Datasets:
Tasks:
Image Classification
Formats:
parquet
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
1K - 10K
License:
Commit
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Parent(s):
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Delete loading script
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beans.py
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# coding=utf-8
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# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Beans leaf dataset with images of diseased and health leaves."""
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import os
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import datasets
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from datasets.tasks import ImageClassification
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_HOMEPAGE = "https://github.com/AI-Lab-Makerere/ibean/"
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_CITATION = """\
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@ONLINE {beansdata,
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author="Makerere AI Lab",
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title="Bean disease dataset",
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month="January",
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year="2020",
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url="https://github.com/AI-Lab-Makerere/ibean/"
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}
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"""
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_DESCRIPTION = """\
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Beans is a dataset of images of beans taken in the field using smartphone
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cameras. It consists of 3 classes: 2 disease classes and the healthy class.
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Diseases depicted include Angular Leaf Spot and Bean Rust. Data was annotated
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by experts from the National Crops Resources Research Institute (NaCRRI) in
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Uganda and collected by the Makerere AI research lab.
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"""
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_URLS = {
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"train": "https://huggingface.co/datasets/beans/resolve/main/data/train.zip",
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"validation": "https://huggingface.co/datasets/beans/resolve/main/data/validation.zip",
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"test": "https://huggingface.co/datasets/beans/resolve/main/data/test.zip",
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}
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_NAMES = ["angular_leaf_spot", "bean_rust", "healthy"]
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class Beans(datasets.GeneratorBasedBuilder):
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"""Beans plant leaf images dataset."""
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"image_file_path": datasets.Value("string"),
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"image": datasets.Image(),
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"labels": datasets.features.ClassLabel(names=_NAMES),
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}
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),
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supervised_keys=("image", "labels"),
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homepage=_HOMEPAGE,
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citation=_CITATION,
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task_templates=[ImageClassification(image_column="image", label_column="labels")],
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)
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def _split_generators(self, dl_manager):
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data_files = dl_manager.download_and_extract(_URLS)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"files": dl_manager.iter_files([data_files["train"]]),
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"files": dl_manager.iter_files([data_files["validation"]]),
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"files": dl_manager.iter_files([data_files["test"]]),
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},
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),
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]
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def _generate_examples(self, files):
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for i, path in enumerate(files):
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file_name = os.path.basename(path)
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if file_name.endswith(".jpg"):
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yield i, {
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"image_file_path": path,
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"image": path,
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"labels": os.path.basename(os.path.dirname(path)).lower(),
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}
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