Skincare Lotion Image Classification Dataset
#Classification tasks #image recognition #Product recognition #market analysis #user recommendation
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-04-13
AI Analysis & Value Prop
In the retail e-commerce industry, there are numerous types of skincare products, posing decision challenges for consumers. Existing image classification technologies still have deficiencies in recognition accuracy and speed, especially given product diversity and complex backgrounds. This dataset aims to enhance the accuracy of skincare lotion image classification through high-quality image samples and precise annotations, offering better product recommendation services for e-commerce platforms. Data collection was performed using professional photography equipment in a standardized environment to ensure clear images with good lighting conditions. We employed multi-round annotations and expert review mechanisms to ensure consistency and accuracy in data annotation. The data is stored in JPG format, organized by category, facilitating subsequent training and testing use.
Dataset Insights
Sample Examples
304bc98a**.jpg|1400*1793|149.86 KB
68a5078c**.jpg|1400*1369|112.88 KB
33805b4f**.jpg|1400*1793|122.90 KB
a5d153da**.jpg|1400*1793|106.35 KB
6b423376**.jpg|1400*1793|100.04 KB
4e29c1f2**.jpg|1400*1793|135.46 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| product_type | string | Identifies the type of skincare lotion in the image, such as lotion, essence, etc. |
| brand_name | string | Identifies the brand of the skincare lotion in the image, such as Lancôme, Estée Lauder, etc. |
| texture | string | Determines the texture characteristics of the skincare lotion in the image, such as transparent, white lotion, etc. |
| bottle_material | string | Identifies the material of the skincare lotion container, such as glass, plastic, etc. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What information does the skincare lotion image classification dataset contain?
- The dataset contains high-quality image samples for classifying skincare lotions, suitable for product recognition and classification tasks on e-commerce platforms.
- How can the skincare lotion image classification dataset be used to improve product recommendations?
- By accurately classifying images, the dataset can help e-commerce platforms enhance their product recommendation algorithms, increasing the relevance and user satisfaction of recommendations.
- Which businesses can benefit from the skincare lotion image classification dataset?
- E-commerce businesses selling skincare products, skincare brand companies, and any retailers needing a product image classification system can benefit.
- What technologies is the skincare lotion image classification dataset suitable for?
- The dataset is suitable for machine learning and deep learning technologies, such as convolutional neural networks (CNN), for image recognition and classification.
- How does the skincare lotion image classification dataset support the development of the e-commerce industry?
- By providing high-accuracy classification information, the dataset enables e-commerce businesses to improve inventory management, market analysis, and customer engagement.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Skincare Lotion Image Classification Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/661bb0afc161d21aff1ce253bceb36fa?dataset_scene_id=9},
urldate={2025-09-15},
keywords={skincare lotion, image classification, retail e-commerce, dataset, machine learning},
version={1.0}
}Using this in research? Please cite us.
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