Datasets:
file_name stringclasses 4
values | quality stringclasses 4
values | shoe_type stringclasses 2
values | shoe_color stringclasses 4
values | shoe_brand stringclasses 3
values | obstruction_level stringclasses 2
values | background_scene stringclasses 1
value | image_quality stringclasses 2
values | lighting_condition stringclasses 1
value | image_orientation stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|
510cdcc0aad16499fe02685de872b0f8.jpg | 1224*1632 | Low | White and Orange | northwave | No Obstruction | Indoor Environment | HD | Bright | Front |
63c30f1b88b300506d220170bf1bf286.jpg | 1198*695 | Low-top | Gray and Black, Pink Details | Unrecognizable | Unobstructed | Indoor Environment | High Definition | Bright | Upright |
77f5bced1daaae705bd8e791426e8928.jpg | 762*695 | Low | Gray | Unidentifiable | No Obstruction | Indoor Environment | HD | Bright | Front |
e46ae4369cb924b11356f17743bbdc10.jpg | 788*1050 | Low | Black and White | Unidentifiable | No Obstruction | Indoor Environment | HD | Bright | Front |
Mountain Shoe Occlusion Image Dataset
The retail e-commerce sector is facing significant challenges due to the increasing complexity of product recognition in images affected by various occlusions, such as mud cover, grass camouflage, and motion blur. Existing solutions often struggle with accuracy and robustness, particularly under these challenging conditions. This dataset aims to address these specific technical issues by providing a rich source of images that depict mountain shoes under various occlusion scenarios, which are crucial for training more effective recognition models. The dataset has been compiled using high-resolution photography techniques in diverse outdoor environments, ensuring a variety of occlusion types. Quality control measures include multiple rounds of annotation, consistency checks among annotators, and expert reviews, ensuring high-quality labeled data. The data is stored in JPG format, organized in a structured manner for easy access and use.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| shoe_type | string | Identify the type of hiking shoes appearing in the image, such as low-cut, mid-cut, high-cut. |
| shoe_color | string | Identify the color of the hiking shoes in the image. |
| shoe_brand | string | Identify the brand of the hiking shoes in the image, if the trademark is visible. |
| obstruction_level | string | Identify the degree to which the hiking shoe in the image is obstructed, such as: no obstruction, partially obstructed, completely obstructed. |
| background_scene | string | Identify the background scene in which the hiking shoe is photographed, such as: outdoor mountainous area, indoor environment. |
| image_quality | string | The quality grade of the image, such as: high definition, blurry, low definition. |
| lighting_condition | string | Lighting conditions in the image, such as: bright, dim, backlit. |
| image_orientation | string | Identify the rotation direction of the image, such as: upright, 90 degrees left, 90 degrees right, inverted. |
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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