README.md

---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: Food-101
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-foodspotting
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: food-101
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          0: apple_pie
          1: baby_back_ribs
          2: baklava
          3: beef_carpaccio
          4: beef_tartare
          5: beet_salad
          6: beignets
          7: bibimbap
          8: bread_pudding
          9: breakfast_burrito
          10: bruschetta
          11: caesar_salad
          12: cannoli
          13: caprese_salad
          14: carrot_cake
          15: ceviche
          16: cheesecake
          17: cheese_plate
          18: chicken_curry
          19: chicken_quesadilla
          20: chicken_wings
          21: chocolate_cake
          22: chocolate_mousse
          23: churros
          24: clam_chowder
          25: club_sandwich
          26: crab_cakes
          27: creme_brulee
          28: croque_madame
          29: cup_cakes
          30: deviled_eggs
          31: donuts
          32: dumplings
          33: edamame
          34: eggs_benedict
          35: escargots
          36: falafel
          37: filet_mignon
          38: fish_and_chips
          39: foie_gras
          40: french_fries
          41: french_onion_soup
          42: french_toast
          43: fried_calamari
          44: fried_rice
          45: frozen_yogurt
          46: garlic_bread
          47: gnocchi
          48: greek_salad
          49: grilled_cheese_sandwich
          50: grilled_salmon
          51: guacamole
          52: gyoza
          53: hamburger
          54: hot_and_sour_soup
          55: hot_dog
          56: huevos_rancheros
          57: hummus
          58: ice_cream
          59: lasagna
          60: lobster_bisque
          61: lobster_roll_sandwich
          62: macaroni_and_cheese
          63: macarons
          64: miso_soup
          65: mussels
          66: nachos
          67: omelette
          68: onion_rings
          69: oysters
          70: pad_thai
          71: paella
          72: pancakes
          73: panna_cotta
          74: peking_duck
          75: pho
          76: pizza
          77: pork_chop
          78: poutine
          79: prime_rib
          80: pulled_pork_sandwich
          81: ramen
          82: ravioli
          83: red_velvet_cake
          84: risotto
          85: samosa
          86: sashimi
          87: scallops
          88: seaweed_salad
          89: shrimp_and_grits
          90: spaghetti_bolognese
          91: spaghetti_carbonara
          92: spring_rolls
          93: steak
          94: strawberry_shortcake
          95: sushi
          96: tacos
          97: takoyaki
          98: tiramisu
          99: tuna_tartare
          100: waffles
  splits:
  - name: train
    num_bytes: 3845865322
    num_examples: 75750
  - name: validation
    num_bytes: 1276249954
    num_examples: 25250
  download_size: 4998236572
  dataset_size: 5122115276
---

# Dataset Card for Food-101

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [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:** [Food-101 Dataset](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/)
- **Repository:**
- **Paper:** [Paper](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/static/bossard_eccv14_food-101.pdf)
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

This dataset consists of 101 food categories, with 101'000 images. 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

## Dataset Structure

### Data Instances

A sample from the training set is provided below:

```
{
  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
  'label': 23
}
```

### Data Fields

The data instances have the following fields:

- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `label`: an `int` classification label.

<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 Splits

 
|   |train|validation|
|----------|----:|---------:|
|# of examples|75750|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

LICENSE AGREEMENT
=================
 - 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

```
 @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

Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.