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Datasets:
ethz
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food101

Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
100K - 1M
Libraries:
Datasets
Dask
License:
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metadata
annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
languages:
  - en
licenses:
  - unknown
multilinguality:
  - monolingual
pretty_name: Food-101
size_categories:
  - 10K<n<100K
source_datasets:
  - extended|other-foodspotting
task_categories:
  - other
task_ids:
  - other-other-image-classification
paperswithcode_id: food-101

Dataset Card for Food-101

Table of Contents

Dataset Description

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

Languages

English

Dataset Structure

Data Instances

A sample from the training set is provided below:

{
  'image': '/root/.cache/huggingface/datasets/downloads/extracted/6e1e8c9052e9f3f7ecbcb4b90860668f81c1d36d86cc9606d49066f8da8bfb4f/food-101/images/churros/1004234.jpg',
  'label': 23
}

Data Fields

The data instances have the following fields:

  • image: a string filepath to an image.
  • label: an int classification label.
Class Label Mappings 
{
  "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
}

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

[More Information Needed]

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 for adding this dataset.