dataset uploaded by roboflow2huggingface package

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/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:378c79ffe81cffc2b6f63b802a8231d3140fb69797dc293e2e5dd28027d2b372
+size 21046948

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1462b51db90e9f454c765d59c5a59f597a97850a1c0cb10a9dce7146988b522e
+size 149707318

data/valid-mini.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:23f169d2ae8e0c6d79608c52d9e3344256a578289b0a8e6649f3c90bcc8279fc
+size 1757186

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:92bbb8086d7d879def4586857e5055d3b6393ee63fd11d530c7bbfc8a0a7e668
+size 42768114

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}