33 lines
1.8 KiB
Markdown
33 lines
1.8 KiB
Markdown
# ADE20K Dataset
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This is the repository for the [ADE20K](http://groups.csail.mit.edu/vision/datasets/ADE20K/) dataset. We provide some [starter code](notebooks/ade20k_starter.ipynb) to analyze the dataset, basic statistics of the data and links to existing projects using ADE20K.
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## Overview
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write
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## Download dataset
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To download the dataset, register in [this link](http://groups.csail.mit.edu/vision/datasets/ADE20K/request_data/). Once you are approved you will be able to download the data, following the [terms of use](http://groups.csail.mit.edu/vision/datasets/ADE20K/terms).
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## ADE20K related projects
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Here is a list of existing challenges and projects using ADE20K data. Contact us if you would like to include the dataset in a new benchmark.
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* [MIT Scene Parsing Benchmark](https://github.com/CSAILVision/sceneparsing): A semantic segmentation benchmark, using a subset of 250 classes from ADE20K
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* [Robust Vision Challenge](http://www.robustvision.net/): A challenge to evaluate the robustness of models to multiple datasets and tasks, including semantic and instance segmentation, depth prediction, optical flow, etc.
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## Citation
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If you use this data, please cite the following paper:
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Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., & Torralba, A. (2019). Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127(3), 302-321.
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```
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@article{zhou2019semantic,
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title={Semantic understanding of scenes through the ade20k dataset},
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author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Xiao, Tete and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
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journal={International Journal of Computer Vision},
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volume={127},
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number={3},
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pages={302--321},
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year={2019},
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publisher={Springer}
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}
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```
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