This dataset contains thousands of color images for scenes recognition provided by Princeton University. The images include environmental scenes, places and objects. To create the dataset, WordNet English dictionary is used to find any nouns completing the sentence “I am in -a place-“ or “Let’s go to -the place-“ and data samples are manually categorized. The number of images per category are different for this dataset with the minimum of 100 images per category for the LSUN397 version.
Different versions are available for the dataset. Here is some information about LSUN397 dataset:
Number of images in the dataset: 16,873
Number of classes: 397 (Abbey, Access_road, etc.)
Here is some information regarding the latest version of this dataset:
Number of images in the dataset: 131,067
Number of classes: 908 scene categories and 3819 object categories
More details and links of download are available on the dataset pages https://vision.princeton.edu/projects/2010/SUN/ and https://groups.csail.mit.edu/vision/SUN/. Recommendations for training and testing split are also available in the mentioned pages.
If you use this dataset, make sure to cite these two papers:
J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba. Sun Database: Large-scale Scene Recognition from Abbey to Zoo, IEEE Conference on Computer Vision and Pattern Recognition, 2010.
J. Xiao, K. A. Ehinger, J. Hays, A. Torralba, and A. Oliva, Sun Database: Exploring a Large Collection of Scene Categories. International Journal of Computer Vision (IJCV), 2014.
keywords: Vision, Image, Classification, Scene, Object Detection