Visual Genome is a large-scale dataset designed to improve the understanding of images through detailed annotations. It contains over 108,000 images, each accompanied by rich information, including object descriptions, attributes, and relationships between objects. This dataset helps in training computer vision models to recognize and interpret visual content more effectively. The primary goal of Visual Genome is to bridge the gap between visual perception and language understanding. By providing structured data, it enables researchers and developers to create applications that can analyze images in a way that mimics human understanding, enhancing tasks like image captioning and scene recognition.