.json, uv.lock
manifestsinference_models/pyproject.toml, pyproject.toml, requirements/requirements.cli.txt, requirements/requirements.clip.txt, requirements/requirements.cloud_deploy.txt, requirements/requirements.cloud_storage.txt, requirements/requirements.code_analysis.txt, requirements/requirements.cpu.txt, requirements/requirements.docs.txt, requirements/requirements.doctr.txt, requirements/requirements.easyocr.txt, requirements/requirements.gpu.txt, requirements/requirements.groundingdino.txt, requirements/requirements.hosted.txt, requirements/requirements.http.txt, requirements/requirements.jetson.txt, requirements/requirements.modal.txt, requirements/requirements.pali.flash_attn.txt, requirements/requirements.parallel.txt, requirements/requirements.sam.txt, requirements/requirements.sam3.txt, requirements/requirements.sam3_3d.txt, requirements/requirements.sdk.http.txt, requirements/requirements.sdk.webrtc.txt, requirements/requirements.test.colab.txt, requirements/requirements.test.integration.txt, requirements/requirements.test.unit.txt, requirements/requirements.transformers.txt, requirements/requirements.vino.txt, requirements/requirements.yolo_world.txt, setup.py, theme_build/package.json
has_codeql_workflowні
has_security_policyні
has_dependabot_configні
Виключено з оцінювання (немає даних або не застосовно): Файли фіксації залежностей. Залишкові ваги перенормовано.

Готовність до ШІ

Наскільки репозиторій оснащений для розробки та супроводу за участі ШІ-агентів? Незалежний, експериментальний бейдж — вага 0.0, тож він подається окремо і не впливає на загальний індекс здоров'я.

81Добрий · 0% загального індексу

Контекст і настанови для агентів

75Добрий
Як обчислюється оцінка
45/45Інструкції для агентів — .cursor/rules/cli-options.mdc, .cursor/rules/empty-lines.mdc, .cursor/rules/execution-engine-version-changelog.mdc, .cursor/rules/function-call.mdc, .cursor/rules/google-docstrings.mdc, .cursor/rules/pathlib.mdc, .cursor/rules/pr-description.mdc, .cursor/rules/pydantic-field-descriptions.mdc, .cursor/rules/return-values.mdc, .cursor/rules/uv-package-management.mdc, AGENTS.md
0/15Машиночитана документація (llms.txt)
0/40Читабельна історія комітів — немає даних
Використані вхідні дані
has_llms_txtні
legible_history_share—
agent_instruction_files.cursor/rules/cli-options.mdc, .cursor/rules/empty-lines.mdc, .cursor/rules/execution-engine-version-changelog.mdc, .cursor/rules/function-call.mdc, .cursor/rules/google-docstrings.mdc, .cursor/rules/pathlib.mdc, .cursor/rules/pr-description.mdc, .cursor/rules/pydantic-field-descriptions.mdc, .cursor/rules/return-values.mdc, .cursor/rules/uv-package-management.mdc, AGENTS.md
agent_instruction_max_bytes4 044
Виключено з оцінювання (немає даних або не застосовно): Читабельна історія комітів. Залишкові ваги перенормовано.

Цикл верифікації (збірка / тести / перевірка типів)

100Відмінний
Як обчислюється оцінка
18/18Розгортання однією командою — Makefile
22/22Автоматизовані тести
11/11Конфігурація лінтера / форматера — .eslintrc.json
11/11Статична перевірка типів — inference/landing/tsconfig.json
10/10Відтворюване середовище — Dockerfile, lockfile
0/10Підтверджена практика роботи з агентами — немає даних
0/8Автоматизоване супроводження — немає даних
0/10OpenSSF Scorecard: Pinned-Dependencies — немає даних
Використані вхідні дані
has_nixні
has_testsтак
lockfilespackage-lock.json, uv.lock
has_dockerfileтак
typed_languageні
bootstrap_filesMakefile
has_devcontainerні
has_linter_configтак
typecheck_configsinference/landing/tsconfig.json
agent_commit_share—
toolchain_manifests—
dependency_bot_commit_share—
Виключено з оцінювання (немає даних або не застосовно): Підтверджена практика роботи з агентами, Автоматизоване супроводження. Залишкові ваги перенормовано.

