Datasets:

frgfm
/

imagenette

@@ -1,160 +0,0 @@
----
-annotations_creators:
-- crowdsourced
-language:
-- en
-language_creators:
-- crowdsourced
-license:
-- apache-2.0
-multilinguality: []
-pretty_name: Imagenette
-size_categories:
-- 1K<n<10K
-source_datasets:
-- extended
-task_categories:
-- image-classification
-task_ids:
-- image-classification
-paperswithcode_id: imagenette
----
-# Dataset Card for Imagenette
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-## Dataset Description
-- **Homepage:** https://github.com/fastai/imagenette
-- **Repository:** https://github.com/fastai/imagenette
-- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagenette
-### Dataset Summary
-A smaller subset of 10 easily classified classes from [Imagenet](https://huggingface.co/datasets/imagenet-1k#dataset-summary), and a little more French.
-This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward), and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset.
-### Supported Tasks and Leaderboards
-- `image-classification`: The dataset can be used to train a model for Image Classification.
-### Languages
-The class labels in the dataset are in English.
-## Dataset Structure
-### Data Instances
-A data point comprises an image URL and its classification label.
-```
-{
-  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=320x320 at 0x19FA12186D8>,
-  'label': 'tench',
-}
-```
-### Data Fields
-- `image`: A `PIL.Image.Image` object containing the image.
-- `label`: the expected class label of the image.
-### Data Splits
-|          |train|validation|
-|----------|----:|---------:|
-|imagenette| 9469|      3925|
-## Dataset Creation
-### Curation Rationale
-cf. https://huggingface.co/datasets/imagenet-1k#curation-rationale
-### Source Data
-#### Initial Data Collection and Normalization
-Imagenette is a subset of [ImageNet](https://huggingface.co/datasets/imagenet-1k). Information about data collection of the source data can be found [here](https://huggingface.co/datasets/imagenet-1k#initial-data-collection-and-normalization).
-### Annotations
-#### Annotation process
-cf. https://huggingface.co/datasets/imagenet-1k#annotation-process
-#### Who are the annotators?
-cf. https://huggingface.co/datasets/imagenet-1k#who-are-the-annotators
-### Personal and Sensitive Information
-cf. https://huggingface.co/datasets/imagenet-1k#personal-and-sensitive-information
-## Considerations for Using the Data
-### Social Impact of Dataset
-cf. https://huggingface.co/datasets/imagenet-1k#social-impact-of-dataset
-### Discussion of Biases
-cf. https://huggingface.co/datasets/imagenet-1k#discussion-of-biases
-### Other Known Limitations
-cf. https://huggingface.co/datasets/imagenet-1k#other-known-limitations
-## Additional Information
-### Dataset Curators
-cf. https://huggingface.co/datasets/imagenet-1k#dataset-curators
-and Jeremy Howard
-### Licensing Information
-[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
-### Citation Information
-```
-@software{Howard_Imagenette_2019,
-    title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},
-    author={Jeremy Howard},
-    year={2019},
-    month={March},
-    publisher = {GitHub},
-    url = {https://github.com/fastai/imagenette}
-}
-```
-### Contributions
-This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward) and published on [Github](https://github.com/fastai/imagenette). It was then only integrated into HuggingFace Datasets by [@frgfm](https://huggingface.co/frgfm).

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-{
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-    "citation": "@software{Howard_Imagenette_2019,\ntitle={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},\nauthor={Jeremy Howard},\nyear={2019},\nmonth={March},\npublisher = {GitHub},\nurl = {https://github.com/fastai/imagenette}\n}\n",
-    "homepage": "https://github.com/fastai/imagenette",
-    "license": "Apache License 2.0",
-    "features": {
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-	      "id": null,
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-      },
-      "label": {
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-	      "id": null,
-	      "_type": "ClassLabel"
-      }
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-    "post_processed": null,
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-      }
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imagenette.py DELETED Viewed

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-# Copyright (C) 2022, François-Guillaume Fernandez.
-# This program is licensed under the Apache License 2.0.
-# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0> for full license details.
-"""Imagenette dataset."""
-import os
-import json
-import datasets
-_HOMEPAGE = "https://github.com/fastai/imagenette"
-_LICENSE = "Apache License 2.0"
-_CITATION = """\
-@software{Howard_Imagenette_2019,
-    title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},
-    author={Jeremy Howard},
-    year={2019},
-    month={March},
-    publisher = {GitHub},
-    url = {https://github.com/fastai/imagenette}
-}
-"""
-_DESCRIPTION = """\
-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).
-"""
-_LABEL_MAP = [
-    'n01440764',
-    'n02102040',
-    'n02979186',
-    'n03000684',
-    'n03028079',
-    'n03394916',
-    'n03417042',
-    'n03425413',
-    'n03445777',
-    'n03888257',
-]
-_REPO = "https://huggingface.co/datasets/frgfm/imagenette/resolve/main/metadata"
-class ImagenetteConfig(datasets.BuilderConfig):
-    """BuilderConfig for Imagette."""
-    def __init__(self, data_url, metadata_urls, **kwargs):
-        """BuilderConfig for Imagette.
-        Args:
-          data_url: `string`, url to download the zip file from.
-          matadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(ImagenetteConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
-        self.data_url = data_url
-        self.metadata_urls = metadata_urls
-class Imagenette(datasets.GeneratorBasedBuilder):
-    """Imagenette dataset."""
-    BUILDER_CONFIGS = [
-        ImagenetteConfig(
-            name="full_size",
-            description="All images are in their original size.",
-            data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz",
-            metadata_urls={
-                "train": f"{_REPO}/imagenette2/train.txt",
-                "validation": f"{_REPO}/imagenette2/val.txt",
-            },
-        ),
-        ImagenetteConfig(
-            name="320px",
-            description="All images were resized on their shortest side to 320 pixels.",
-            data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz",
-            metadata_urls={
-                "train": f"{_REPO}/imagenette2-320/train.txt",
-                "validation": f"{_REPO}/imagenette2-320/val.txt",
-            },
-        ),
-        ImagenetteConfig(
-            name="160px",
-            description="All images were resized on their shortest side to 160 pixels.",
-            data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-160.tgz",
-            metadata_urls={
-                "train": f"{_REPO}/imagenette2-160/train.txt",
-                "validation": f"{_REPO}/imagenette2-160/val.txt",
-            },
-        ),
-    ]
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION + self.config.description,
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "label": datasets.ClassLabel(
-                        names=[
-                            "tench",
-                            "English springer",
-                            "cassette player",
-                            "chain saw",
-                            "church",
-                            "French horn",
-                            "garbage truck",
-                            "gas pump",
-                            "golf ball",
-                            "parachute",
-                        ]
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-    def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download(self.config.data_url)
-        metadata_paths = dl_manager.download(self.config.metadata_urls)
-        archive_iter = dl_manager.iter_archive(archive_path)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "images": archive_iter,
-                    "metadata_path": metadata_paths["train"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "images": archive_iter,
-                    "metadata_path": metadata_paths["validation"],
-                },
-            ),
-        ]
-    def _generate_examples(self, images, metadata_path):
-        with open(metadata_path, encoding="utf-8") as f:
-            files_to_keep = set(f.read().split("\n"))
-        idx = 0
-        for file_path, file_obj in images:
-            if file_path in files_to_keep:
-                label = _LABEL_MAP.index(file_path.split("/")[-2])
-                yield idx, {
-                    "image": {"path": file_path, "bytes": file_obj.read()},
-                    "label": label,
-                }
-                idx += 1

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