import datasets _CITATION = """\ @InProceedings{huggingface:dataset, title = {Small image-text set}, author={James Briggs}, year={2022} } """ _DESCRIPTION = """\ Demo dataset for testing or showing image-text capabilities. """ _HOMEPAGE = "https://huggingface.co/datasets/danbrown/testman-dataset" _LICENSE = "" _REPO = "https://huggingface.co/datasets/danbrown/testman-dataset" class ImageSet(datasets.GeneratorBasedBuilder): """Small sample of image-text pairs""" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { 'text': datasets.Value("string"), 'image': datasets.Image(), } ), supervised_keys=None, homepage=_HOMEPAGE, citation=_CITATION, ) def _split_generators(self, dl_manager): images_archive = dl_manager.download(f"{_REPO}/resolve/main/images.tar.gz") image_iters = dl_manager.iter_archive(images_archive) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "images": image_iters, } ), ] def _generate_examples(self, images): """ This function returns the examples in the raw (text) form.""" for idx, (filepath, image) in enumerate(images): description = filepath.split('/')[-1][:-4] description = description.replace('_', ' ') yield idx, { "image": {"path": filepath, "bytes": image.read()}, "text": description, }