ImageNet is formally a project aimed at (manually) labeling and categorizing images into almost 22,000 separate object categories for the purpose of computer vision research.

The goal of ImageNet Large Scale Visual Recognition Challenge, or ILSVRC for short, is to train a model that can correctly classify an input image into 1,000 separate object categories. Models are trained on ~1.2 million training images with another 50,000 images for validation and 100,000 images for testing. These 1,000 image categories represent object classes that we encounter in our day-to-day lives, such as species of dogs, cats, various household objects, vehicle types, and much more.

https://www.pyimagesearch.com/2017/03/20/imagenet-vggnet-resnet-inception-xception-keras/