Restaurant Dietary Health Culture Wall Image Classification Dataset
#image classification #computer vision #deep learning #restaurant health culture promotion #dietary health education #public health management
- 500 records
- 1.2G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
Currently, the restaurant industry commonly faces challenges in image recognition for health culture promotion, with traditional manual management methods being inefficient and having limited coverage. Although some image classification technologies have been applied in other fields, limitations remain in recognition accuracy and real-time performance within restaurant culture walls. This dataset aims to resolve the issue of automated recognition and classification of restaurant dietary health culture wall images, providing support to enhance promotional effects. Data is collected using high-resolution cameras under various lighting conditions to ensure diversity. For quality control, a three-round annotation mechanism is adopted, with consistency checks and expert reviews ensuring annotation accuracy and consistency. The annotation team consists of 10 image processing experts. Data preprocessing includes image denoising, normalization, and enhancement processing, finally stored by theme in JPG format.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| image_category | string | The category of the image related to healthy eating promotion. |
| dominant_colors | string | The dominant colors present in the image, typically used for visual identification. |
| text_presence | boolean | Indicates whether text is present in the image. |
| text_language | string | The language of the text present in the image. |
| object_count | int | The number of main objects identified in the image. |
| image_brightness | float | The average brightness value of the image. |
| image_contrast | float | The contrast level of the image. |
| texture_features | string | The texture features of the image, such as smoothness or roughness. |
| scene_type | string | The type of scene depicted in the image, such as indoor or outdoor. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| 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 type of image classification tasks can this dataset be used for?
- The restaurant dietary health culture wall image classification dataset can be used for image classification tasks related to healthy eating culture within a restaurant.
- Which healthcare sectors can benefit from this dataset?
- This dataset can assist in advancing research and applications in fields like nutrition, public health advocacy, and healthy eating guidance.
- How can this dataset help restaurants improve dietary health promotion?
- By analyzing and classifying images on culture walls, restaurants can more effectively display healthy eating information, enhancing customer health awareness and brand image.
- What are the practical applications of this dataset in the healthcare industry?
- This dataset aids the healthcare industry in promoting and adopting healthy eating habits through visual cultural campaigns.
- Why choose images as the data modality?
- Images, as intuitive information carriers, can more effectively convey healthy cultural concepts and capture consumer attention.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Restaurant Dietary Health Culture Wall Image Classification Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/830f014d97b0b9b4d098bc637075745a?dataset_task_cate_id=7},
urldate={2026-02-04},
keywords={restaurant health culture, dietary health image classification, health promotion dataset},
version={1.0}
}Using this in research? Please cite us.
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