Facial Cleanser Product Image Classification Dataset

#Image Classification #Feature Extraction #Product Recognition #Market Analysis #User Recommendation
  • 5000 records
  • 1.2G
  • JPG/PNG/JSON
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-03-18

AI Analysis & Value Prop

In the current retail e-commerce industry, the diversity and competitiveness of facial cleanser products are continuously increasing, posing challenges for merchants on how to effectively identify and classify products to enhance user experience. Currently, many existing image classification solutions rely on simple feature matching and cannot accurately handle subtle differences in products, resulting in insufficient classification accuracy. This dataset aims to help merchants improve product recognition accuracy and meet the demand for personalized recommendations by providing high-quality facial cleanser product images. Data collection is conducted using high-resolution cameras under standard lighting conditions to ensure image clarity. To ensure data quality, multiple rounds of annotation and consistency checks are employed, with all annotation results reviewed by experts. The data will be stored in JPEG format and organized according to image ID to facilitate quick retrieval and use.

Dataset Insights

Sample Examples

94fce87b**.png|991*1280|397.14 KB

5b3a9eee**.png|771*1280|718.79 KB

e20e9296**.png|974*1280|1.69 MB

91cc048c**.png|991*1280|706.35 KB

cfddf4a8**.png|988*1280|1.03 MB

Technical Specifications

FieldTypeDescription
file_namestringFile name
qualitystringResolution
product_brandstringThe brand name of the product.
product_typestringThe specific category of the product such as cleanser, gel, etc.
product_colorstringThe color of the product or its packaging.
product_sizestringThe capacity of the product, such as 100ml.
label_textstringThe text information displayed on the product packaging.
product_shapestringThe description of the shape of the product or its packaging.
image_backgroundstringThe type of background in the product image, such as solid color or complex background.
product_texturestringThe description of the texture or surface material of the product packaging.

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 can this dataset be used for?
The facial cleansing product image classification dataset can be used to develop product recognition and recommendation systems on e-commerce platforms, improving shopping experience and precise marketing results.
What types of images does this dataset include?
This dataset includes various types of facial cleansing product images, such as cleansing creams, foams, and gels.
How to process images in the dataset to improve model accuracy?
Images can be processed using data augmentation techniques such as rotation, scaling, and flipping to improve the accuracy of image classification models.
What machine learning models is this dataset suitable for?
This dataset is suitable for various machine learning models, including convolutional neural networks (CNN), which can be used to train image classification models.
What should be considered when using the facial cleansing product image classification dataset?
When using the dataset, it is important to verify the accuracy of image labels and ensure dataset diversity to avoid model overfitting.

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Cite this Work

@dataset{Mobiusi2025,
  title={Facial Cleanser Product Image Classification Dataset},
  author={MOBIUSI INC},
  year={2025},
  url={https://www.mobiusi.com/datasets/38f0af5475081531ed94887087cd23f6?dataset_scene_id=9},
  urldate={2025-09-15},
  keywords={facial cleanser product image, product classification dataset, retail e-commerce image data},
  version={1.0}
}

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