---
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](https://renumics.com/)
- **GitHub** [Spotlight](https://github.com/Renumics/spotlight)
- **Dataset Homepage** [data.vision.ee.ethz.ch](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/)
- **Paper:** [Food-101 – Mining Discriminative Components with Random Forests](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/static/bossard_eccv14_food-101.pdf)

### 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/](http://www.foodspotting.com/)
[2] [http://www.foodspotting.com/terms/](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.