import datasets import pandas as pd _CITATION = """\ @InProceedings{huggingface:dataset, title = {bald_classification}, author = {TrainingDataPro}, year = {2023} } """ _DESCRIPTION = """\ Dataset consists of 5000 photos of people with 7 stages of hairloss according to the Norwood scale. Dataset is useful for training neural networks for the recommendation systems, optimizing the work processes of trichologists and applications in the Med / Beauty spheres. """ _NAME = 'bald_classification' _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" _LICENSE = "" _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" class BaldClassification(datasets.GeneratorBasedBuilder): """Small sample of image-text pairs""" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features({ 'image_id': datasets.Value('int32'), 'image': datasets.Image(), 'annotations': datasets.Value('string') }), supervised_keys=None, homepage=_HOMEPAGE, citation=_CITATION, ) def _split_generators(self, dl_manager): images = dl_manager.download(f"{_DATA}images.tar.gz") annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") images = dl_manager.iter_archive(images) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={ "images": images, 'annotations': annotations }), ] def _generate_examples(self, images, annotations): annotations_df = pd.read_csv(annotations) for idx, (image_path, image) in enumerate(images): yield idx, { 'image_id': annotations_df.loc[ annotations_df['image_name'] == image_path] ['image_id'].values[0], "image": { "path": image_path, "bytes": image.read() }, 'annotations': annotations_df.loc[ annotations_df['image_name'] == image_path] ['annotations'].values[0] }