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# coding=utf-8
# Copyright 2022 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.
"""ImageNet-Sketch data set for evaluating model's ability in learning (out-of-domain) semantics at ImageNet scale"""
import os
import datasets
from datasets.tasks import ImageClassification
from .classes import IMAGENET2012_CLASSES
_HOMEPAGE = "https://huggingface.co/datasets/AIPI540/test2/tree/main"
_CITATION = """\
@inproceedings{wang2019learning,
title={Learning Robust Global Representations by Penalizing Local Predictive Power},
author={Wang, Haohan and Ge, Songwei and Lipton, Zachary and Xing, Eric P},
booktitle={Advances in Neural Information Processing Systems},
pages={10506--10518},
year={2019}
}
"""
_DESCRIPTION = """\
Artwork Images, to predict the year of the artwork created.
"""
_URL = "https://huggingface.co/datasets/AIPI540/Art2/resolve/main/final_art_data.parquet"
class Artwork(datasets.GeneratorBasedBuilder):
"""Artwork Images - a dataset of centuries of Images classes"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"file_name": str,
"label": datasets.features.ClassLabel(names=[0,1,2,3,4,5,6]),
"image_data": bytes
}
),
supervised_keys=("filename", "label","image_data"),
homepage=_HOMEPAGE,
citation=_CITATION,
task_templates=[ImageClassification(image_column="image_data", label_column="label")],
)
def _split_generators(self, dl_manager):
data_files = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": dl_manager.iter_files([data_files]),
},
),
]
def _generate_examples(self, files):
for i, path in enumerate(files):
yield i, {
"filename": path['file_name'],
"label": path['label'],
"image_data": path['image_data'],
} |