metadata

pretty_name: Visual Attributes in the Wild (VAW)
language:
  - en

Dataset Card for Visual Attributes in the Wild (VAW)

Dataset Description

Homepage: http://vawdataset.com/

Repository: https://github.com/adobe-research/vaw_dataset;

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.
The train split loaded from this hf dataset is a concatenation of the train_part1.json and train_part2.json.
The image_id field corresponds to respective image ids in the v1.4 Visual Genome dataset.

LICENSE: https://github.com/adobe-research/vaw_dataset/blob/main/LICENSE.md

Paper Citation:

@InProceedings{Pham_2021_CVPR,
    author    = {Pham, Khoi and Kafle, Kushal and Lin, Zhe and Ding, Zhihong and Cohen, Scott and Tran, Quan and Shrivastava, Abhinav},
    title     = {Learning To Predict Visual Attributes in the Wild},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {13018-13028}
}

Dataset Summary

A large scale visual attributes dataset with explicitly labelled positive and negative attributes.

620 Unique Attributes including color, shape, texture, posture and many others
260,895 Instances of different objects
2260 Unique Objects observed in the wild
72,274 Images from the Visual Genome Dataset
4 different evaluation metrics for measuring multi-faceted performance metrics