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dataset uploaded by roboflow2huggingface package

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README.dataset.txt ADDED
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+ # 150 Pokemon > Pokedex resized
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+ https://universe.roboflow.com/robert-demo-qvail/pokedex
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+
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+ Provided by [Lance Zhang](https://www.kaggle.com/lantian773030/pokemonclassification)
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+ License: Public Domain
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+
README.md ADDED
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+ ---
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+ task_categories:
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+ - image-classification
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+ tags:
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+ - roboflow
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+ - roboflow2huggingface
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+ - Gaming
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+ ---
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+
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+ <div align="center">
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+ <img width="640" alt="fcakyon/pokemon-classification" src="https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/thumbnail.jpg">
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+ </div>
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+
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+ ### Dataset Labels
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+
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+ ```
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+ ['test', 'train', 'valid', 'valid-mini']
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+ ```
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+
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+
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+ ### Number of Images
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+
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+ ```json
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+ {'train': 4869, 'test': 732, 'valid': 1390}
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+ ```
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+
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+
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+ ### How to Use
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+
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+ - Install [datasets](https://pypi.org/project/datasets/):
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+
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+ ```bash
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+ pip install datasets
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+ ```
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+
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+ - Load the dataset:
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ ds = load_dataset("fcakyon/pokemon-classification", name="full")
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+ example = ds['train'][0]
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+ ```
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+
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+ ### Roboflow Dataset Page
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+ [https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14](https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14?ref=roboflow2huggingface)
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+
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+ ### Citation
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+
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+ ```
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+ @misc{ pokedex_dataset,
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+ title = { Pokedex Dataset },
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+ type = { Open Source Dataset },
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+ author = { Lance Zhang },
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+ howpublished = { \\url{ https://universe.roboflow.com/robert-demo-qvail/pokedex } },
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+ url = { https://universe.roboflow.com/robert-demo-qvail/pokedex },
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+ journal = { Roboflow Universe },
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+ publisher = { Roboflow },
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+ year = { 2022 },
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+ month = { dec },
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+ note = { visited on 2023-01-14 },
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+ }
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+ ```
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+
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+ ### License
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+ Public Domain
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+
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+ ### Dataset Summary
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+ This dataset was exported via roboflow.com on December 20, 2022 at 5:34 PM GMT
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+
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+ Roboflow is an end-to-end computer vision platform that helps you
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+ * collaborate with your team on computer vision projects
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+ * collect & organize images
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+ * understand unstructured image data
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+ * annotate, and create datasets
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+ * export, train, and deploy computer vision models
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+ * use active learning to improve your dataset over time
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+
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+ It includes 6991 images.
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+ Pokemon are annotated in folder format.
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+
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+ The following pre-processing was applied to each image:
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+ * Auto-orientation of pixel data (with EXIF-orientation stripping)
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+ * Resize to 224x224 (Fit (black edges))
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+
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+ No image augmentation techniques were applied.
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+
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+
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+
README.roboflow.txt ADDED
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+
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+ Pokedex - v14 Pokedex resized
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+ ==============================
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+
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+ This dataset was exported via roboflow.com on December 20, 2022 at 5:34 PM GMT
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+
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+ Roboflow is an end-to-end computer vision platform that helps you
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+ * collaborate with your team on computer vision projects
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+ * collect & organize images
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+ * understand unstructured image data
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+ * annotate, and create datasets
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+ * export, train, and deploy computer vision models
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+ * use active learning to improve your dataset over time
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+
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+ It includes 6991 images.
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+ Pokemon are annotated in folder format.
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+
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+ The following pre-processing was applied to each image:
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+ * Auto-orientation of pixel data (with EXIF-orientation stripping)
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+ * Resize to 224x224 (Fit (black edges))
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+
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+ No image augmentation techniques were applied.
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+
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+
Raichu/thumbnail.jpg ADDED

Git LFS Details

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  • Pointer size: 130 Bytes
  • Size of remote file: 95.6 kB
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pokemon-classification.py ADDED
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+ import os
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+
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+ import datasets
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+ from datasets.tasks import ImageClassification
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+
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+
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+ _HOMEPAGE = "https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14"
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+ _LICENSE = "Public Domain"
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+ _CITATION = """\
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+ @misc{ pokedex_dataset,
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+ title = { Pokedex Dataset },
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+ type = { Open Source Dataset },
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+ author = { Lance Zhang },
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+ howpublished = { \\url{ https://universe.roboflow.com/robert-demo-qvail/pokedex } },
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+ url = { https://universe.roboflow.com/robert-demo-qvail/pokedex },
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+ journal = { Roboflow Universe },
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+ publisher = { Roboflow },
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+ year = { 2022 },
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+ month = { dec },
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+ note = { visited on 2023-01-14 },
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+ }
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+ """
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+ _CATEGORIES = ['test', 'train', 'valid', 'valid-mini']
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+
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+
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+ class POKEMONCLASSIFICATIONConfig(datasets.BuilderConfig):
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+ """Builder Config for pokemon-classification"""
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+
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+ def __init__(self, data_urls, **kwargs):
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+ """
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+ BuilderConfig for pokemon-classification.
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+
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+ Args:
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+ data_urls: `dict`, name to url to download the zip file from.
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+ **kwargs: keyword arguments forwarded to super.
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+ """
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+ super(POKEMONCLASSIFICATIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
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+ self.data_urls = data_urls
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+
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+
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+ class POKEMONCLASSIFICATION(datasets.GeneratorBasedBuilder):
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+ """pokemon-classification image classification dataset"""
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+
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+ VERSION = datasets.Version("1.0.0")
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+ BUILDER_CONFIGS = [
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+ POKEMONCLASSIFICATIONConfig(
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+ name="full",
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+ description="Full version of pokemon-classification dataset.",
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+ data_urls={
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+ "train": "https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/data/train.zip",
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+ "validation": "https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/data/valid.zip",
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+ "test": "https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/data/test.zip",
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+ }
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+ ,
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+ ),
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+ POKEMONCLASSIFICATIONConfig(
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+ name="mini",
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+ description="Mini version of pokemon-classification dataset.",
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+ data_urls={
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+ "train": "https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/data/valid-mini.zip",
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+ "validation": "https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/data/valid-mini.zip",
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+ "test": "https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/data/valid-mini.zip",
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+ },
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+ )
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+ ]
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ features=datasets.Features(
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+ {
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+ "image_file_path": datasets.Value("string"),
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+ "image": datasets.Image(),
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+ "labels": datasets.features.ClassLabel(names=_CATEGORIES),
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+ }
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+ ),
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+ supervised_keys=("image", "labels"),
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+ homepage=_HOMEPAGE,
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+ citation=_CITATION,
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+ license=_LICENSE,
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+ task_templates=[ImageClassification(image_column="image", label_column="labels")],
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ data_files = dl_manager.download_and_extract(self.config.data_urls)
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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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+ "files": dl_manager.iter_files([data_files["train"]]),
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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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+ "files": dl_manager.iter_files([data_files["validation"]]),
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ gen_kwargs={
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+ "files": dl_manager.iter_files([data_files["test"]]),
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+ },
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+ ),
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+ ]
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+
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+ def _generate_examples(self, files):
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+ for i, path in enumerate(files):
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+ file_name = os.path.basename(path)
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+ if file_name.endswith((".jpg", ".png", ".jpeg", ".bmp", ".tif", ".tiff")):
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+ yield i, {
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+ "image_file_path": path,
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+ "image": path,
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+ "labels": os.path.basename(os.path.dirname(path)),
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+ }
split_name_to_num_samples.json ADDED
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+ {"train": 4869, "test": 732, "valid": 1390}