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{
"builder_name": "scene_parse_150",
"citation": "@inproceedings{zhou2017scene,\n title={Scene Parsing through ADE20K Dataset},\n author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},\n booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},\n year={2017}\n}\n\n@article{zhou2016semantic,\n title={Semantic understanding of scenes through the ade20k dataset},\n author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},\n journal={arXiv preprint arXiv:1608.05442},\n year={2016}\n}\n",
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"description": "Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed.\nMIT Scene Parsing Benchmark (SceneParse150) provides a standard training and evaluation platform for the algorithms of scene parsing.\nThe data for this benchmark comes from ADE20K Dataset which contains more than 20K scene-centric images exhaustively annotated with objects and object parts.\nSpecifically, the benchmark is divided into 20K images for training, 2K images for validation, and another batch of held-out images for testing.\nThere are totally 150 semantic categories included for evaluation, which include stuffs like sky, road, grass, and discrete objects like person, car, bed.\nNote that there are non-uniform distribution of objects occuring in the images, mimicking a more natural object occurrence in daily scene.\n",
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