ultralytics/yolov5
Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export. observed · 2026-08-28
Health v2 · maintenance only
67/100
- Activity 99
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2298
- days_rel: n/a
- days_push: 9
- n_releases_24m: 0
Adoption not part of the score
57929 stars · 17475 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Ultralytics YOLOv5 is a PyTorch-based computer vision model family for real-time object detection, instance segmentation, and image classification, with training and export to formats like ONNX, CoreML, TFLite, and TensorRT. It is now labeled a legacy model, with Ultralytics recommending newer successors such as YOLOv8, YOLO11, and YOLO26 for new projects.
Use cases
- detect objects in images and video in real time
- train a custom object detection model on my own dataset
- run instance segmentation on images
- classify images with a pretrained model
- export a model to ONNX or TensorRT for edge deployment
- run inference via PyTorch Hub with a few lines of code
- deploy a detection model on cloud GPUs like AWS or GCP
When to choose
- you need a battle-tested, widely documented real-time object detection model
- you want easy training on custom datasets with extensive tutorials
- you need multi-format export (ONNX, CoreML, TFLite, TensorRT) for deployment
- you are maintaining an existing YOLOv5-based pipeline
When to avoid
- you are starting a new project and want the latest accuracy and features - prefer YOLOv8, YOLO11, or YOLO26
- you need a permissively licensed model for closed-source commercial products - YOLOv5 is AGPL-3.0 and requires an enterprise license
- you need tasks beyond detection, segmentation, and classification such as pose estimation or tracking
Facets
library · maturity maintenance
machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning artificial-intelligence python cross-platform yolo object-detection instance-segmentation image-classification pytorch onnx tensorrt model-export model-training inference real-time gpu docker
10 sources
- readme: https://github.com/ultralytics/yolov5 · fetched 2026-08-28 · e5a2eb57dbf8
- homepage: https://docs.ultralytics.com/yolov5/ · fetched 2026-08-28 · 5801dc8a198f
- site_page: https://docs.ultralytics.com/quickstart · fetched 2026-08-28 · 80d7989c34f3
- site_page: https://docs.ultralytics.com/yolov5/quickstart-tutorial · fetched 2026-08-28 · ebf4e4a49f02
- site_page: https://docs.ultralytics.com/integrations · fetched 2026-08-28 · 1dec60b917d0
- site_page: https://docs.ultralytics.com/ · fetched 2026-08-28 · fba5847c3417
- site_page: https://docs.ultralytics.com/yolov5/environments/google-cloud-quickstart-tutorial · fetched 2026-08-28 · 8b0428f5bac8
- site_page: https://docs.ultralytics.com/yolov5/environments/aws-quickstart-tutorial · fetched 2026-08-28 · 2d9d1af1db07
- site_page: https://docs.ultralytics.com/yolov5/environments/azureml-quickstart-tutorial · fetched 2026-08-28 · 37bce7d9a4f4
- site_page: https://docs.ultralytics.com/yolov5/environments/docker-image-quickstart-tutorial · fetched 2026-08-28 · b20ef4e84ca2
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ultralytics/yolov5 | main | 67 |
For agents
markdown · JSON · MCP: product_card(name="ultralytics/yolov5")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem