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import os |
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import json |
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from pathlib import Path |
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import datasets |
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from datasets.tasks import ImageClassification |
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_CITATION = """ |
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@misc{imagenette, |
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author = "Jeremy Howard", |
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title = "imagenette", |
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url = "https://github.com/fastai/imagenette/" |
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} |
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""" |
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_DESCRIPTION = """\ |
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# ImageNette |
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Imagenette is a subset of 10 easily classified classes from Imagenet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute). |
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'Imagenette' is pronounced just like 'Imagenet', except with a corny inauthentic French accent. |
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If you've seen Peter Sellars in The Pink Panther, then think something like that. |
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It's important to ham up the accent as much as possible, otherwise people might not be sure whether you're refering to "Imagenette" or "Imagenet". |
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(Note to native French speakers: to avoid confusion, be sure to use a corny inauthentic American accent when saying "Imagenet". |
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Think something like the philosophy restaurant skit from Monty Python's The Meaning of Life.) |
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This version of the dataset allows researchers/practitioners to quickly try out |
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ideas and share with others. The dataset comes in three variants: |
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* Full size |
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* 320 px |
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* 160 px |
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The '320 px' and '160 px' versions have their shortest side resized to that size, with their aspect ratio maintained. |
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Too easy for you? In that case, you might want to try Imagewoof. |
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# Imagewoof |
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Imagewoof is a subset of 10 classes from Imagenet that aren't so easy to classify, since they're all dog breeds. |
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The breeds are: Australian terrier, Border terrier, Samoyed, Beagle, Shih-Tzu, English foxhound, Rhodesian ridgeback, Dingo, Golden retriever, Old English sheepdog. |
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(No we will not enter in to any discussion in to whether a dingo is in fact a dog. |
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Any suggestions to the contrary are un-Australian. Thank you for your cooperation.) |
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Full size download; |
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320 px download; |
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160 px download. |
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""" |
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_URL_PREFIX = "https://s3.amazonaws.com/fast-ai-imageclas/" |
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_URL_IMAGENET_REFS = 'https://huggingface.co/datasets/jerpint/imagenette/raw/main/imagenet_refs.json' |
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_LABELS = { |
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"imagenette": [ |
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"cassette_player", |
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"chain_saw", |
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"church", |
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"English_springer", |
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"French_horn", |
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"garbage_truck", |
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"gas_pump", |
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"golf_ball", |
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"parachute", |
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"tench", |
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], |
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"imagewoof": [ |
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"Australian_terrier", |
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"beagle", |
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"Border_terrier", |
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"dingo", |
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"English_foxhound", |
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"golden_retriever", |
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"Old_English_sheepdog", |
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"Rhodesian_ridgeback", |
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"Samoyed", |
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"Shih-Tzu", |
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], |
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} |
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_NAME_TO_DIR = { |
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"imagenette-full-res": "imagenette2", |
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"imagenette-320px": "imagenette2-320", |
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"imagenette-160px": "imagenette2-160", |
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"imagewoof-full-res": "imagewoof2", |
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"imagewoof-320px": "imagewoof2-320", |
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"imagewoof-160px": "imagewoof2-160", |
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} |
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class ImagenetteConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Imagenette.""" |
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def __init__(self, name, **kwargs): |
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super(ImagenetteConfig, self).__init__( |
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name=name, description="{} version.".format(name), **kwargs |
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) |
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self.dataset = name.split("-")[0] |
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self.labels = _LABELS[self.dataset] |
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self.name = name |
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def _make_builder_configs(): |
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return [ImagenetteConfig(name) for name in _NAME_TO_DIR] |
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class Imagenette(datasets.GeneratorBasedBuilder): |
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"""A smaller subset of 10 easily classified classes from Imagenet.""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = _make_builder_configs() |
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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": datasets.Image(), |
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"labels": datasets.ClassLabel(names=self.config.labels), |
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} |
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), |
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supervised_keys=("path", "labels"), |
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homepage="https://github.com/fastai/imagenette", |
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citation=_CITATION, |
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task_templates=[ |
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ImageClassification( |
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image_column="path", |
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label_column="labels", |
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) |
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], |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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print(self.__dict__.keys()) |
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print(self.config) |
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name = self.config.name |
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dirname = _NAME_TO_DIR[name] |
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refs_path = dl_manager.download(_URL_IMAGENET_REFS) |
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with open(refs_path) as f: |
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self.ref_to_label = json.load(f) |
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url = _URL_PREFIX + "{}.tgz".format(dirname) |
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path = dl_manager.download_and_extract(url) |
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train_path = os.path.join(path, dirname, "train") |
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val_path = os.path.join(path, dirname, "val") |
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assert os.path.exists(train_path) |
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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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"datapath": train_path, |
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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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"datapath": val_path, |
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}, |
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), |
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] |
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def _generate_examples(self, datapath): |
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"""Yields examples.""" |
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for path in Path(datapath).glob("**/*.JPEG"): |
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record = { |
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"image": str(path), |
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"labels": self.ref_to_label[path.parent.name], |
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} |
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yield path.name, record |
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