Verified project record

ultralytics/yolov5

yolov5 is a mature, production-proven PyTorch model for object detection, instance segmentation, and image classification. It provides capabilities for training, validation, inference, and export to various deployment formats.

Project overview

yolov5 is renowned for its speed, accuracy, and simplicity in object detection, making it a strong candidate for teams seeking an end-to-end computer vision workflow from training to deployment.

Project type: Model Runtime · Image & Vision · Video
Use cases: Data Analysis
Deployment: Refer to project documentation
License: AGPL-3.0

Best for

Developers and AI engineers who need a fast, accurate, and easy-to-use PyTorch model for object detection, instance segmentation, or image classification.
Teams that want an end-to-end workflow from training to multi-format export for deployment.

Key capabilities

Runs object detection inference on images, videos, streams, and webcam feeds via PyTorch Hub or detect.py.
Performs instance segmentation prediction using pretrained YOLOv5-seg models.
Supports image classification tasks.
Trains YOLOv5 models on custom datasets or the COCO dataset, supporting single and multi-GPU distributed training.
Exports trained models to deployment formats including ONNX, TensorRT, CoreML, and TFLite.

Limitations and risks

The AGPL-3.0 license requires acquiring an Enterprise License to integrate the model into commercial products and services.

Getting started

Clone the repository, navigate to the yolov5 directory, install dependencies with pip install -r requirements.txt, then run python detect.py --weights yolov5s.pt --source data/images/ to perform inference.
PyTorch version 1.8 or higher must be installed. A GPU is optional for inference and basic use.

Evidence and sources

README: Ultralytics YOLOv5 🚀 is a fast, accurate, and easy-to-use computer vision model developed by [Ultralytics](https://www.ultralytics.com/). Based on the [PyTorch](https://pytorch.or…
GitHub project description: Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
README: YOLOv5 is a mature, production-proven model that remains an excellent choice for fast and reliable [object detection](https://docs.ultralytics.com/tasks/detect), [instance segment…
README: Use YOLOv5 via [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) for inference. [Models](https://github.com/ultralytics/yolov5/tree/master/mod…
README: results.print() # Print results to console results.show() # Display results in a window results.save() # Save results to runs/detect/exp

Reviewed at: 2026-07-21T22:03:01Z