Contributing to the Ultralytics Hub
Last updated September 4, 2024
The Ultralytics Hub is a collaborative platform where users can share their trained YOLOv8 models and datasets, making them available to the wider community. Contributing to the Hub benefits both you and others by expanding the pool of readily available resources for object detection.
Contributing a YOLOv8 Model
- Model Training: Train a YOLOv8 model on your custom dataset, ensuring high accuracy and a robust performance.
- Model Evaluation: Thoroughly evaluate your model's performance on a separate validation dataset to quantify its capabilities.
- Model Documentation: Provide detailed documentation for your model, including:
- Dataset used for training
- Model architecture and hyperparameters
- Performance metrics (mAP, inference speed)
- Usage instructions and potential applications
- Submission: Follow the Ultralytics Hub guidelines for submitting your model, including required file formats and metadata.
Contributing a Dataset
- Data Collection: Gather a comprehensive and well-labeled dataset relevant to your specific object detection task.
- Data Annotation: Annotate your dataset accurately and consistently using appropriate tools.
- Data Format: Ensure that your dataset conforms to the expected format for the Ultralytics Hub, including image and annotation files.
- Dataset Documentation: Provide a detailed description of your dataset, including:
- Object classes and their definitions
- Data collection procedures
- Any specific aspects of your data that might be important for users
- Submission: Adhere to the Ultralytics Hub guidelines for dataset submissions.
Benefits of Contributing
- Sharing Knowledge: Contribute to the progress of object detection by sharing your expertise with the community.
- Community Recognition: Receive recognition for your contributions and engage with fellow developers.
- Collaboration Opportunities: Connect with other developers and potentially collaborate on projects or tasks.
- Impactful Contribution: Help build a valuable resource for researchers, developers, and anyone interested in object detection.
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