ZSL datasets

Zero-Shot Learning (ZSL) datasets are collections used to evaluate how well a computer system can recognize objects or concepts it hasn't seen during training. They contain labeled examples for some categories (like animals or objects) and descriptions or attributes for others that are not included in the training data. ZSL models learn to relate visual features to these descriptions, enabling them to identify new categories without direct examples. These datasets help develop AI that can generalize better, making it more flexible and capable of understanding a wider range of concepts beyond its initial training.