metadata

license: mit
task_categories:
  - image-classification
pretty_name: Food-100 Data Set
size_categories:
  - 100K<n<1M
tags:
  - image classification
  - food-101
  - food-101-enriched
  - embeddings
  - enhanced
language:
  - en

Dataset Card for Food-101-Enriched (Enhanced by Renumics)

Dataset Description

Homepage: Renumics Homepage
GitHub Spotlight
Dataset Homepage data.vision.ee.ethz.ch
Paper: Food-101 – Mining Discriminative Components with Random Forests

Dataset Summary

This data set contains 101'000 images from 101 food categories. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.

Languages

English class labels.

Dataset Structure

Data Instances

Sample data instance:

{'image': '/huggingface/datasets/downloads/extracted/49750366cbaf225ce1b5a5c033fa85ceddeee2e82f1d6e0365e8287859b4c7c8/0/0.jpg',
 'label': 6,
 'label_str': 'beignets',
 'split': 'train'
}

Data Fields

Feature	Data Type
image	Image(decode=True, id=None)
split	Value(dtype='string', id=None)
label	ClassLabel(names=[...], id=None)
label_str	Value(dtype='string', id=None)

Data Splits

Dataset Split	Number of Images in Split
Train	75750
Test	25250

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

The Food-101 data set consists of images from Foodspotting [1] which are not property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond scientific fair use must be negociated with the respective picture owners according to the Foodspotting terms of use [2].
[1] http://www.foodspotting.com/ [2] http://www.foodspotting.com/terms/

Citation Information

If you use this dataset, please cite the following paper:

@inproceedings{bossard14,
  title = {Food-101 -- Mining Discriminative Components with Random Forests},
  author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
  booktitle = {European Conference on Computer Vision},
  year = {2014}
}

Contributions

Lukas Bossard, Matthieu Guillaumin, Luc Van Gool, and Renumics GmbH.