ImageNet is currently the world's largest image recognition database, primarily used for image classification and object detection in the field of machine vision. The dataset is organized according to the WordNet hierarchical structure, where each node (also called a category) consists of hundreds or even thousands of images, with an average of over 500 images per node. The dataset contains a total of 22,000 image categories and approximately 15 million images. The ImageNet Dataset was first released by Fei-Fei Li and others from Stanford University at the Society for Visual Science in 2009, and has been continuously improved in the ImageNet Visual Recognition Challenge (ILSVRC) since 2010.