Multi-Class Fashion Item Detection Images with Seasonal Labels

Encompassing 144,810 images for fashion item detection, with 19,968 images of male subjects and 124,842 of female subjects. Fashion items are annotated with rectangular bounding boxes and categorized into four seasons: spring, autumn, summer, and winter. The data was created by Nexdata.

Use Cases

• Train an object detection model to locate fashion items using the provided rectangular bounding box annotations.
• Analyze the distribution of fashion items across the four seasonal categories (spring, autumn, summer, winter).
• Build a classifier to predict the gender of the subject (male or female) based on the associated image.
• Compare the frequency of detected fashion items between the 124,842 female subject images and the 19,968 male subject images.

Strengths

• 144,810 total images provide a substantial sample for model training.
• Images are categorized into four distinct seasonal labels (spring, autumn, summer, winter).
• Dataset includes gender labels for subjects, with 124,842 female and 19,968 male images.

Limitations

• The dataset is described as a sample of a larger paid dataset, so it may not be the complete collection.
• The gender distribution is heavily imbalanced, with over six times more female subject images than male.

Provenance

Source

Nexdata

Collection Method

null

Time Range

null

Freshness

The dataset was last updated on 2025-04-24.

Geography

Region tag suggests 'us', but this is not confirmed in the description.