Datasets:
file_name stringclasses 5 values | quality stringclasses 3 values | object_count stringclasses 4 values | main_object_type stringclasses 3 values | main_object_color stringclasses 3 values | lighting_condition stringclasses 2 values | image_quality stringclasses 2 values | background_complexity stringclasses 3 values | object_position stringclasses 5 values | image_orientation stringclasses 2 values | texture_pattern stringclasses 3 values | object_distance stringclasses 3 values |
|---|---|---|---|---|---|---|---|---|---|---|---|
6457b6ee78d8f7ce03895764c9e6e8d3.jpg | 1080*1371 | 2 | Robot Vacuum | White | Bright | High Quality | Simple | Right | Portrait | Smooth | About 1 meter |
9e6a8f8f1436bf9e349b11895054065f.jpg | 1080*1440 | multiple | robot dog | black and white | bright | high quality | complex | centered | portrait | various patterns | about 1 meter |
b47b1d22d9db7e63db5e436e3662f360.jpg | 1080*1920 | 5 | robot vacuum | white | bright | high quality | simple | center | portrait | smooth | about 1 meter |
e45d3010939e9e2d39f9bb336891a002.jpg | 1080*1440 | 2 | Robot Vacuum | White | Bright | High Quality | Simple | Bottom Left | Portrait | Smooth | About 2 meters |
e6ff42cf5ac238d3d02a700415201b5c.jpg | 1080*1440 | 1 | Robot Vacuum | White | Bright | High Quality | Simple | Center | Portrait | Smooth | About 1 meter |
Home Robot Image Classification Dataset
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.
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 |
Source & Contact
If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com
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