How to Cluster Images

Clustering, a fundamental unsupervised machine learning technique, is explored in the context of image data using tools like FiftyOne, Scikit-learn, and feature embeddings, such as those from the CLIP model. Unlike classification, clustering does not rely on predefined labels but rather uncovers inherent structures within data by grouping similar objects based on selected features. The article explains various clustering algorithms—centroid-based, density-based, and hierarchical—and emphasizes the importance of feature selection and dimensionality reduction techniques like UMAP to enhance clustering effectiveness. Using the FiftyOne platform, the blog demonstrates practical clustering applications, including visualizing clusters and labeling them with GPT-4V, highlighting the impact of different algorithms and hyperparameters. The discussion encourages experimenting with various models and parameters to optimally leverage clustering insights for better model training and data understanding, while also suggesting further exploration into different embedding models and clustering techniques for improved outcomes.

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