Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K<n<10K
Tags:
License:
File size: 3,673 Bytes
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# -*- coding: utf-8 -*-
"""Untitled9.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1qRAN4BBFZkzQKFec3qaXZ_obxuWQa6c6
"""
# 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.
"""Beans leaf dataset with images of diseased and health leaves."""
import os
import datasets
from datasets.tasks import ImageClassification
_HOMEPAGE = "https://huggingface.co/datasets/poolrf2001/mask"
_CITATION = """\
@ONLINE {masksdata,
author="Pool_rf",
title="Mask face dataset",
month="January",
year="2023",
url="https://huggingface.co/datasets/poolrf2001/mask"
}
"""
_DESCRIPTION = """\
MaskFace es un conjunto de datos de imágenes de personas con y sin mascarillas Consta de 3 clases: 1 clase de si la persona está puesta la mascarilla,
otra clase si la persona no esta puesta la mascarilla y una clase donde la persona está puesta la mascarilla incorrectamente.
"""
_URLS = {
"train": "https://huggingface.co/datasets/poolrf2001/mask/blob/main/train.zip",
"validation": "https://huggingface.co/datasets/poolrf2001/mask/blob/main/validation.zip",
"test": "https://huggingface.co/datasets/poolrf2001/mask/blob/main/test.zip",
}
_NAMES = ["mask_weared_incorrect", "width_mask", "without_mask"]
class mask(datasets.GeneratorBasedBuilder):
"""MaskFace images dataset."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Image(),
"labels": datasets.features.ClassLabel(names=_NAMES),
}
),
supervised_keys=("image", "labels"),
homepage=_HOMEPAGE,
citation=_CITATION,
task_templates=[ImageClassification(image_column="image", label_column="labels")],
)
def _split_generators(self, dl_manager):
data_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": dl_manager.iter_files([data_files["train"]]),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"files": dl_manager.iter_files([data_files["validation"]]),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"files": dl_manager.iter_files([data_files["test"]]),
},
),
]
def _generate_examples(self, files):
for i, path in enumerate(files):
file_name = os.path.basename(path)
if file_name.endswith(".png"):
yield i, {
"image": path,
"labels": os.path.basename(os.path.dirname(path)).lower(),
} |