Datasets:

nateraw
/

food101

	# 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