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""" |
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Loading script for the Food Vision 199 classes dataset. |
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See the template: https://github.com/huggingface/datasets/blob/main/templates/new_dataset_script.py |
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See the example for Food101: https://huggingface.co/datasets/food101/blob/main/food101.py |
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See another example: https://huggingface.co/datasets/davanstrien/encyclopedia_britannica/blob/main/encyclopedia_britannica.py |
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""" |
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import datasets |
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import os |
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import requests |
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import pandas as pd |
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from datasets.tasks import ImageClassification |
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print(f"Datasets version: {datasets.__version__}") |
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datasets.logging.set_verbosity(10) |
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print(f"Verbosity level: {datasets.logging.get_verbosity()}") |
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_HOMEPAGE = "https://www.nutrify.app" |
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_LICENSE = "TODO" |
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_CITATION = "TODO" |
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_DESCRIPTION = "Images of 199 food classes from the Nutrify app." |
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_NAMES = ['almond_butter', |
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'almonds', |
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'apple', |
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'apricot', |
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'asparagus', |
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'avocado', |
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'bacon', |
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'bacon_and_egg_burger', |
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'bagel', |
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'baklava', |
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'banana', |
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'banana_bread', |
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'barbecue_sauce', |
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'beans', |
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'beef', |
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'beef_curry', |
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'beef_mince', |
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'beef_stir_fry', |
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'beer', |
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'beetroot', |
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'biltong', |
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'blackberries', |
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'blueberries', |
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'bok_choy', |
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'bread', |
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'broccoli', |
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'broccolini', |
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'brownie', |
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'brussel_sprouts', |
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'burrito', |
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'butter', |
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'cabbage', |
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'calamari', |
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'candy', |
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'capsicum', |
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'carrot', |
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'cashews', |
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'cauliflower', |
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'celery', |
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'cheese', |
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'cheeseburger', |
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'cherries', |
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'chicken_breast', |
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'chicken_thighs', |
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'chicken_wings', |
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'chilli', |
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'chimichurri', |
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'chocolate', |
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'chocolate_cake', |
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'coconut', |
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'coffee', |
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'coleslaw', |
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'cookies', |
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'coriander', |
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'corn', |
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'corn_chips', |
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'cream', |
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'croissant', |
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'crumbed_chicken', |
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'cucumber', |
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'cupcake', |
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'daikon_radish', |
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'dates', |
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'donuts', |
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'dragonfruit', |
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'eggplant', |
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'eggs', |
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'enoki_mushroom', |
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'fennel', |
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'figs', |
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'french_toast', |
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'fried_rice', |
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'fries', |
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'fruit_juice', |
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'garlic', |
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'garlic_bread', |
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'ginger', |
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'goji_berries', |
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'granola', |
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'grapefruit', |
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'grapes', |
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'green_beans', |
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'green_onion', |
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'guacamole', |
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'guava', |
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'gyoza', |
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'ham', |
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'honey', |
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'hot_chocolate', |
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'ice_coffee', |
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'ice_cream', |
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'iceberg_lettuce', |
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'jerusalem_artichoke', |
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'kale', |
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'karaage_chicken', |
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'kimchi', |
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'kiwi_fruit', |
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'lamb_chops', |
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'leek', |
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'lemon', |
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'lentils', |
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'lettuce', |
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'lime', |
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'mandarin', |
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'mango', |
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'maple_syrup', |
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'mashed_potato', |
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'mayonnaise', |
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'milk', |
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'miso_soup', |
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'mushrooms', |
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'nectarines', |
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'noodles', |
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'nuts', |
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'olive_oil', |
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'olives', |
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'omelette', |
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'onion', |
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'orange', |
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'orange_juice', |
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'oysters', |
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'pain_au_chocolat', |
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'pancakes', |
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'papaya', |
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'parsley', |
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'parsnips', |
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'passionfruit', |
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'pasta', |
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'pawpaw', |
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'peach', |
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'pear', |
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'peas', |
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'pickles', |
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'pineapple', |
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'pizza', |
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'plum', |
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'pomegranate', |
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'popcorn', |
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'pork_belly', |
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'pork_chop', |
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'pork_loins', |
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'porridge', |
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'potato_bake', |
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'potato_chips', |
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'potato_scallop', |
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'potatoes', |
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'prawns', |
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'pumpkin', |
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'radish', |
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'ramen', |
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'raspberries', |
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'red_onion', |
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'red_wine', |
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'rhubarb', |
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'rice', |
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'roast_beef', |
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'roast_pork', |
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'roast_potatoes', |
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'rockmelon', |
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'rosemary', |
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'salad', |
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'salami', |
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'salmon', |
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'salsa', |
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'salt', |
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'sandwich', |
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'sardines', |
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'sausage_roll', |
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'sausages', |
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'scrambled_eggs', |
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'seaweed', |
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'shallots', |
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'snow_peas', |
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'soda', |
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'soy_sauce', |
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'spaghetti_bolognese', |
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'spinach', |
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'sports_drink', |
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'squash', |
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'starfruit', |
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'steak', |
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'strawberries', |
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'sushi', |
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'sweet_potato', |
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'tacos', |
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'tamarillo', |
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'taro', |
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'tea', |
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'toast', |
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'tofu', |
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'tomato', |
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'tomato_chutney', |
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'tomato_sauce', |
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'turnip', |
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'watermelon', |
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'white_onion', |
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'white_wine', |
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'yoghurt', |
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'zucchini'] |
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class Food199(datasets.GeneratorBasedBuilder): |
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"""Food199 Images dataset""" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"label": datasets.ClassLabel(names=_NAMES) |
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} |
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), |
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supervised_keys=("image", "label"), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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license=_LICENSE |
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) |
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def _split_generators(self, dl_manager): |
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""" |
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This function returns the logic to split the dataset into different splits as well as labels. |
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""" |
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annotations_csv = dl_manager.download("https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/raw/main/annotations_with_links.csv") |
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print(annotations_csv) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"annotations": annotations_csv, |
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"split": "train" |
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} |
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), |
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] |
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def _generate_examples(self, annotations, split): |
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""" |
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This function takes in the kwargs from the _split_generators method and can then yield information from them. |
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""" |
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annotations_df = pd.read_csv(annotations, low_memory=False) |
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if split == "train": |
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annotations = annotations_df[["image", "label"]][annotations_df["split"] == "train"].to_dict(orient="records") |
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elif split == "test": |
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annotations = annotations_df[["image", "label"]][annotations_df["split"] == "test"].to_dict(orient="records") |
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for id_, row in enumerate(annotations): |
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if id_ == 100: |
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break |
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row["image"] = str(row.pop("image")) |
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row["label"] = row.pop("label") |
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yield id_, row |
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