Large-scale image annotation remains a critical bottleneck in training high-performing machine learning models, particularly for multi-class classification tasks. Existing annotation tools often lack dedicated support for classification and fail to integrate human-in-the-loop strategies that balance automation with expert supervision.

We introduce PyTAGIT, an open-source annotation framework designed for fast, scalable, and interactive multi-class labeling. PyTAGIT combines AI-assisted classification with intuitive user interactions such as drag-and-drop assignment, t-SNE-based exploration, and iterative refinement through confidence thresholds. The tool operates efficiently even on mid-range laptops and supports datasets with thousands of samples and dozens of classes.