Nearest Neighbor Embeddings Search with Qdrant and FiftyOne

FiftyOne and Qdrant are utilized together to perform efficient nearest neighbor searches on embeddings for image and video datasets, offering a flexible and repeatable process that reduces annotation costs and enhances dataset quality. This process involves using neural network embeddings, which capture data semantics, to facilitate tasks such as classification and anomaly detection without needing custom networks for each problem, often leveraging pre-trained models. Qdrant serves as an open-source vector database to conduct approximate nearest neighbor searches on dense embeddings, while FiftyOne aids in dataset management, visualization, and model evaluation. The integration of these tools allows for seamless workflows, including auto-labeling and evaluating results, as demonstrated with the MNIST dataset classification example. This collaboration between Qdrant and Voxel51 underscores the capability to enhance AI workflows by integrating new ground truth labels and repeating the pre-annotation process, thereby producing higher-quality datasets efficiently.