Kitchen Tableware Placement Image Dataset

#object recognition #image classification #placement detection #scene understanding #smart home #machine vision #indoor navigation #home automation
Records
500 records
Size
1.6G
Format
JPG
Provider
MOBIUSI INCMOBIUSI INC
Updated
v1.02026-11-04
AI Analysis & Value Prop
The current smart home industry is developing rapidly, but challenges remain in object recognition and dynamic detection. Many existing solutions lack accuracy in recognition and understanding of complex placement scenarios. This dataset aims to improve the accuracy and efficiency of object recognition in home scenarios to meet the demand for high-precision data in smart homes. Data is collected by high-resolution cameras in various home environments, covering different times and lighting conditions. Quality control includes multiple rounds of manual annotation and expert review to ensure accuracy and consistency, carried out by a team of ten with computer vision backgrounds. Data preprocessing methods include image enhancement and normalization, stored in JPG format for easy retrieval and use.

Overview

Dataset Insights

Preview

Sample Examples

55cd6e0b**.jpg|1080*1407|380.48 KB

739e7585**.jpg|1080*1397|349.01 KB

41dce834**.jpg|1080*1404|409.32 KB

099e6f03**.jpg|1080*1401|299.52 KB

Schema

Technical Specifications

file_namestringFile name
qualitystringResolution
object_typestringThe category of kitchen items in the image, such as pots, bowls, chopsticks, etc.
object_colorstringThe primary color of the item identified in the image.
object_sizestringThe size or volume of the item identified in the image, such as large, medium, small.
position_in_kitchenstringThe specific placement of the item in the kitchen, such as on the counter, on the wall, in a drawer, etc.
object_orientationstringThe orientation of the item in the image, such as front, side, back, etc.
material_typestringThe material type of the item in the image, such as glass, metal, plastic, etc.
image_qualitystringThe quality level of the image, such as high-definition, blurry, overexposed, etc.
light_conditionstringThe lighting conditions during the image capture, such as bright, shadow, backlit, etc.
background_typestringThe background type of the image, such as monochromatic, cluttered, kitchen scene, etc.

Trust

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

FAQ

Frequently Asked Questions

What is the Kitchen Utensil Placement Image Dataset?
The Kitchen Utensil Placement Image Dataset includes high-quality images of kitchen items with corresponding text labels, supporting smart home applications.
How to use the Kitchen Utensil Placement Image Dataset?
You can use this dataset to train machine learning models to recognize and locate kitchen items, promoting the development of smart homes.
Which industries can benefit from the Kitchen Utensil Placement Image Dataset?
This dataset is suitable for any industry involved in kitchenware management or smart home system development.
What types of images are included in the Kitchen Utensil Placement Image Dataset?
The dataset includes high-quality images of various kitchen utensils and their placements.
Why is the Kitchen Utensil Placement Image Dataset helpful for smart home applications?
This dataset provides precise recognition and location abilities for kitchen items, significantly enhancing the functionality of smart home systems.

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Cite this Work

@dataset{Mobiusi2026,
  title={Kitchen Tableware Placement Image Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/65477e19b8f019c7d7746d6de878676c?cate=2},
  urldate={2026-02-04},
  keywords={kitchen item recognition, tableware placement detection, smart home image data, home item classification},
  version={1.0}
}

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