Читабельність коду для моделей

81Добрий
Як обчислюється оцінка
27/45Типізований код — Python з конфігурацією перевірки типів (inference/landing/tsconfig.json)
54.4/55Керовані розміри файлів — 28/2 431 файлів вихідного коду понад 60 КБ
Використані вхідні дані
primary_languagePython
largest_source_bytes354 082
source_files_sampled2 431
oversized_source_files28

Машиночитні інтерфейси

40У зоні ризику
Як обчислюється оцінка
0/40Схема API (OpenAPI/GraphQL/proto)
0/20Сервер MCP
40/40Придатні до запуску приклади — examples, notebooks
Використані вхідні дані
example_dirsexamples, notebooks
has_mcp_signalні
api_schema_files—

Ключові факти

2 374зірок GitHub
100контриб'юторів
3 484комітів за останні 12 місяців
0днів від останнього пушу
100релізів
2бас-фактор
78відкритих issue
npm, PyPIпакетних екосистем

Попередження щодо збору даних

  • Could not fetch pypi package 'inference-development' from its registry
  • Could not fetch npm package 'inference-docs-theme' from its registry
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

Докладніше

Прямі залежності 45
РеєстрПакетОбмеження версіїМаніфест
PyPInumpy—inference_models/pyproject.toml
PyPItorch>=2.0.0,<3.0.0inference_models/pyproject.toml
PyPItorchvision—inference_models/pyproject.toml
PyPIopencv-python>=4.8.1.78inference_models/pyproject.toml
PyPIrequests>=2.33.0,<3.0.0inference_models/pyproject.toml
PyPIsupervision>=0.29.0inference_models/pyproject.toml
PyPIbackoff~=2.2.0inference_models/pyproject.toml
PyPItransformers>=5.5.0,<=5.9.0inference_models/pyproject.toml
PyPItimm>=1.0.0,<2.0.0inference_models/pyproject.toml
PyPIaccelerate>=1.0.0,<2.0.0inference_models/pyproject.toml
PyPIeinops>=0.7.0,<1.0.0inference_models/pyproject.toml
PyPIpeft>=0.18.1inference_models/pyproject.toml
PyPInum2words~=0.5.14inference_models/pyproject.toml
PyPIbitsandbytes>=0.46.1,<0.48.0inference_models/pyproject.toml
PyPIpyvips>=2.2.3,<3.0.0inference_models/pyproject.toml
PyPIrf-clip==1.1inference_models/pyproject.toml
PyPIpython-doctr>=1.0.0inference_models/pyproject.toml
PyPIpackaging>=24.0.0inference_models/pyproject.toml
PyPIrich>=14.1.0,<15.0.0inference_models/pyproject.toml
PyPIpydantic>=2.11.7,<3.0.0inference_models/pyproject.toml
PyPIfilelock>=3.20.3,<4.0.0inference_models/pyproject.toml
PyPIsegmentation-models-pytorch>=0.5.0,<1.0.0inference_models/pyproject.toml
PyPIscikit-image>=0.24.0,<0.26.0inference_models/pyproject.toml
PyPIeasyocr~=1.7.2inference_models/pyproject.toml
PyPIsentencepiece>=0.2.1,<0.3.0inference_models/pyproject.toml
PyPIrf_groundingdino==0.3.0inference_models/pyproject.toml
PyPItldextract~=5.1.2inference_models/pyproject.toml
PyPIpybase64~=1.0.0inference_models/pyproject.toml
PyPIrf-segment-anything==1.0inference_models/pyproject.toml
PyPIrf-sam-2==1.0.3inference_models/pyproject.toml
PyPIpycocotools>=2.0.10,<2.1.0inference_models/pyproject.toml
PyPIPyYAML>=6.0.0,<7.0.0inference_models/pyproject.toml
PyPIpyclipper>=1.3.0,<2.0.0inference_models/pyproject.toml
PyPIshapely>=2.0.0,<3.0.0inference_models/pyproject.toml
PyPIurllib3>=2.7.0,<3.0.0inference_models/pyproject.toml
PyPIpillow>=12.2.0,<13.0.0inference_models/pyproject.toml
PyPIGitPython>=3.1.50,<4.0.0inference_models/pyproject.toml
PyPICairoSVG>=2.9.0,<3.0.0inference_models/pyproject.toml
PyPIfonttools>=4.60.2,<5.0.0inference_models/pyproject.toml
PyPIPygments>=2.20.0,<3.0.0inference_models/pyproject.toml
PyPIidna>=3.15inference_models/pyproject.toml
npm@rive-app/canvas^2.25.7theme_build/package.json
npm@tailwindcss/line-clamp^0.4.4theme_build/package.json
npmgsap^3.12.7theme_build/package.json
npmswiper^12.1.2theme_build/package.json
Усі залежності 1 040

