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🌟 Welcome to the Ultralytics Assets Repository

Welcome! You've arrived at the Ultralytics Assets repository, your one-stop hub for visual assets, powerful pre-trained models, and carefully curated datasets. These tools are meticulously crafted to complement the Ultralytics YOLO ecosystem, providing capabilities that span object detection, instance segmentation, image classification, pose estimation, and tracking.

🛠 Features at a Glance

🖼 Visual Assets: Dive into our collection of banners and logos that you can incorporate into your applications or as part of your collaboration with Ultralytics tools.
🤖 Models at Your Fingertips: Tap into the power of pre-trained models, fine-tuned and ready to deploy. These models are optimized to tackle a wide range of computer vision tasks with ease and precision.
📦 Datasets Ready for Action: Enhance your machine learning projects with our repositories of annotated data, primed for model training, validation, and beyond.

💡 Getting Started with Usage

📥 Download Pretrained Models Seamlessly

Ultralytics YOLO frameworks are engineered for convenience—missing a pre-trained model? It will be automatically fetched from this very repository.

For example:

from ultralytics import YOLO

# Instantiating a pre-trained YOLOv8n model
model = YOLO("yolov8n.pt")

# Path to your image
source = "path/to/image.jpg"

# Perform inference with just one line
results = model(source)  # This command completes the inference cycle and returns detection results

🌐 Embrace the Visuals

All our visual assets are at your fingertips, downloadable straight from the main branch for your projects, presentations, or documentation.

📚 Explore Our Datasets

Our datasets are accessible via repository releases and come with comprehensive READMEs to guide you through the process. Make sure to review the licenses and specific guidance for each dataset to align with your project needs.

🤝 Contribute

We welcome contributions from the community! Whether you're fixing bugs, adding new features, or improving documentation, your input is invaluable. Take a look at our Contributing Guide to get started. Also, we'd love to hear about your experience with Ultralytics products. Please consider filling out our Survey. A huge 🙏 and thank you to all of our contributors!

<a href="https://github.com/ultralytics/yolov5/graphs/contributors"> <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" alt="Ultralytics open-source contributors"></a>

©️ License

Ultralytics is excited to offer two different licensing options to meet your needs:

AGPL-3.0 License: Perfect for students and hobbyists, this OSI-approved open-source license encourages collaborative learning and knowledge sharing. Please refer to the LICENSE file for detailed terms.
Enterprise License: Ideal for commercial use, this license allows for the integration of Ultralytics software and AI models into commercial products without the open-source requirements of AGPL-3.0. For use cases that involve commercial applications, please contact us via Ultralytics Licensing.

📬 Contact Us

For bug reports, feature requests, and contributions, head to GitHub Issues. For questions and discussions about this project and other Ultralytics endeavors, join us on Discord!