Shoe-Net-10K Dataset

The Shoe-Net-10K dataset is a curated collection of 10,000 shoe images annotated for multi-class image classification. This dataset is suitable for training deep learning models to recognize different types of shoes from images.

Dataset Details

• Total Images: 10,000

• Image Size: Varies (typical width range: 94 px to 519 px)

• Format: Parquet

• Split:

  • train: 10,000 images

• Modality: Image

• License: Apache 2.0

Labels

The dataset includes 5 distinct shoe categories:

labels_list = [
  'Ballet Flat',
  'Boat',
  'Brogue',
  'Clog',
  'Sneaker'
]

Each image is labeled with one of the above shoe types.

Usage

You can load this dataset using the Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Shoe-Net-10K")

Access individual samples as follows:

sample = dataset["train"][0]
image = sample["image"]
label = sample["label"]

Applications

This dataset can be used for:

• Image classification
• Shoe-type detection
• Retail recommendation systems
• Style and fashion recognition models