Rice Cooker Shell Color Difference Recognition Dataset
#Image Classification #Anomaly Detection #Quality Control #Product Inspection #Manufacturing
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-04-16
AI Analysis & Value Prop
The current industrial sector faces significant challenges in maintaining consistent product quality, particularly in color matching for manufactured goods. Existing solutions often fall short due to manual inspection limitations and subjective assessments, leading to undetected color discrepancies that affect customer satisfaction and brand integrity. This dataset aims to address the need for an automated system capable of detecting color differences in rice cooker shells, facilitating more reliable quality assurance processes. The dataset comprises images captured in a controlled environment using high-resolution cameras, ensuring clarity and detail. Data collection was performed in a lighting-consistent setting to minimize external variations. Quality control measures included multi-round annotations by trained inspectors and consistency checks to enhance reliability. Images are organized in a structured format for easy access and processing, stored in JPG format to balance quality and size.
Dataset Insights
Sample Examples
52baabe1**.png|1534*1601|1.69 MB
eec2111b**.png|1534*2408|2.84 MB
9ba64ed7**.png|1534*1827|1.75 MB
d1e71fbc**.png|1534*1585|1.53 MB
e9837a98**.jpg|1534*2560|279.77 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| primary_color_code | string | The primary color code of the rice cooker shell, usually in RGB, CMYK, or HEX values. |
| defect_area_percentage | float | The percentage of the rice cooker shell's total area that is occupied by color inconsistency. |
| color_distribution_uniformity | float | Reflects the uniformity of color distribution on the surface of the rice cooker. |
| reference_color_code | string | The standard color code used for comparison. |
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
- What problem can this dataset help solve?
- This electric rice cooker shell color difference detection dataset can be used to detect whether there are color inconsistencies in the same batch of rice cooker products.
- How does the color difference in rice cooker shells affect product quality?
- Color differences in rice cooker shells can affect the appearance consistency of the product, potentially impacting customer purchasing decisions and brand image.
- What are the applications of this type of dataset in industry?
- In industry, such datasets are typically used for quality control to ensure that product specifications meet standards, thus reducing defect rates and costs.
- How can this dataset be used to train machine learning models?
- The images in the dataset can be used to train machine learning models to automate the detection of color differences, improving detection efficiency and accuracy.
- What type of machine learning task does this dataset support?
- This dataset supports object detection tasks, which can be used to train models to identify and classify different color difference scenarios.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Rice Cooker Shell Color Difference Recognition Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/b8e657c3174b818a4fd2aa38c695de11?dataset_scene_id=2},
urldate={2025-08-28},
keywords={Rice Cooker Dataset,Color Difference Recognition,Industrial Quality Control,Image Dataset for Inspection},
version={1.0}
}Using this in research? Please cite us.
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