Delete loading script

food101.py

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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 datasets
-from datasets.tasks import ImageClassification
-
-
-_BASE_URL = "http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz"
-
-_METADATA_URLS = {
-    "train": "https://s3.amazonaws.com/datasets.huggingface.co/food101/meta/train.txt",
-    "test": "https://s3.amazonaws.com/datasets.huggingface.co/food101/meta/test.txt",
-}
-
-_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}
-}
-"""
-
-_LICENSE = """\
-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/
-"""
-
-_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",
-]
-
-_IMAGES_DIR = "food-101/images/"
-
-
-class Food101(datasets.GeneratorBasedBuilder):
-    """Food-101 Images dataset."""
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "label": datasets.ClassLabel(names=_NAMES),
-                }
-            ),
-            supervised_keys=("image", "label"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE,
-            task_templates=[ImageClassification(image_column="image", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download(_BASE_URL)
-        split_metadata_paths = dl_manager.download(_METADATA_URLS)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "images": dl_manager.iter_archive(archive_path),
-                    "metadata_path": split_metadata_paths["train"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "images": dl_manager.iter_archive(archive_path),
-                    "metadata_path": split_metadata_paths["test"],
-                },
-            ),
-        ]
-
-    def _generate_examples(self, images, metadata_path):
-        """Generate images and labels for splits."""
-        with open(metadata_path, encoding="utf-8") as f:
-            files_to_keep = set(f.read().split("\n"))
-        for file_path, file_obj in images:
-            if file_path.startswith(_IMAGES_DIR):
-                if file_path[len(_IMAGES_DIR) : -len(".jpg")] in files_to_keep:
-                    label = file_path.split("/")[2]
-                    yield file_path, {
-                        "image": {"path": file_path, "bytes": file_obj.read()},
-                        "label": label,
-                    }