Clothes Rack Image Classification Dataset

#Image Classification #Object Recognition #Smart Home #Internet of Things #Product Recognition
  • 5000 records
  • 1.6G
  • JPG/PNG/JSON
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-02-04

AI Analysis & Value Prop

In the field of smart devices, as smart homes become more popular, user demand for smart clothes racks is increasing. However, there is a lack of high-quality image classification datasets in the market, leading to imprecise recognition and recommendation algorithms for related products. Existing datasets mostly have insufficient sample sizes and varying annotation quality. Therefore, this dataset aims to provide high-quality clothes rack image classification data to support related technological research and product development. The dataset contains 5000 images of different types of clothes racks, all annotated by professionals to ensure consistency and accuracy. Data collection was performed using a high-resolution camera in a standardized shooting environment to ensure image quality. For quality control, we implemented multiple rounds of annotation and consistency checks, with final accuracy confirmed by expert review. The data is stored in JPG format, facilitating user access and usage.

Dataset Insights

Sample Examples

086c238a**.jpg|1080*1422|150.01 KB

bf6e98ed**.jpg|1440*1049|150.91 KB

9eaca074**.jpg|1440*1045|133.48 KB

28b07ac1**.jpg|1440*1048|133.56 KB

74a93ba6**.jpg|1080*1440|196.08 KB

Technical Specifications

FieldTypeDescription
file_namestringFile name
qualitystringResolution
hanger_typestringThe specific type of hanger, such as foldable, fixed, etc.
materialstringThe primary material used in the hanger, such as metal, plastic, wood.
colorstringThe color of the hanger. There might be multiple color combinations.
conditionstringThe condition of the hanger, such as brand new, used.
usage_scenariostringThe applicable usage scenario for the hanger, such as indoor, outdoor.

Compliance Statement

Authorization TypeCC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
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 types of clothes hanger images are included in this dataset?
The clothes hanger image classification dataset contains various types of clothes hanger images, such as retractable, fixed, and smart hangers.
How can the clothes hanger image classification dataset improve smart home systems?
By using this dataset, smart home systems can be trained to recognize and classify different types of clothes hangers, enhancing the system's ability to automatically control related hanger devices.
What are the applications of the clothes hanger image classification dataset in machine learning?
This dataset can be used to train machine learning models for automatic classification and recognition of clothes hangers, which in turn optimizes home space management.
What are the advantages of this dataset in the smart device industry?
The clothes hanger image classification dataset offers high-quality classification data that can enhance the accuracy and automation of smart devices, providing significant advantages in the smart device industry.
How to evaluate the quality of the clothes hanger image classification dataset?
Methods to evaluate the dataset quality include checking the clarity of images, diversity, accuracy of annotations, and whether the data volume is sufficient to meet the training requirements.

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Cite this Work

@dataset{Mobiusi2025,
  title={Clothes Rack Image Classification Dataset},
  author={MOBIUSI INC},
  year={2025},
  url={https://www.mobiusi.com/datasets/5e45d7d833abb1111eba3367f1bd73f3?dataset_scene_id=6},
  urldate={2025-09-15},
  keywords={clothes rack image classification, smart device dataset, image classification dataset},
  version={1.0}
}

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