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import rarfile |
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import datasets |
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from io import BytesIO |
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import tempfile |
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class Linnaeus5(datasets.GeneratorBasedBuilder): |
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"""Linnaeus 5 Dataset: RGB images (256x256) for classification across 5 categories.""" |
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VERSION = datasets.Version("1.0.0") |
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def _info(self): |
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return datasets.DatasetInfo( |
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description="""Linnaeus 5 dataset contains RGB images (256x256) for classification across 5 categories: |
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berry, bird, dog, flower, and other (negative set). It includes 1200 training images |
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and 400 test images per class.""", |
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features=datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"label": datasets.ClassLabel(names=["berry", "bird", "dog", "flower", "other"]), |
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} |
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), |
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supervised_keys=("image", "label"), |
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homepage="http://chaladze.com/l5/", |
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citation="""@article{chaladze2017linnaeus, |
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title={Linnaeus 5 dataset for machine learning}, |
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author={Chaladze, G and Kalatozishvili, L}, |
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journal={chaladze. com}, |
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year={2017}} |
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""" |
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) |
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def _split_generators(self, dl_manager): |
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archive_path = dl_manager.download( |
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"http://chaladze.com/l5/img/Linnaeus%205%20256X256.rar" |
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) |
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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={"archive_path": archive_path, "split": "train"}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"archive_path": archive_path, "split": "test"}, |
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), |
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] |
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def _generate_examples(self, archive_path, split): |
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with rarfile.RarFile(archive_path) as rar: |
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for member in rar.infolist(): |
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if split in member.filename and member.filename.endswith(".jpg"): |
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label = member.filename.split("/")[2] |
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with rar.open(member) as file: |
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image_bytes = file.read() |
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with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file: |
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temp_file.write(image_bytes) |
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temp_file_path = temp_file.name |
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yield member.filename, { |
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"image": {"path": temp_file_path}, |
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"label": label, |
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} |
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