ultralytics/yolov5
Some data may be outdated — see provenance labels.
Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export. observed · 2026-10-02
Health v2 · maintenance only
66/100
- Activity 97
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2347
- days_rel: n/a
- days_push: 19
- n_releases_24m: 0
Adoption not part of the score
58103 stars · 17460 forks observed · 2026-10-02
What it is AI-extracted, prompt v1, taxonomy v1, 2026-09-21, confidence 0.99
Ultralytics YOLOv5 is a PyTorch‑based library for real‑time object detection, instance segmentation, and image classification. It provides training, inference, and model export to multiple deployment formats.
Use cases
- detect objects in images or video streams
- perform instance segmentation on photos
- classify images using pretrained YOLO models
- train a custom object detection dataset
- export YOLO models to ONNX, TensorRT, CoreML, or TFLite for edge deployment
- run inference on GPU‑enabled servers or containers
- integrate YOLOv5 into Python applications or CI pipelines
When to choose
- You need fast, real‑time detection with a small model footprint
- You prefer a PyTorch ecosystem with easy model loading via torch.hub
- You want built‑in support for exporting to multiple deployment formats
- You require extensive documentation, community examples, and cloud integration
When to avoid
- Your project demands higher accuracy than YOLOv5 can provide
- You need a non‑Python or non‑PyTorch implementation
- You cannot comply with the AGPL‑3.0 license for commercial use
- You require specialized detection heads not supported by YOLO architecture
Facets
library · maturity active
image-processing computer-vision artificial-intelligence deep-learning computer-vision python windows cloud object-detection instance-segmentation image-classification pytorch onnx tflite coreml tensorrt gpu-accelerated agpl-3.0 linux macos docker
10 sources
- readme: https://github.com/ultralytics/yolov5 · fetched 2026-10-02 · e5a2eb57dbf8
- homepage: https://docs.ultralytics.com/yolov5/ · fetched 2026-09-07 · fecb01160021
- site_page: https://docs.ultralytics.com/quickstart · fetched 2026-09-07 · 4c07747fd0af
- site_page: https://docs.ultralytics.com/yolov5/quickstart-tutorial · fetched 2026-09-07 · 59387a58082d
- site_page: https://docs.ultralytics.com/integrations · fetched 2026-09-07 · d5a078892b61
- site_page: https://docs.ultralytics.com/ · fetched 2026-09-07 · a4563dcc4936
- site_page: https://docs.ultralytics.com/yolov5/environments/google-cloud-quickstart-tutorial · fetched 2026-09-07 · 720f4ef67e01
- site_page: https://docs.ultralytics.com/yolov5/environments/aws-quickstart-tutorial · fetched 2026-09-07 · 21f12bef6dc2
- site_page: https://docs.ultralytics.com/yolov5/environments/azureml-quickstart-tutorial · fetched 2026-09-07 · 287bb76e2195
- site_page: https://docs.ultralytics.com/yolov5/environments/docker-image-quickstart-tutorial · fetched 2026-09-07 · 3be34ca3558d
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ultralytics/yolov5 | main | 66 |
For agents
markdown · JSON · MCP: product_card(name="ultralytics/yolov5")
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem