File size: 3,764 Bytes
fe1345c 0b21e1c fe1345c 0b21e1c fe1345c b3ea911 0070714 e3cb957 0b21e1c e8fdce3 0b21e1c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
---
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
---
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]```
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.kietzmannlab.org/ecoset](https://www.kietzmannlab.org/ecoset/)
- **Repository:** [https://codeocean.com/capsule/9570390/tree/v1](https://codeocean.com/capsule/9570390/tree/v1)
- **Paper:** [https://doi.org/10.1073/pnas.2011417118](https://doi.org/10.1073/pnas.2011417118)
- **Point of Contact:** [support@codeocean.com](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). Here we collect resources associated with ecoset. This includes the dataset,
trained deep neural network models, code to interact with them, and published papers
using it.
### Supported Tasks and Leaderboards
Ecoset is a large multi-class single-label object recognition image dataset (similar to ImageNet).
## 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
<details>
<summary>Click to expand the Data/size information for each language (deduplicated)</summary>
#### 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
|