Children's Dress Image Classification Dataset

#Image Classification #Object Detection #Product Recognition #Market Analysis #User Recommendations
  • 15000 records
  • 2.8G
  • JPG/PNG/JSON
  • CATL
  • MOBIUSI INCMOBIUSI INC
Updated:2026-04-16

AI Analysis & Value Prop

The current retail e-commerce industry faces challenges such as a wide variety of products and consumer difficulty in choosing, especially in the field of children's clothing, where the accuracy of product recognition and classification directly affects sales and user experience. Existing solutions often rely on manual annotation, which is inefficient and prone to error. This dataset aims to address the need for automatic recognition and classification by providing high-quality images of children's dresses. The dataset is collected from various e-commerce platforms using web crawling technology, ensuring sample diversity. Data quality control is conducted through multiple rounds of annotation, consistency checks, and expert review to ensure accuracy and consistency of the annotations. Data is stored in JPG format and organized in a folder structure for easy subsequent reading and processing.

Dataset Insights

Sample Examples

a8dfd872**.png|1027*1280|1.29 MB

7f3b25cd**.png|966*1280|1.06 MB

f3d94a4a**.png|935*1280|1.20 MB

76a35390**.png|944*1280|1.18 MB

d494df56**.png|999*1280|775.02 KB

Technical Specifications

FieldTypeDescription
file_namestringFile name
qualitystringResolution
colorstringThe primary color of the children's dress in the image.
patternstringThe type of pattern shown on the children's dress, such as stripes or prints.
sleeve_lengthstringThe length of the sleeves on the children's dress, such as short sleeves, long sleeves, or sleeveless.
dress_lengthstringThe length of the children's dress, such as mini, knee-length, or maxi.
stylestringThe design style of the children's dress, such as casual, formal, or party style.
materialstringThe primary material of the children's dress, such as cotton or polyester.
embellishmentsstringThe embellishment details on the children's dress, such as lace, buttons, or bows.

Compliance Statement

Authorization TypeProprietary - Commercial AI Training License (No Redistribution)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What are the potential applications of the Children's Dresses Image Classification Dataset?
This dataset can be used for product recognition and classification on e-commerce platforms, inventory management, virtual fitting rooms, and other applications.
How does this dataset help improve efficiency in the retail industry?
By automating the classification and management of product images, this dataset helps retailers rapidly identify and organize products, enhancing business operation efficiency.
Is the Children's Dresses Image Classification Dataset suitable for machine learning beginners?
Yes, the dataset contains labeled and diverse image data, making it an ideal practice project for machine learning beginners.
What should be considered when using this dataset for image classification?
When using the dataset, attention should be paid to its balance and quality, ensuring diversity in the sample set and appropriate image resolution to improve model accuracy.
What types of children's dress images are included in the dataset?
The dataset includes images of children's dresses with various styles and colors for use in training classification models.

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Cite this Work

@dataset{Mobiusi2025,
  title={Children's Dress Image Classification Dataset},
  author={MOBIUSI INC},
  year={2025},
  url={https://www.mobiusi.com/datasets/5ab96492c574fed751012cc772275abe},
  urldate={2025-09-15},
  keywords={Children's Dresses, Image Classification Dataset, Retail E-commerce, Product Recognition},
  version={1.0}
}

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