Home Robot Image Classification Dataset
#image classification #target detection #floor cleaning #patrolling #companion robot recognition
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
With the rapid development of smart devices, home robots are playing an increasingly important role in daily life. However, existing image recognition technology still lacks accuracy in complex environments, leading to misjudgments when robots recognize obstacles and execute tasks. This dataset aims to address the image classification issues of home robots in different scenes and provides high-quality labeled data for algorithm training. Data collection involved real shooting in various home scenarios, using high-resolution cameras to capture images under different lighting and environmental conditions. To ensure data quality, quality control measures such as multiple rounds of labeling, consistency checks, and expert reviews were used. The final storage is in JPG format, organized in a folder structure for easy subsequent use.
Dataset Insights
Sample Examples
b47b1d22**.jpg|1080*1920|393.35 KB
9e6a8f8f**.jpg|1080*1440|331.73 KB
e45d3010**.jpg|1080*1440|133.69 KB
e6ff42cf**.jpg|1080*1440|445.26 KB
6457b6ee**.jpg|1080*1371|163.80 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| object_count | int | The number of objects identified in the image. |
| main_object_type | string | The type of the main object in the image, such as chair, table, etc. |
| main_object_color | string | The color of the main object in the image. |
| lighting_condition | string | The lighting condition during image capture, such as bright, dim, etc. |
| image_quality | string | The clarity and noise level of the image, such as high quality, medium quality, etc. |
| background_complexity | string | The complexity of the image background, such as simple, complex, etc. |
| object_position | string | The position of the main object in the image, such as upper left, center, etc. |
| image_orientation | string | The orientation of the image, such as landscape, portrait, etc. |
| texture_pattern | string | The texture or pattern characteristics of the main object's surface in the image. |
| object_distance | float | The estimated distance between the camera and the main object in the image, in meters. |
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 applications is the Household Robot Image Classification Dataset suitable for?
- This dataset is suitable for scenarios where household robots need to perform image recognition and classification, such as home security surveillance, smart home management, and automated navigation.
- What are the benefits of using the Household Robot Image Classification Dataset?
- Using this dataset can improve the accuracy of image recognition in household robots, thereby enhancing task execution efficiency and user satisfaction.
- How does the Household Robot Image Classification Dataset differ from traditional image classification datasets?
- This dataset focuses on images related to household robots, considering special scenes and objects in home environments, thus offering more targeted training data.
- What are the main features of the Household Robot Image Classification Dataset?
- The dataset provides high-quality images covering various home environments, supporting the training and application of machine learning models under different conditions.
- How to effectively utilize the Household Robot Image Classification Dataset for training?
- Implement data augmentation techniques and adjust model parameters to improve training outcomes, and employ cross-scenario validation to ensure model generalization.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Home Robot Image Classification Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/62aea17051dec74163983994835c1519?dataset_scene_id=6},
urldate={2025-09-15},
keywords={home robot, image classification dataset, machine vision, smart devices, robot recognition},
version={1.0}
}Using this in research? Please cite us.
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