This dataset contains millions of color images for scenes and objects which is far bigger than ImageNet dataset. The labels for this dataset are available based on human’s effort for labeling in conjunction with several different image classification models. The images are from parent databases Pascal Voc 2012 and 10 Million Images for 10 Scene Categories.
Here is some information regarding the LSUN dataset:
Number of images in the dataset: More than 59 million and still growing
Number of classes: 10 scene categories and 20 object categories
Scene categories (bedroom, bridge, church_outdoor, classroom, conference_room, dining_room, kitchen, living_room, restaurant, tower)
20 object categories (airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining_table, dog, horse, motorbike, person, potted_plant, sheep, sofa, train, tv-monitor)
The dataset can be downloaded either from GitHub https://github.com/fyu/lsun or the categories lists on http://tigress-web.princeton.edu/~fy/lsun/public/release/. More details are available on the dataset page http://www.yf.io/p/lsun.
If you use this dataset, make sure to cite the paper:
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser and Jianxiong Xiao. Corr, LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop. abs/1506.03365, 2015
keywords: Vision, Image, Classification, Scene, Object Detection