Tableware Placement Structure Image Dataset

Name: Tableware Placement Structure Image Dataset
Creator: MOBIUSI INC
Published: 2026-02-04

#image classification #object detection #pattern recognition #robot learning #dining management #automated placement #robot navigation #household services

500 records
1.6G
JPG
CC-BY-NC-SA 4.0
MOBIUSI INC

Updated:2026-02-04

AI Analysis & Value Prop

With the rapid development of the catering industry, the efficiency and standardization of tableware placement have become a focus of the industry, and existing solutions often rely on manual inspection, which is inefficient and prone to errors. This dataset aims to provide high-quality training data for automated tableware placement systems to address the limitations and inconsistencies of manual operations. Data collection is done by taking tens of thousands of table setting photos under different lighting and environmental conditions, using high-resolution cameras. Strict quality control is employed, including multiple rounds of annotation and consistency checks, reviewed by an expert team with dining industry experience. The annotation team consists of 10 people with extensive experience in image processing and annotation. Data has been cropped, enhanced, and normalized to improve model training effectiveness, ultimately stored in a structured JPG format, systematically organized for easy access.

Dataset Insights

Sample Examples

e76918db**.jpg|4096*3072|689.25 KB

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
image_orientation	string	The orientation in which the image was captured, such as landscape or portrait.
light_conditions	string	The lighting conditions present when the image was taken, such as natural light or indoor lighting.
tableware_type	string	The types of tableware visible in the image, such as plates, knives, forks, or spoons.
placement_pattern	string	The manner in which tableware is arranged in the image, such as symmetrical or asymmetrical placement.
background_clarity	string	The discernibility level of the background in the image, such as blurred or clear.
surface_texture	string	The texture features of the table or background surface, such as wood grain or marble.
color_palette	string	The combination of colors between tableware and the background in the image.
sharpness	string	The clarity and detail representation levels of the image.
reflection_level	string	The intensity of light reflection on tableware or surfaces visible in the image.

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 is the Cutlery Arrangement Image Dataset?: The Cutlery Arrangement Image Dataset is a collection of images showcasing various arrangements of cutlery, aimed at enhancing the intelligence of catering management and automated arrangement systems.

What are the application scenarios for the Cutlery Arrangement Image Dataset?: This dataset can be used in scenarios such as intelligent restaurant management systems, household cutlery arrangement robots, and automated cutlery sorting systems.

How can restaurants use the Cutlery Arrangement Image Dataset?: Restaurants can use this dataset to train machine learning models for intelligent cutlery management and automatic arrangement, improving efficiency and customer satisfaction.

How does the Cutlery Arrangement Image Dataset help automation systems?: This dataset provides rich visual information for automated cutlery arrangement systems, aiding in precise identification and arrangement of cutlery, thus enhancing the level of automation.

What advantages does the Cutlery Arrangement Image Dataset have over traditional cutlery arranging methods?: Compared to traditional manual cutlery arranging methods, this dataset supports the development of smarter algorithms that enable more efficient, standardized, and consistent cutlery arrangements.

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Cite this Work

@dataset{Mobiusi2026,
  title={Tableware Placement Structure Image Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/b623366d737981cbbbe4859393742e8b},
  urldate={2026-02-04},
  keywords={tableware image dataset, automated placement, dining management image, AI tableware arrangement},
  version={1.0}
}

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