How to Train Your Dragon (Detector)

The article provides a comprehensive guide to creating high-quality deep learning datasets and models using ClearML and FiftyOne, with a focus on training a computer vision model for detecting dragons. It emphasizes the importance of a data-centric approach to machine learning, highlighting the role of meticulous dataset understanding, tracking, and improvement in developing high-performing models. The workflow involves using FiftyOne for building datasets and analyzing model results, while ClearML facilitates experiment tracking and management, enabling reproducibility and scalability. The article demonstrates the process through a practical example using the DETR model, with a new dragon image dataset, and showcases how integrating these tools helps in identifying model failures and iteratively improving both datasets and model performance. Through detailed steps, it illustrates how to automate tracking, log metrics, visualize data, evaluate model predictions, and address annotation issues, ultimately promoting a systematic approach for enhancing machine learning outcomes.