Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
100K - 1M
License:
| annotations_creators: | |
| - crowdsourced | |
| language_creators: | |
| - crowdsourced | |
| language: | |
| - en | |
| license: | |
| - unknown | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - extended|other-foodspotting | |
| task_categories: | |
| - image-classification | |
| task_ids: | |
| - multi-class-image-classification | |
| paperswithcode_id: food-101 | |
| pretty_name: 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: 3842657187.0 | |
| num_examples: 75750 | |
| - name: validation | |
| num_bytes: 1275182340.5 | |
| num_examples: 25250 | |
| download_size: 5059972308 | |
| dataset_size: 5117839527.5 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| - split: validation | |
| path: data/validation-* | |
| ## 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 | |
| ### 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| |