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README.dataset.txt

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-# undefined > original
-https://public.roboflow.ai/classification/undefined
-
-Provided by undefined
-License: CC BY 4.0
-
-undefined

README.md

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----
-task_categories:
-- image-classification
-tags:
-- roboflow
-- roboflow2huggingface
-
----
-
-<div align="center">
-  <img width="640" alt="keremberke/painting-style-classification" src="https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/thumbnail.jpg">
-</div>
-
-### Dataset Labels
-
-```
-['Realism', 'Art_Nouveau_Modern', 'Analytical_Cubism', 'Cubism', 'Expressionism', 'Action_painting', 'Synthetic_Cubism', 'Symbolism', 'Ukiyo_e', 'Naive_Art_Primitivism', 'Post_Impressionism', 'Impressionism', 'Fauvism', 'Rococo', 'Minimalism', 'Mannerism_Late_Renaissance', 'Color_Field_Painting', 'High_Renaissance', 'Romanticism', 'Pop_Art', 'Contemporary_Realism', 'Baroque', 'New_Realism', 'Pointillism', 'Northern_Renaissance', 'Early_Renaissance', 'Abstract_Expressionism']
-```
-
-
-### Number of Images
-
-```json
-{'valid': 1295, 'train': 4493, 'test': 629}
-```
-
-
-### How to Use
-
-- Install [datasets](https://pypi.org/project/datasets/):
-
-```bash
-pip install datasets
-```
-
-- Load the dataset:
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("keremberke/painting-style-classification", name="full")
-example = ds['train'][0]
-```
-
-### Roboflow Dataset Page
-[https://universe.roboflow.com/art-dataset/wiki-art/dataset/1](https://universe.roboflow.com/art-dataset/wiki-art/dataset/1?ref=roboflow2huggingface)
-
-### Citation
-
-```
-@misc{ wiki-art_dataset,
-    title = { wiki art Dataset },
-    type = { Open Source Dataset },
-    author = { Art Dataset },
-    howpublished = { \\url{ https://universe.roboflow.com/art-dataset/wiki-art } },
-    url = { https://universe.roboflow.com/art-dataset/wiki-art },
-    journal = { Roboflow Universe },
-    publisher = { Roboflow },
-    year = { 2022 },
-    month = { mar },
-    note = { visited on 2023-01-18 },
-}
-```
-
-### License
-CC BY 4.0
-
-### Dataset Summary
-This dataset was exported via roboflow.ai on March 9, 2022 at 1:47 AM GMT
-
-It includes 6417 images.
-27 are annotated in folder format.
-
-The following pre-processing was applied to each image:
-* Auto-orientation of pixel data (with EXIF-orientation stripping)
-* Resize to 416x416 (Stretch)
-
-No image augmentation techniques were applied.
-
-
-

README.roboflow.txt

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-
-wiki art - v1 original
-==============================
-
-This dataset was exported via roboflow.ai on March 9, 2022 at 1:47 AM GMT
-
-It includes 6417 images.
-27 are annotated in folder format.
-
-The following pre-processing was applied to each image:
-* Auto-orientation of pixel data (with EXIF-orientation stripping)
-* Resize to 416x416 (Stretch)
-
-No image augmentation techniques were applied.
-
-

data/valid.zip → full/painting-style-classification-test.parquet

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data/train.zip → full/painting-style-classification-train.parquet

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data/test.zip → full/painting-style-classification-validation.parquet

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thumbnail.jpg → mini/painting-style-classification-test.parquet

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data/valid-mini.zip → mini/painting-style-classification-train.parquet

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mini/painting-style-classification-validation.parquet

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painting-style-classification.py

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-import os
-
-import datasets
-from datasets.tasks import ImageClassification
-
-
-_HOMEPAGE = "https://universe.roboflow.com/art-dataset/wiki-art/dataset/1"
-_LICENSE = "CC BY 4.0"
-_CITATION = """\
-@misc{ wiki-art_dataset,
-    title = { wiki art Dataset },
-    type = { Open Source Dataset },
-    author = { Art Dataset },
-    howpublished = { \\url{ https://universe.roboflow.com/art-dataset/wiki-art } },
-    url = { https://universe.roboflow.com/art-dataset/wiki-art },
-    journal = { Roboflow Universe },
-    publisher = { Roboflow },
-    year = { 2022 },
-    month = { mar },
-    note = { visited on 2023-01-18 },
-}
-"""
-_CATEGORIES = ['Realism', 'Art_Nouveau_Modern', 'Analytical_Cubism', 'Cubism', 'Expressionism', 'Action_painting', 'Synthetic_Cubism', 'Symbolism', 'Ukiyo_e', 'Naive_Art_Primitivism', 'Post_Impressionism', 'Impressionism', 'Fauvism', 'Rococo', 'Minimalism', 'Mannerism_Late_Renaissance', 'Color_Field_Painting', 'High_Renaissance', 'Romanticism', 'Pop_Art', 'Contemporary_Realism', 'Baroque', 'New_Realism', 'Pointillism', 'Northern_Renaissance', 'Early_Renaissance', 'Abstract_Expressionism']
-
-
-class PAINTINGSTYLECLASSIFICATIONConfig(datasets.BuilderConfig):
-    """Builder Config for painting-style-classification"""
-
-    def __init__(self, data_urls, **kwargs):
-        """
-        BuilderConfig for painting-style-classification.
-
-        Args:
-          data_urls: `dict`, name to url to download the zip file from.
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(PAINTINGSTYLECLASSIFICATIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
-        self.data_urls = data_urls
-
-
-class PAINTINGSTYLECLASSIFICATION(datasets.GeneratorBasedBuilder):
-    """painting-style-classification image classification dataset"""
-
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        PAINTINGSTYLECLASSIFICATIONConfig(
-            name="full",
-            description="Full version of painting-style-classification dataset.",
-            data_urls={
-    "train": "https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/data/train.zip",
-    "validation": "https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/data/valid.zip",
-    "test": "https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/data/test.zip",
-}
-,
-        ),
-        PAINTINGSTYLECLASSIFICATIONConfig(
-            name="mini",
-            description="Mini version of painting-style-classification dataset.",
-            data_urls={
-                "train": "https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/data/valid-mini.zip",
-                "validation": "https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/data/valid-mini.zip",
-                "test": "https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/data/valid-mini.zip",
-            },
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=datasets.Features(
-                {
-                    "image_file_path": datasets.Value("string"),
-                    "image": datasets.Image(),
-                    "labels": datasets.features.ClassLabel(names=_CATEGORIES),
-                }
-            ),
-            supervised_keys=("image", "labels"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE,
-            task_templates=[ImageClassification(image_column="image", label_column="labels")],
-        )
-
-    def _split_generators(self, dl_manager):
-        data_files = dl_manager.download_and_extract(self.config.data_urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "files": dl_manager.iter_files([data_files["train"]]),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "files": dl_manager.iter_files([data_files["validation"]]),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "files": dl_manager.iter_files([data_files["test"]]),
-                },
-            ),
-]
-
-    def _generate_examples(self, files):
-        for i, path in enumerate(files):
-            file_name = os.path.basename(path)
-            if file_name.endswith((".jpg", ".png", ".jpeg", ".bmp", ".tif", ".tiff")):
-                yield i, {
-                    "image_file_path": path,
-                    "image": path,
-                    "labels": os.path.basename(os.path.dirname(path)),
-                }

split_name_to_num_samples.json

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-{"valid": 1295, "train": 4493, "test": 629}