task_categories:image-classification task_ids:multi-class-image-classification size_categories:100K<n<1M source_datasets:extended|other-foodspotting source_datasets:extended|food101 language:en license:unknown image classification food-101 food-101-enriched embeddings enhanced spotlight region:us
 @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}
}
---
license: unknown
paperswithcode_id: food-101
pretty_name: Food-101 Data Set
size_categories:
- 100K<n<1M
tags:
- image classification
- food-101
- food-101-enriched
- embeddings
- enhanced
- spotlight
language:
- en
source_datasets:
- extended|other-foodspotting
- extended|food101
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
# Dataset Card for Food-101-Enriched (Enhanced by Renumics)

## Dataset Description

- **Homepage:** [Renumics Homepage](https://renumics.com/?hf-dataset-card=food101-enriched)
- **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

📊 [Data-centric AI](https://datacentricai.org) principles have become increasingly important for real-world use cases.  
At [Renumics](https://renumics.com/?hf-dataset-card=food101-enriched) we believe that classical benchmark datasets and competitions should be extended to reflect this development. 

🔍 This is why we are publishing benchmark datasets with application-specific enrichments (e.g. embeddings, baseline results, uncertainties, label error scores). We hope this helps the ML community in the following ways:
1. Enable new researchers to quickly develop a profound understanding of the dataset.
2. Popularize data-centric AI principles and tooling in the ML community.
3. Encourage the sharing of meaningful qualitative insights in addition to traditional quantitative metrics.

📚 This dataset is an enriched version of the [Food101 Data Set](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/).

### Explore the Dataset

![Analyze Food101 with Spotlight](https://spotlight.renumics.com/resources/hf-food101-enriched.png)

The enrichments allow you to quickly gain insights into the dataset. The open source data curation tool [Renumics Spotlight](https://github.com/Renumics/spotlight) enables that with just a few lines of code:

Install datasets and Spotlight via [pip](https://packaging.python.org/en/latest/key_projects/#pip):

```python
!pip install renumics-spotlight datasets
```

Load the dataset from huggingface in your notebook:

```python
import datasets

dataset = datasets.load_dataset("renumics/food101-enriched", split="train")
```

Start exploring with a simple view:

```python
from renumics import spotlight

df_show = dataset.to_pandas()
spotlight.show(df_show, port=8000, dtype={"image": spotlight.Image})
```
You can use the UI to interactively configure the view on the data. Depending on the concrete tasks (e.g. model comparison, debugging, outlier detection) you might want to leverage different enrichments and metadata.


### Food101 Dataset

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.

### Supported Tasks and Leaderboards

- `image-classification`: The goal of this task is to classify a given image of a dish into one of 101 classes. The leaderboard is available [here](https://paperswithcode.com/sota/fine-grained-image-classification-on-food-101).

### Languages

English class labels.

## Dataset Structure

### Data Instances

A sample from the training set is provided below:

```python
{
  "image": "/huggingface/datasets/downloads/extracted/49750366cbaf225ce1b5a5c033fa85ceddeee2e82f1d6e0365e8287859b4c7c8/0/0.jpg",
  "label": 6,
  "label_str": "beignets",
  "split": "train"
}
```
<details>
  <summary>Class Label Mappings</summary>

  ```json
  {
    "apple_pie": 0,
    "baby_back_ribs": 1,
    "baklava": 2,
    "beef_carpaccio": 3,
    "beef_tartare": 4,
    "beet_salad": 5,
    "beignets": 6,
    "bibimbap": 7,
    "bread_pudding": 8,
    "breakfast_burrito": 9,
    "bruschetta": 10,
    "caesar_salad": 11,
    "cannoli": 12,
    "caprese_salad": 13,
    "carrot_cake": 14,
    "ceviche": 15,
    "cheesecake": 16,
    "cheese_plate": 17,
    "chicken_curry": 18,
    "chicken_quesadilla": 19,
    "chicken_wings": 20,
    "chocolate_cake": 21,
    "chocolate_mousse": 22,
    "churros": 23,
    "clam_chowder": 24,
    "club_sandwich": 25,
    "crab_cakes": 26,
    "creme_brulee": 27,
    "croque_madame": 28,
    "cup_cakes": 29,
    "deviled_eggs": 30,
    "donuts": 31,
    "dumplings": 32,
    "edamame": 33,
    "eggs_benedict": 34,
    "escargots": 35,
    "falafel": 36,
    "filet_mignon": 37,
    "fish_and_chips": 38,
    "foie_gras": 39,
    "french_fries": 40,
    "french_onion_soup": 41,
    "french_toast": 42,
    "fried_calamari": 43,
    "fried_rice": 44,
    "frozen_yogurt": 45,
    "garlic_bread": 46,
    "gnocchi": 47,
    "greek_salad": 48,
    "grilled_cheese_sandwich": 49,
    "grilled_salmon": 50,
    "guacamole": 51,
    "gyoza": 52,
    "hamburger": 53,
    "hot_and_sour_soup": 54,
    "hot_dog": 55,
    "huevos_rancheros": 56,
    "hummus": 57,
    "ice_cream": 58,
    "lasagna": 59,
    "lobster_bisque": 60,
    "lobster_roll_sandwich": 61,
    "macaroni_and_cheese": 62,
    "macarons": 63,
    "miso_soup": 64,
    "mussels": 65,
    "nachos": 66,
    "omelette": 67,
    "onion_rings": 68,
    "oysters": 69,
    "pad_thai": 70,
    "paella": 71,
    "pancakes": 72,
    "panna_cotta": 73,
    "peking_duck": 74,
    "pho": 75,
    "pizza": 76,
    "pork_chop": 77,
    "poutine": 78,
    "prime_rib": 79,
    "pulled_pork_sandwich": 80,
    "ramen": 81,
    "ravioli": 82,
    "red_velvet_cake": 83,
    "risotto": 84,
    "samosa": 85,
    "sashimi": 86,
    "scallops": 87,
    "seaweed_salad": 88,
    "shrimp_and_grits": 89,
    "spaghetti_bolognese": 90,
    "spaghetti_carbonara": 91,
    "spring_rolls": 92,
    "steak": 93,
    "strawberry_shortcake": 94,
    "sushi": 95,
    "tacos": 96,
    "takoyaki": 97,
    "tiramisu": 98,
    "tuna_tartare": 99,
    "waffles": 100
  }
  ```
</details>

### 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.