Welcome to the Ultralytics Assets Repository
The Ultralytics Assets Repository stands as a comprehensive resource for anyone delving into the world of computer vision. This platform is intricately designed to integrate with the Ultralytics YOLO ecosystem, offering a treasure trove of visual assets, pre-trained models, and meticulously curated datasets that bolster a range of capabilities including object detection, instance segmentation, image classification, pose estimation, and tracking.
Features at a Glance
Visual Assets
Ultralytics provides users with a selection of high-quality visual assets. These include various logos and banners that users can easily implement into their applications or use in collaboration projects involving Ultralytics tools. These assets are easily accessible and downloadable for use in various types of media or presentations.
Models at Your Fingertips
The repository houses powerful pre-trained models that are ready to be deployed. These models are designed to handle a wide array of computer vision tasks efficiently. Users can seamlessly download these models directly from the repository, enhancing convenience for developers and data scientists looking to incorporate advanced AI functionalities into their projects.
Datasets Ready for Action
To support the training and validation of machine learning models, Ultralytics offers extensive datasets. These repositories of annotated data are primed and ready for immediate use, facilitating model training, validation processes, and further machine learning explorations. Each dataset is carefully documented to guide users in its effective application.
Getting Started with Usage
Download Pretrained Models Seamlessly
Ultralytics YOLO frameworks prioritize user convenience. When a pre-trained model is required but missing, it will automatically be downloaded from the assets repository, ensuring an uninterrupted workflow for the user. For instance, a line of code can be executed for efficient model inference:
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)
Embrace the Visuals
All visual assets offered by Ultralytics can be easily accessed and utilized for various projects, whether for development purposes, personal presentations, or professional documentation.
Explore Our Datasets
Ultralytics datasets are available via the repository releases and come with extensive documentation to aid users in their application. Users are encouraged to examine the licenses and specific guidelines for each dataset to ensure compatibility with their project goals and needs.
Contribute
Ultralytics warmly welcomes community contributions. Whether it's fixing bugs, introducing new features, or refining documentation, every input is valued and appreciated. Contributors can start by reviewing the Contributing Guide. Ultralytics also offers a survey to gather user insights and feedback on experiences with their products, helping the organization to grow and improve continuously.
License
Ultralytics offers two licensing options tailored to varying needs:
- AGPL-3.0 License: Suitable for students and hobbyists, this open-source license fosters collaboration and knowledge sharing.
- Enterprise License: Aimed at commercial applications, this license facilitates the integration of Ultralytics software and models into commercial products, bypassing the open-source requirements of AGPL-3.0. Commercial users can contact Ultralytics for further information regarding licensing.
Contact Us
Ultralytics is open to receiving bug reports, feature requests, and contributions through GitHub Issues. Additionally, for questions and broader discussions related to Ultralytics projects, users are encouraged to join the community on Discord.
This repository, with its wealth of resources, stands as an invaluable tool for developers, researchers, and enthusiasts eager to push the boundaries of what's possible in the realm of computer vision.