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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 → full/shoe-classification-test.parquet

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+oid sha256:48acf76814e1a4dd9d0613e9dfd46654a7c16aae58bcb8d96afa097a4a0dc6ea
+size 777088

data/train.zip → full/shoe-classification-train.parquet

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data/valid.zip → full/shoe-classification-validation.parquet

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thumbnail.jpg → mini/shoe-classification-test.parquet

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data/valid-mini.zip → mini/shoe-classification-train.parquet

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mini/shoe-classification-validation.parquet

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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

@@ -1 +0,0 @@
-{"train": 576, "test": 83, "valid": 166}