Flickr30k Dataset

Name: Flickr30k
Creator: Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaie.
License: https://creativecommons.org/licenses/by/2.0/

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Visualization of the Flickr30k dataset in the Deep Lake UI

Flickr30k dataset

What is Flickr30k dataset?

The Flickr30k dataset is a popular benchmark for sentence-based picture portrayal. The dataset is comprised of 31,783 images that capture people engaged in everyday activities and events. Each image has a descriptive caption. Flickr30k is used for understanding the visual media (image) that correspond to a linguistic expression (description of the image). This dataset is commonly used as a standard benchmark for sentence-based image descriptions.

Download Flickr30k dataset in Python

Instead of downloading the Flickr30k dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load Flickr30k Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/flickr30k')

Flickr30k Dataset Structure

Flickr30k Data Fields

images: tensor containing the image.
texts: tensor to represent text associated with the image.
comment_nos: tensor to represent the number of comments.

Flickr30k Data Splits

The Flickr30k dataset training set is composed of 31,783 images.

How to use Flickr30k Dataset with PyTorch and TensorFlow in Python

Train a model on the Flickr30k 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 the Flickr30k dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional information about Flickr30k Dataset

Flickr30k Dataset Description

Homepage: https://shannon.cs.illinois.edu/DenotationGraph/
Paper: Introduced by Peter Young and Alice Lai and Micah Hodosh and Julia Hockenmaie in From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Point of Contact: N/A

Flickr30k Dataset Curators

Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaie.

Flickr30k 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!

Flickr30k Dataset Citation Information

				
					@article{flickr30k,
    title={From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions},
    author={Peter Young and Alice Lai and Micah Hodosh and Julia Hockenmaier},
    journal={TACL},
    volume={2},
    pages={67--78},
    year={2014}
}

Flickr30k Datasets FAQs

What is the Flickr30k dataset for Python?

The Flickr30k dataset is a popular benchmark for sentence-based picture portrayal. The dataset has over 31,000 images. Each image in the dataset has five reference sentences provided by human annotators. The annotations on each image allow for progress in automatic image description and grounded language understanding.

How to download the Flickr30k dataset in Python?

You can load the Flickr30k 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 Flickr30k dataset training subset in Python.

How can I use the Flickr30k dataset in Pytorch or TensorFlow?

Use the open-source package Activeloop Deep Lake in Python to stream Flickr30k while training a model in PyTorch or TensorFlow. See detailed instructions on how to train a model on the Flickr30k dataset with PyTorch in Python or train a model on the Flickr30k dataset with TensorFlow in Python.