Datasets:
Tasks:
Image Classification
Modalities:
Text
Languages:
English
Size:
< 1K
Tags:
image classification
object detection
product recognition
user experience enhancement
recommendation systems
License:
| tags: | |
| - image classification | |
| - object detection | |
| - product recognition | |
| - user experience enhancement | |
| - recommendation systems | |
| license: cc-by-nc-sa-4.0 | |
| task_categories: | |
| - image-classification | |
| language: | |
| - en | |
| pretty_name: Eyebrow Makeup Product Image Classification Dataset | |
| size_categories: | |
| - 1B<n<10B | |
| # Eyebrow Makeup Product Image Classification Dataset | |
| The current retail e-commerce industry faces the challenge of a wide variety of products and diverse customer needs, especially in the recognition and classification of eyebrow makeup products. Traditional manual annotation methods are inefficient and prone to errors. Existing solutions often fail to meet the requirements for high precision and efficiency, thus there is an urgent need for a high-quality image classification dataset to support the development of automated recognition technologies. This dataset aims to provide a large number of eyebrow makeup product images to assist in training and optimizing image recognition models, thereby enhancing the user shopping experience. Data collection was carried out using professional photographic equipment, shot under natural and standard lighting conditions to ensure image clarity and color accuracy. To ensure data quality, we implemented multiple rounds of annotation and consistency checks, with expert review to ensure annotation accuracy. Data is stored in JPEG format, with structured storage for quick retrieval and analysis. | |
| ## Technical Specifications | |
| | Field | Type | Description | | |
| | :--- | :--- | :--- | | |
| | file_name | string | File name | | |
| | quality | string | Resolution | | |
| | product_type | string | Identifies the specific type of eyebrow makeup product, such as eyebrow pencil or eyebrow powder. | | |
| | color_variation | string | Identifies the color variation of the product, such as black or brown. | | |
| | brand_name | string | Identifies the brand name of the product. | | |
| | product_packaging | string | Identifies the packaging form of the product, such as boxed or single stick. | | |
| ## Compliance Statement | |
| <table> | |
| <tr> | |
| <td>Authorization Type</td> | |
| <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td> | |
| </tr> | |
| <tr> | |
| <td>Commercial Use</td> | |
| <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td> | |
| </tr> | |
| <tr> | |
| <td>Privacy and Anonymization</td> | |
| <td>No PII, no real company names, simulated scenarios follow industry standards</td> | |
| </tr> | |
| <tr> | |
| <td>Compliance System</td> | |
| <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td> | |
| </tr> | |
| </table> | |
| ## Source & Contact | |
| If you need more dataset details, please visit [Mobiusi](https://www.mobiusi.com/datasets/e8829c768a86844f146c8bd8439349b1?utm_source=huggingface&utm_medium=referral). or contact us via contact@mobiusi.com | |