Update README.md

README.md

@@ -417,4 +417,61 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+task_categories:
+- image-classification
+language:
+- en
+tags:
+- princeton
+pretty_name: SUN397
+size_categories:
+- 100K<n<1M
+paperswithcode_id: sun397
+multilinguality:
+- monolingual
+annotations_creators:
+- expert-generated
 ---
+
+# Scene UNderstanding 397 — SUN397
+
+[![](https://vision.princeton.edu/projects/2010/SUN/sun_mosaic_logo.jpg)](https://vision.princeton.edu/projects/2010/SUN/)
+
+## Dataset Description
+
+- **Homepage:** [SUN Database: Scene Categorization Benchmark](https://vision.princeton.edu/projects/2010/SUN/)
+- **Repository:** [github:CSAILVision/ADE20K](https://github.com/CSAILVision/ADE20K)
+
+## Description
+
+Scene categorization is a fundamental problem in computer vision.
+However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories.
+Whereas standard databases for object categorization contain hundreds of different classes of objects, the largest available dataset of scene categories contains only 15 classes.
+In this paper we propose the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images.
+We use 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance.
+We measure human scene classification performance on the SUN database and compare this with computational methods.
+
+## Citations
+
+```bibtex
+@inproceedings{5539970,
+	title     = {SUN database: Large-scale scene recognition from abbey to zoo},
+	author    = {Xiao, Jianxiong and Hays, James and Ehinger, Krista A. and Oliva, Aude and Torralba, Antonio},
+	year      = 2010,
+	booktitle = {2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
+	volume    = {},
+	number    = {},
+	pages     = {3485--3492},
+	doi       = {10.1109/CVPR.2010.5539970},
+	keywords  = {Sun;Large-scale systems;Layout;Humans;Image databases;Computer vision;Anthropometry;Bridges;Legged locomotion;Spatial databases}
+}
+@article{Xiao2014SUNDE,
+	title     = {SUN Database: Exploring a Large Collection of Scene Categories},
+	author    = {Jianxiong Xiao and Krista A. Ehinger and James Hays and Antonio Torralba and Aude Oliva},
+	year      = 2014,
+	journal   = {International Journal of Computer Vision},
+	volume    = 119,
+	pages     = {3--22},
+	url       = {https://api.semanticscholar.org/CorpusID:10224573}
+}
+```