Acquiring a dataset is just the beginning; the real challenge lies in refining it for training a Computer Vision model. Bloated, low-quality datasets waste resources and hamper model performance. The key to effective curation? Active Learning pipelines.

By employing Active Learning, teams can intelligently select data that significantly impacts the model's performance. This method focuses on the model's current needs, ensuring each data point is impactful. The result is a streamlined annotation process and a more accurate, efficient Computer Vision model.