Since players are encouraged to produce as many terms per image, the dataset's increased coverage is at the expense of accuracy in the word-to-image mapping: a dog in a field with a house in the background might be a golden retriever in ImageNet and could have tags dog, golden retriever, grass, field, house, door in the ESP Dataset.
images in the ESP dataset do not make a distinction between objects in the foreground and in the background, or between the relative size of the objects (tags for images are provided in a random order, so the top tag is not necessarily the best one).