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AQUA Dataset

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Visualization of the AQUA  dataset in the Deep Lake UI

AQUA dataset

What is AQUA Dataset?

In the AQUA (Algebra Question Answering with Rationales) Dataset, question-and-answer (QA) pairs are constructed automatically utilizing cutting-edge question-generating methods based on paintings and comments from an existing art knowledge dataset. Crowdsourcing workers clean the QA pairs in terms of grammatical accuracy, answerability, and correctness of answers. Visual (painting-based) and knowledge (comment-based) questions are intrinsically included in the dataset.

Download AQUA Dataset in Python

Instead of downloading the AQUA dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load AQUA Dataset Training Subset in Python

import deeplake
ds = deeplake.load("hub://activeloop/aqua-train")

Load AQUA Dataset Valiadation Subset in Python

import deeplake
ds = deeplake.load("hub://activeloop/aqua-val")

Load AQUA Dataset Testing Subset in Python

import deeplake
ds = deeplake.load("hub://activeloop/aqua-test")

AQUA Dataset Structure

AQUA Data Fields
  • image: tensor containing the face image.
  • need_external_knowledge: a binary tensor of True/False
  • questions: a text tensor containing questions related to the image.
  • answers: a text tensor containing answers related to the question asked.
AQUA Data Splits

How to use AQUA Dataset with PyTorch and TensorFlow in Python

Train a model on AQUA dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)
Train a model on AQUA dataset with TensorFlow in Python
dataloader = ds.tensorflow()

Additional Information about AQUA Dataset

AQUA Dataset Description

  • Homepage: N/A
  • Repository: https://github.com/noagarcia/ArtVQA
  • Paper: Noa Garcia and Chentao Ye and Zihua Liu and Qingtao Hu and Mayu Otani and Chenhui Chu and Yuta Nakashima and Teruko Mitamura in “A Dataset and Baselines for Visual Question Answering on Art”
  • Point of Contact: N/A
AQUA Dataset Curators

Noa Garcia and Chentao Ye and Zihua Liu and Qingtao Hu and Mayu Otani and Chenhui Chu and Yuta Nakashima and Teruko Mitamura

AQUA Dataset Licensing Information

Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.

If you’re a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

AQUA Dataset Citation Information
@InProceedings{garcia2020AQUA,
   author    = {Noa Garcia and Chentao Ye and Zihua Liu and Qingtao Hu and
                Mayu Otani and Chenhui Chu and Yuta Nakashima and Teruko Mitamura},
   title     = {A Dataset and Baselines for Visual Question Answering on Art},
   booktitle = {Proceedings of the European Conference in Computer Vision Workshops},
   year      = {2020},
}

AQUA Dataset FAQs

What is the AQUA dataset for Python?

In AQUA Dataset, question-and-answer (QA) pairs are constructed automatically utilizing cutting-edge question-generating methods based on paintings and comments from an existing art knowledge dataset. Crowdsourcing workers clean the QA pairs in terms of grammatical accuracy, answerability, and correctness of answers. Visual (painting-based) and knowledge (comment-based) questions are intrinsically included in the dataset.

How to download the AQUA dataset in Python?

You can load the AQUA dataset fast with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to load the AQUA dataset training subset or AQUA data testing subset in Python.

How can I use the AQUA dataset in PyTorch or TensorFlow?

You can stream the AQUA dataset while training a model in PyTorch or TensorFlow with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to train a model on the AQUA dataset with PyTorch in Python or train a model on the AQUA dataset with TensorFlow in Python.

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