Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
Indonesian
Size Categories:
n<1K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
License:
| # 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. | |
| """Coffee beans dataset with images.""" | |
| import os | |
| import datasets | |
| from datasets.tasks import ImageClassification | |
| _HOMEPAGE="""rasyid.dev/coffee_detection""" | |
| _CITATION = """\ | |
| N/A | |
| """ | |
| _DESCRIPTION = """\ | |
| Beans is a dataset of images of beans taken in the field using smartphone | |
| cameras. It consists of 3 classes: Grade 1, 2 and 3 | |
| """ | |
| _URLS = { | |
| "train": "https://huggingface.co/datasets/rasyidf/coffee-beans/resolve/main/data/train.zip", | |
| "validation": "https://huggingface.co/datasets/rasyidf/coffee-beans/resolve/main/data/validation.zip", | |
| "test": "https://huggingface.co/datasets/rasyidf/coffee-beans/resolve/main/data/test.zip", | |
| } | |
| _NAMES = ["1", "2", "3", "0"] | |
| class Beans(datasets.GeneratorBasedBuilder): | |
| """Coffee Beans images dataset.""" | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "image_file_path": datasets.Value("string"), | |
| "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(".jpg"): | |
| yield i, { | |
| "image_file_path": path, | |
| "image": path, | |
| "labels": os.path.basename(os.path.dirname(path)).lower(), | |
| } |