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--- |
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pretty_name: 'Visual Attributes in the Wild (VAW)' |
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language: |
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- en |
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--- |
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# Dataset Card for Visual Attributes in the Wild (VAW) |
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## Dataset Description |
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**Homepage:** http://vawdataset.com/ |
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**Repository:** https://github.com/adobe-research/vaw_dataset; |
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- The raw dataset files will be downloaded from: https://github.com/adobe-research/vaw_dataset/tree/main/data, where one can also find additional metadata files such as attribute types. |
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- The train split loaded from this hf dataset is a concatenation of the train_part1.json and train_part2.json. |
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- The image_id field corresponds to respective image ids in the v1.4 Visual Genome dataset. |
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**LICENSE:** https://github.com/adobe-research/vaw_dataset/blob/main/LICENSE.md |
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**Paper Citation:** |
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``` |
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@InProceedings{Pham_2021_CVPR, |
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author = {Pham, Khoi and Kafle, Kushal and Lin, Zhe and Ding, Zhihong and Cohen, Scott and Tran, Quan and Shrivastava, Abhinav}, |
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title = {Learning To Predict Visual Attributes in the Wild}, |
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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month = {June}, |
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year = {2021}, |
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pages = {13018-13028} |
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} |
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``` |
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## Dataset Summary |
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A large scale visual attributes dataset with explicitly labelled positive and negative attributes. |
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- 620 Unique Attributes including color, shape, texture, posture and many others |
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- 260,895 Instances of different objects |
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- 2260 Unique Objects observed in the wild |
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- 72,274 Images from the Visual Genome Dataset |
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- 4 different evaluation metrics for measuring multi-faceted performance metrics |
