Datasets:

arielnlee
/

Superimposed-Masked-Dataset

	# coding=utf-8
	# Copyright 2022 the HuggingFace Datasets Authors.
	#
	# 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.

	# This script was modified from the imagenet-1k HF dataset repo

	import os

	import datasets
	from datasets.tasks import ImageClassification

	from .classes import IMAGENET2012_CLASSES


	_CITATION = """\
	@misc{lee2023hardwiring,
	title={Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing},
	author={Ariel N. Lee and Sarah Adel Bargal and Janavi Kasera and Stan Sclaroff and Kate Saenko and Nataniel Ruiz},
	year={2023},
	eprint={2306.17848},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
	}
	"""

	_HOMEPAGE = "https://arielnlee.github.io/PatchMixing/"

	_DESCRIPTION = """\
	SMD is an occluded ImageNet-1K validation set, created to be an additional way to evaluate the impact of occlusion on model performance. This experiment used a variety of occluder objects that are not in the ImageNet-1K label space and are unambiguous in relationship to objects that reside in the label space.
	"""

	_DATA_URL = {
	"smd": [
	f"https://huggingface.co/datasets/ariellee/Superimposed-Masked-Dataset/resolve/main/smd_{i}.tar.gz"
	for i in range(1, 41)
	]
	}


	class SMD(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	DEFAULT_WRITER_BATCH_SIZE = 1000

	def _info(self):
	assert len(IMAGENET2012_CLASSES) == 1000
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"image": datasets.Image(),
	"label": datasets.ClassLabel(names=list(IMAGENET2012_CLASSES.values())),
	}
	),
	homepage=_HOMEPAGE,
	citation=_CITATION,
	task_templates=[ImageClassification(image_column="image", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	archives = dl_manager.download(_DATA_URL)

	return [
	datasets.SplitGenerator(
	name="SMD", # "SMD (occluded IN-1K val set)"
	gen_kwargs={
	"archives": [dl_manager.iter_archive(archive) for archive in archives["smd"]],
	},
	),
	]


	def _generate_examples(self, archives):
	"""Yields examples."""
	idx = 0
	for archive in archives:
	for path, file in archive:
	if path.endswith(".png"):
	synset_id = os.path.basename(os.path.dirname(path))
	label = IMAGENET2012_CLASSES[synset_id]
	ex = {"image": {"path": path, "bytes": file.read()}, "label": label}
	yield idx, ex
	idx += 1