YOLOv5

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Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are fast, accurate, and easy to use. They excel at object detection, tracking, instance segmentation, semantic segmentation, image classification, and pose estimation tasks.

Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums!

Request an Enterprise License for commercial use at Ultralytics Licensing.

📄 Documentation

See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full Ultralytics Docs.

<details open> <summary>Install</summary>

Install the ultralytics package, including all requirements, in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

For alternative installation methods, including Conda, Docker, and building from source via Git, please consult the Quickstart Guide.

</details> <details open> <summary>Usage</summary>

CLI

You can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the yolo command:

# Predict using a pretrained YOLO model (e.g., YOLOv5n) on an image
yolo predict model=yolov5n.pt source='https://ultralytics.com/images/bus.jpg'

The yolo command supports various tasks and modes, accepting additional arguments like imgsz=640. Explore the YOLO CLI Docs for more examples.

Python

Ultralytics YOLO can also be integrated directly into your Python projects. It accepts the same configuration arguments as the CLI:

from ultralytics import YOLO

# Load a pretrained YOLOv5n model
model = YOLO("yolov5n.pt")

# Train the model on the COCO8 dataset for 100 epochs
train_results = model.train(
    data="coco8.yaml",  # Path to dataset configuration file
    epochs=100,  # Number of training epochs
    imgsz=640,  # Image size for training
    device="cpu",  # Device to run on (e.g., 'cpu', 0, [0,1,2,3])
)

# Evaluate the model's performance on the validation set
metrics = model.val()

# Perform object detection on an image
results = model("path/to/image.jpg")  # Predict on an image
results[0].show()  # Display results

# Export the model to ONNX format for deployment
path = model.export(format="onnx")  # Returns the path to the exported model

Discover more examples in the YOLO Python Docs.

</details>

✨ Models

Ultralytics supports a wide range of YOLO models, from early versions like YOLOv3 to the latest YOLO26. The tables below showcase YOLOv5 models pretrained on COCO for Detection and Segmentation, and Classification models pretrained on ImageNet. Tracking mode is compatible with Detection and Segmentation models. All Models download automatically from the latest Ultralytics release on first use.

<a href="https://docs.ultralytics.com/tasks" target="_blank"> <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/docs/ultralytics-yolov8-tasks-banner.avif" alt="Ultralytics YOLO supported tasks"> </a> <br> <br> <details open><summary>Detection (COCO)</summary>

Explore the Detection Docs for usage examples. These models are trained on the COCO dataset, featuring 80 object classes.

• All checkpoints are trained to 300 epochs with default settings. Nano and Small models use hyp.scratch-low.yaml hyps, all others use hyp.scratch-high.yaml.
• mAP^val values are for single-model single-scale on COCO val2017 dataset.<br>Reproduce by python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
• Speed averaged over COCO val images using a AWS p3.2xlarge instance. NMS times (~1 ms/img) not included.<br>Reproduce by python val.py --data coco.yaml --img 640 --task speed --batch 1
• TTA Test Time Augmentation includes reflection and scale augmentations.<br>Reproduce by python val.py --data coco.yaml --img 1536 --iou 0.7 --augment

</details> <details><summary>Segmentation (COCO)</summary>

Refer to the Segmentation Docs for usage examples. These models are trained on COCO-Seg, including 80 classes.

• All checkpoints are trained to 300 epochs with SGD optimizer with lr0=0.01 and weight_decay=5e-5 at image size 640 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official
• Accuracy values are for single-model single-scale on COCO dataset.<br>Reproduce by python segment/val.py --data coco.yaml --weights yolov5s-seg.pt
• Speed averaged over 100 inference images using a Colab Pro A100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image). <br>Reproduce by python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1
• Export to ONNX at FP32 and TensorRT at FP16 done with export.py. <br>Reproduce by python export.py --weights yolov5s-seg.pt --include engine --device 0 --half

</details> <details><summary>Classification (ImageNet)</summary>

Consult the Classification Docs for usage examples. These models are trained on ImageNet, covering 1000 classes.

• All checkpoints are trained to 90 epochs with SGD optimizer with lr0=0.001 and weight_decay=5e-5 at image size 224 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
• Accuracy values are for single-model single-scale on ImageNet-1k dataset.<br>Reproduce by python classify/val.py --data ../datasets/imagenet --img 224
• Speed averaged over 100 inference images using a Google Colab Pro V100 High-RAM instance.<br>Reproduce by python classify/val.py --data ../datasets/imagenet --img 224 --batch 1
• Export to ONNX at FP32 and TensorRT at FP16 done with export.py. <br>Reproduce by python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224

</details>

🧩 Integrations

Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with partners like Weights & Biases, Comet ML, Roboflow, and Intel OpenVINO, can optimize your AI workflow. Explore more at Ultralytics Integrations.

<a href="https://platform.ultralytics.com" target="_blank"> <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations"> </a>

🤝 Contribute

We thrive on community collaboration! Ultralytics YOLO wouldn't be the SOTA framework it is without contributions from developers like you. Please see our Contributing Guide to get started. We also welcome your feedback—share your experience by completing our Survey. A huge Thank You 🙏 to everyone who contributes!

<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=1280 -->

We look forward to your contributions to help make the Ultralytics ecosystem even better!

📜 License

Ultralytics offers two licensing options to suit different needs:

• AGPL-3.0 License: This OSI-approved open-source license is perfect for students, researchers, and enthusiasts. It encourages open collaboration and knowledge sharing. See the LICENSE file for full details.
• Ultralytics Enterprise License: For development and production use, this license enables seamless integration of Ultralytics software and AI models into business products and services, including internal tools, automated workflows, and production deployments, bypassing the open-source requirements of AGPL-3.0. To get started, please contact us via Ultralytics Licensing.

📞 Contact

For bug reports and feature requests related to Ultralytics software, please visit GitHub Issues. For questions, discussions, and community support, join our active communities on Discord, Reddit, and the Ultralytics Community Forums. We're here to help with all things Ultralytics!

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