| | --- |
| | 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. |
| |
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| |
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| |
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| |
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