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Ultralytics YOLO

State-of-the-art real-time object detection supporting YOLOv5 through v11.

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About

The Ultralytics package is the home of the modern YOLO family, bundling training, validation, inference, and export for real-time computer vision into one Python library and a single yolo command-line interface. It covers object detection, instance and semantic segmentation, image classification, pose estimation, oriented bounding boxes, and multi-object tracking, with pretrained weights spanning YOLOv5 through YOLO11 and newer generations in five sizes from nano to extra large. Models train on custom datasets with a few lines of Python and export to deployment formats such as ONNX, TensorRT, CoreML, and TFLite for edge and server inference on GPU or CPU. The code is open source under AGPL-3.0, with a separate enterprise license available for closed-source commercial products. It is one of the most widely used detection stacks in both industry and research, adopted by roboticists, ML engineers, and students who need accurate real-time vision without building a pipeline from scratch.

Should you use Ultralytics YOLO?

Pick it when

Pick Ultralytics YOLO when you need to train a real-time detector, segmenter, or pose model on your own labeled data with a few lines of Python and export to TensorRT, CoreML, or TFLite, and your project is open source or you hold the enterprise license.

Look elsewhere when

Skip it for closed-source commercial products unless you buy the enterprise license, since AGPL-3.0 applies. RF-DETR's Apache sizes or MMDetection avoid that, and YOLO-World handles classes you have no labels for yet.

Alternatives to Ultralytics YOLO

  • RF-DETR

    Apache 2.0 for Nano through Large with strong fine-tuning accuracy, but fewer task types: no oriented boxes or classification, and pose is a preview.

  • MMDetection

    Apache 2.0 with far more architectures, including RTMDet, but a steeper config-driven setup and an 8 GB GPU floor.

  • YOLO-World

    Detects new classes from text prompts without labeling at real-time speed, but GPL-3.0 copyleft, and zero-shot results usually trail a detector fine-tuned on your labels.

  • Detectron2

    Apache 2.0 with Mask R-CNN, DensePose, and panoptic segmentation for research, but more setup and less focus on edge export.

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