Datasets:
food101

Task Categories: other
Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: unknown
Language Creators: crowdsourced
Annotations Creators: crowdsourced
File size: 5,370 Bytes
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# coding=utf-8
# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Dataset class for Food-101 dataset."""

import json
from pathlib import Path

import datasets
from datasets.tasks import ImageClassification


_BASE_URL = "http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz"

_HOMEPAGE = "https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/"

_DESCRIPTION = (
    "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."
)

_CITATION = """\
 @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}
}
"""

_NAMES = [
    "apple_pie",
    "baby_back_ribs",
    "baklava",
    "beef_carpaccio",
    "beef_tartare",
    "beet_salad",
    "beignets",
    "bibimbap",
    "bread_pudding",
    "breakfast_burrito",
    "bruschetta",
    "caesar_salad",
    "cannoli",
    "caprese_salad",
    "carrot_cake",
    "ceviche",
    "cheesecake",
    "cheese_plate",
    "chicken_curry",
    "chicken_quesadilla",
    "chicken_wings",
    "chocolate_cake",
    "chocolate_mousse",
    "churros",
    "clam_chowder",
    "club_sandwich",
    "crab_cakes",
    "creme_brulee",
    "croque_madame",
    "cup_cakes",
    "deviled_eggs",
    "donuts",
    "dumplings",
    "edamame",
    "eggs_benedict",
    "escargots",
    "falafel",
    "filet_mignon",
    "fish_and_chips",
    "foie_gras",
    "french_fries",
    "french_onion_soup",
    "french_toast",
    "fried_calamari",
    "fried_rice",
    "frozen_yogurt",
    "garlic_bread",
    "gnocchi",
    "greek_salad",
    "grilled_cheese_sandwich",
    "grilled_salmon",
    "guacamole",
    "gyoza",
    "hamburger",
    "hot_and_sour_soup",
    "hot_dog",
    "huevos_rancheros",
    "hummus",
    "ice_cream",
    "lasagna",
    "lobster_bisque",
    "lobster_roll_sandwich",
    "macaroni_and_cheese",
    "macarons",
    "miso_soup",
    "mussels",
    "nachos",
    "omelette",
    "onion_rings",
    "oysters",
    "pad_thai",
    "paella",
    "pancakes",
    "panna_cotta",
    "peking_duck",
    "pho",
    "pizza",
    "pork_chop",
    "poutine",
    "prime_rib",
    "pulled_pork_sandwich",
    "ramen",
    "ravioli",
    "red_velvet_cake",
    "risotto",
    "samosa",
    "sashimi",
    "scallops",
    "seaweed_salad",
    "shrimp_and_grits",
    "spaghetti_bolognese",
    "spaghetti_carbonara",
    "spring_rolls",
    "steak",
    "strawberry_shortcake",
    "sushi",
    "tacos",
    "takoyaki",
    "tiramisu",
    "tuna_tartare",
    "waffles",
]


class Food101(datasets.GeneratorBasedBuilder):
    """Food-101 Images dataset."""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=_NAMES),
                }
            ),
            supervised_keys=("image", "label"),
            homepage=_HOMEPAGE,
            task_templates=[ImageClassification(image_file_path_column="image", label_column="label", labels=_NAMES)],
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        dl_path = Path(dl_manager.download_and_extract(_BASE_URL))
        meta_path = dl_path / "food-101" / "meta"
        image_dir_path = dl_path / "food-101" / "images"
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"json_file_path": meta_path / "train.json", "image_dir_path": image_dir_path},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"json_file_path": meta_path / "test.json", "image_dir_path": image_dir_path},
            ),
        ]

    def _generate_examples(self, json_file_path, image_dir_path):
        """Generate images and labels for splits."""
        labels = self.info.features["label"]
        data = json.loads(json_file_path.read_text())
        for label, images in data.items():
            for image_name in images:
                image = image_dir_path / f"{image_name}.jpg"
                features = {"image": str(image), "label": labels.encode_example(label)}
                yield image_name, features