Eyeliner Product Image Classification Dataset
#classification task #image recognition #deep learning training #product recognition #user experience optimization #market analysis
- 20000 records
- 2.5G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-10-19
AI Analysis & Value Prop
The current retail e-commerce industry faces challenges such as low product recognition efficiency and poor user experience, especially in the classification of eyeliner products, where traditional methods fail to meet user demands. Existing solutions often rely on manual labeling, which is inefficient and prone to errors. This dataset aims to provide high-quality eyeliner product images to enhance recognition accuracy and support the training of deep learning models. The data collection process uses high-resolution cameras in a standardized environment to ensure image clarity and consistency. We implement multiple rounds of annotation and expert review procedures to ensure data quality and accuracy. Data are stored in JPG format for quick reading and processing.
Dataset Insights
Sample Examples
adb2e192**.png|1749*1280|1.40 MB
5ca71945**.png|977*1280|1.50 MB
ff816edd**.png|945*1280|230.61 KB
f8353cd0**.png|990*1280|536.55 KB
5bb8e89d**.png|1280*961|626.96 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| main_color | string | The primary color in the image, used for identifying the type of color of the product. |
| product_type | string | The specific type of eyeliner product identified from the image, such as liquid eyeliner, gel eyeliner, pencil eyeliner, etc. |
| brand_recognition | string | Identify the product brand based on the logo or packaging visible in the image. |
| package_type | string | Identify the packaging style of the product, such as boxed or bagged. |
| texture_indicator | string | Determine the texture properties of the product through the texture and glossiness of the image. |
| printed_text | string | Identifies printed text in the image, which helps confirm product information. |
| size_estimation | string | Estimate the size of the product through image perspective and proportion. |
| cap_design | string | Identify the design style of the cap for eyeliner products, such as screw cap, snap cap, 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
- How many different types of eyeliner product images does this dataset contain?
- This dataset contains multiple types of eyeliner product images, each carefully categorized.
- On which retail platforms can the Eyeliner Product Image Classification Dataset be applied?
- The Eyeliner Product Image Classification Dataset can be applied to various e-commerce platforms like Taobao and Amazon to enhance product recognition and management efficiency.
- What are the main benefits of using this dataset?
- The main benefits of using this dataset include significantly improving the classification accuracy and recognition speed of products on e-commerce platforms, thereby enhancing the user shopping experience.
- What technologies are used in this dataset to achieve better classification results?
- This dataset employs state-of-the-art image recognition and deep learning technologies in image classification to ensure optimal results.
- How does this dataset improve recognition accuracy during model training?
- The dataset improves recognition accuracy during model training through a diverse range of training samples and advanced annotation methods.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Eyeliner Product Image Classification Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/557441030327b4bb6bb18e337784fa41?dataset_scene_id=9},
urldate={2025-09-15},
keywords={eyeliner product image classification, e-commerce image dataset, product recognition dataset},
version={1.0}
}Using this in research? Please cite us.
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