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
- Installation
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://www.kietzmannlab.org/ecoset
- Repository: https://codeocean.com/capsule/9570390/tree/v1
- Paper: https://doi.org/10.1073/pnas.2011417118
- Point of Contact: support@codeocean.com
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