FIGRIM Dataset

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Visualization of figrim dataset in the Deep Lake UI

FIGRIM dataset

What is FIGRIM Dataset?

FIne-GRained Image Memorability (FIGRIM) is a dataset of 9428 images, 1754 of which are target images for which we obtained memorability scores. The images contain 21 scene categories from the SUN database. Each scene category contains at least 300 images of size 700×700 or greater. All images were cropped to 700×700 pixels.

Download FIGRIM Dataset in Python

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

Load FIGRIM Dataset Training Subset in Python

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

FIGRIM Dataset Structure

FIGRIM Data Fields

image: tensor containing the image.

labels: tensor to represent a category of an image.

FIGRIM Data Splits

The FIGRIM dataset training set is composed of 9428.

How to use FIGRIM Dataset with PyTorch and TensorFlow in Python

Train a model on FIGRIM dataset with PyTorch in Python

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

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)

Train a model on the FIGRIM dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about FIGRIM Dataset

FIGRIM Dataset Description

Homepage: http://figrim.mit.edu/

Paper: Introduced by Bylinskii, Zoya and Isola, Phillip and Bainbridge, Constance and Torralba, Antonio and Oliva, Aude in Intrinsic and Extrinsic Effects on Image Memorabilit

Point of Contact: [email protected]

FIGRIM Dataset Curators

Bylinskii, Zoya and Isola, Phillip and Bainbridge, Constance and Torralba, Antonio and Oliva, Aude

FIGRIM 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!

FIGRIM Dataset Citation Information

				
					@article{figrim,
  title={Intrinsic and Extrinsic Effects on Image Memorability},
  author={Bylinskii, Zoya and Isola, Phillip and Bainbridge, Constance and Torralba, Antonio and Oliva, Aude},
  journal={Vision research},
  volume={116},
  pages={165--178},
  year={2015},
  publisher={Elsevier}
}

FIGRIM Dataset FAQs

What is the FIGRIM dataset for Python?

FIGRIM is a dataset of 9428 images, 1754 of which are target images for which we obtained memorability scores. Each scene category in the dataset contains at least 300 images of size 700×700 or greater. The images also contain 21 scene categories from the SUN database. All images were cropped to 700×700 pixels.

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

You can stream the FIGRIM 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 FIGRIM dataset with PyTorch in Python or train a model on the FIGRIM dataset with TensorFlow in Python.