Dataset Card for Food-101

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.

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.

{
  "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

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 for adding this dataset.