dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# Nike Adidas and Converse Shoes Classification > rawImages_70-20-10split
+https://universe.roboflow.com/popular-benchmarks/nike-adidas-and-converse-shoes-classification
+
+Provided by a Roboflow user
+License: Public Domain
+
+## Nike, Adidas and Converse Shoes Dataset for Classification
+
+This dataset was obtained from [Kaggle](https://kaggle.com): https://www.kaggle.com/datasets/die9origephit/nike-adidas-and-converse-imaged/
+
+### Dataset Collection Methodology:
+"The dataset was obtained downloading images from `Google images`. The images with a `.webp` format were transformed into `.jpg` images. The obtained images were randomly shuffled and resized so that all the images had a resolution of `240x240 pixels`. Then, they were split into `train` and `test` datasets and saved."
+
+### Versions:
+* *v1*: `original_raw-images`: the original images without [Preprocessing](https://docs.roboflow.com/image-transformations/image-preprocessing) or [Augmentation](https://docs.roboflow.com/image-transformations/image-augmentation) applied, other than [Auto-Orient to remove EXIF data](https://blog.roboflow.com/exif-auto-orientation/). These images are in the original train/test split from Kaggle: `237 images in each train set` and `38 images in each test set`
+* *v2*: `original_trainTestSplit-augmented3x`: the original train/test split, augmented with 3x image generation. This version was not trained with [Roboflow Train](https://docs.roboflow.com/train).
+* *v3*: `original_trainTestSplit-augmented5x`: the original train/test split, augmented with 5x image generation. This version was not trained with Roboflow Train.
+* *v4*: `rawImages_70-20-10split`: the original images without Preprocessing or Augmentation applied, other than Auto-Orient to remove EXIF data. Dataset splies were modified to a `70% train`, `20% valid`, `10%` test [train/valid/test split](https://blog.roboflow.com/train-test-split/)
+	* NOTE: 70%/20%/10% split: `576 images in train set`, `166 images in valid set`, `83 images in test set`
+* *v5*: `70-20-10split-augmented3x`: modified to a `70% train`, `20% valid`, `10%` test train/valid/test split, augmented with 3x image generation. This version was trained with Roboflow Train.
+* *v6*: `70-20-10split-augmented5x`: modified to a `70% train`, `20% valid`, `10%` test train/valid/test split, augmented with 5x image generation. This version was trained with Roboflow Train.

README.md

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+---
+task_categories:
+- image-classification
+tags:
+- roboflow
+- roboflow2huggingface
+- Sports
+- Retail
+- Benchmark
+---
+
+<div align="center">
+  <img width="640" alt="keremberke/shoe-classification" src="https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/thumbnail.jpg">
+</div>
+
+### Dataset Labels
+
+```
+['converse', 'adidas', 'nike']
+```
+
+
+### Number of Images
+
+```json
+{'train': 576, 'test': 83, 'valid': 166}
+```
+
+
+### 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/shoe-classification", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/popular-benchmarks/nike-adidas-and-converse-shoes-classification/dataset/4](https://universe.roboflow.com/popular-benchmarks/nike-adidas-and-converse-shoes-classification/dataset/4?ref=roboflow2huggingface)
+
+### Citation
+
+```
+
+```
+
+### License
+Public Domain
+
+### Dataset Summary
+This dataset was exported via roboflow.com on October 28, 2022 at 2:38 AM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+It includes 825 images.
+Shoes are annotated in folder format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+
+No image augmentation techniques were applied.
+
+
+

README.roboflow.txt

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+
+Nike Adidas and Converse Shoes Classification - v4 rawImages_70-20-10split
+==============================
+
+This dataset was exported via roboflow.com on October 28, 2022 at 2:38 AM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+It includes 825 images.
+Shoes are annotated in folder format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+
+No image augmentation techniques were applied.
+
+

data/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d065e7520eb26d1320420e77468a6543e67ce2ff5c445ead4ef0f8ad76cd1084
+size 769200

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:20c43e3ca05372202b35a44b61d418b5dc812f23cb893d36f1ecdb5b91e64add
+size 5127451

data/valid-mini.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:562174fb8186ae8ce519dcc309a6cd5cabe43fd82dcf0a0544f2cc28123f6e7f
+size 18516

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:90ee4574c99c8e08c5fd83cef2c94f323211afa8d1c2e07eb4f9ca665877ac92
+size 1526731

shoe-classification.py

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+import os
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_HOMEPAGE = "https://universe.roboflow.com/popular-benchmarks/nike-adidas-and-converse-shoes-classification/dataset/4"
+_LICENSE = "Public Domain"
+_CITATION = """\
+
+"""
+_CATEGORIES = ['converse', 'adidas', 'nike']
+
+
+class SHOECLASSIFICATIONConfig(datasets.BuilderConfig):
+    """Builder Config for shoe-classification"""
+
+    def __init__(self, data_urls, **kwargs):
+        """
+        BuilderConfig for shoe-classification.
+
+        Args:
+          data_urls: `dict`, name to url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SHOECLASSIFICATIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_urls = data_urls
+
+
+class SHOECLASSIFICATION(datasets.GeneratorBasedBuilder):
+    """shoe-classification image classification dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        SHOECLASSIFICATIONConfig(
+            name="full",
+            description="Full version of shoe-classification dataset.",
+            data_urls={
+    "train": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/valid.zip",
+    "test": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/test.zip",
+}
+,
+        ),
+        SHOECLASSIFICATIONConfig(
+            name="mini",
+            description="Mini version of shoe-classification dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/valid-mini.zip",
+                "validation": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/valid-mini.zip",
+                "test": "https://huggingface.co/datasets/keremberke/shoe-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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+{"train": 576, "test": 83, "valid": 166}