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metadata
pretty_name: Ecoset
license: cc
source_datasets:
  - original
task_categories:
  - image-classification
  - image
task_ids:
  - multi-class-image-classification
  - other-other-image-classification
  - image-classification
  - other-image-classification
paperswithcode_id: ecoset

Table of Contents

Dataset Description

Dataset Summary

Tired of all the dogs in ImageNet (ILSVRC)? Then ecoset is here for you. 1.5m images from 565 basic level categories, chosen to be both (i) frequent in linguistic usage, and (ii) rated by human observers as concrete (e.g. ‘table’ is concrete, ‘romance’ is not).

Supported Tasks and Leaderboards

Ecoset is a large multi-class single-label object recognition image dataset (similar to ImageNet).

Install Requirements

In order to work with ecoset, please make sure to install the s3 compatible version of huggingface datasets, which should include the s3fs, botocore and boto3 modules:

pip install datasets[s3]

Download Settings

Please set ignore_verifications=True. when downloading this dataset, else the download will result in an error:

from datasets import load_dataset

dataset = load_dataset("DiGyt/ecoset", ignore_verifications=True)

Dataset Structure

We show detailed information for all the configurations of the dataset. Currently, there is only one setting (Full) available, containing all data.

Data Instances

Full

  • Size of downloaded dataset files: 155 GB
  • Total amount of disk used: 311 GB

Dataset Creation

Personal and Sensitive Information

... # TODO

Considerations for Using the Data

Social Impact of Dataset

... # TODO

Discussion of Biases

... # TODO

Other Known Limitations

... # TODO

Additional Information

Dataset Curators

The corpus was put together by # TODO.

Licensing Information

Ecoset is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 2.0 license (cc-by-nc-sa-2.0).

Citation Information

@article{mehrer2021ecologically,
  title={An ecologically motivated image dataset for deep learning yields better models of human vision},
  author={Mehrer, Johannes and Spoerer, Courtney J and Jones, Emer C and Kriegeskorte, Nikolaus and Kietzmann, Tim C},
  journal={Proceedings of the National Academy of Sciences},
  volume={118},
  number={8},
  pages={e2011417118},
  year={2021},
  publisher={National Acad Sciences}
}

Contributions

Thanks to #TODO