Повний розв'язаний набір залежностей із графа залежностей GitHub: 87 прямих і 953 непрямих (транзитивних) пакетів. Транзитивне замикання є повним, коли в репозиторії закомічено lockfile.

РеєстрПакетВерсіяЗв'язок
npm@rive-app/canvas2.25.7пряма
npm@tailwindcss/line-clamp0.4.4пряма
npmgsap3.12.7пряма
npmswiper12.1.2пряма
PyPIaccelerate—пряма
PyPIaccelerate1.7.0пряма
PyPIbackoff—пряма
PyPIbackoff2.2.1пряма
PyPIbitsandbytes—пряма
PyPIbitsandbytes0.47.0пряма
PyPIcairosvg2.9.0пряма
PyPIeasyocr1.7.2пряма
PyPIeinops—пряма
PyPIeinops0.8.1пряма
PyPIfilelock—пряма
PyPIfilelock3.29.0пряма
PyPIfonttools4.62.1пряма
PyPIgitpython3.1.50пряма
PyPIidna3.18пряма
PyPInum2words—пряма
PyPInum2words0.5.14пряма
PyPInumpy—пряма
PyPInumpy1.26.4пряма
PyPIopencv-python—пряма
PyPIopencv-python4.11.0.86пряма
PyPIpackaging—пряма
PyPIpackaging25.0пряма
PyPIpeft—пряма
PyPIpeft0.18.1пряма
PyPIpillow—пряма
PyPIpillow12.2.0пряма
PyPIpybase64—пряма
PyPIpybase641.0.2пряма
PyPIpyclipper1.3.0.post6пряма
PyPIpycocotools—пряма
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Звіт у форматі JSON машиночитний
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          },
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Пов’язані записи

Перевірені репозиторії, що мають спільні каталожні теги або екосистеми з roboflow/inference. Читати поруч →

93deepset-ai/haystackOpen-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.Відмінний90ray-project/rayRay is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.Відмінний88huggingface/transformers🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. Відмінний93apache/airflowApache Airflow - A platform to programmatically author, schedule, and monitor workflowsВідмінний90streamlit/streamlitStreamlit — A faster way to build and share data apps.Відмінний88microsoft/onnxruntimeONNX Runtime: cross-platform, high performance ML inferencing and training acceleratorВідмінний

Оцінки — це сигнали, а не гарантії. Вони відображають публічно видимі практики на GitHub — це не аудит коду й не гарантія безпеки.

Відсутні дані виключаються, а ваги перенормовуються — нуль за відсутність ніколи не ставиться. Методологія версіонована й відкрита: метрики v1.13.0, схема v0.11.0 — повна методологія · вікі метрик.

Як окремий результат виглядає на тлі всього реєстру: сукупна статистика — PyPI.