Datasets:

ethz
/

food101

	---
	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.