Fashion Gender Classification Dataset with labeled male and female images for binary classification tasks. Ideal for training, validating, and testing machine learning models using frameworks like TensorFlow, Keras, and PyTorch

This dataset was created to support machine learning research in clothing classification, particularly for smart wardrobe and laundry applications. Inspired by the digital wardrobe concept popularized in media such as Clueless (1995), the dataset contains three primary categories of clothing items: - Tops: t-shirts, button-up shirts, sweaters, hoodies, and other upper garments. - Bottoms: jeans, shorts, formal pants, long trousers, and other lower garments. - Socks: long socks and short socks photographed in pairs and individually.

All images were self-collected using an iPhone camera in HEIC format and later converted to JPG/PNG. Backgrounds were removed manually using Canva and programmatically using Rembg with the U²-Net model. Augmentation techniques (rotation, flipping, cropping, brightness and contrast adjustments) were applied to increase dataset diversity. - Raw images: 521 (200 tops, 200 bottoms, 121 socks) - Final images after augmentation: ~1,900 (balanced across all classes)

This dataset can be used for experiments in: - Image classification - Data augmentation pipelines - Transfer learning (e.g., Teachable Machine, TensorFlow, PyTorch) - Applied computer vision in smart wardrobe and smart home systems

Fashion-MNIST is a dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('fashion_mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

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