Datasets:
file_name stringclasses 4
values | quality stringclasses 4
values | occlusion_degree stringclasses 4
values | label_presence stringclasses 2
values | bottle_orientation stringclasses 2
values | cap_presence stringclasses 3
values | lighting_condition stringclasses 2
values | reflection_intensity stringclasses 2
values | material_type stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|
3150000d661caa30af9cb0b930fcffe0.png | 1481*1500 | 0% | Visible | Vertical | With Cap | Bright | Moderate | Plastic |
4463c5abc9d61aac7cd49d77107d85dc.png | 1155*1500 | Not Noticeably Occluded | Visible | Vertical | With Cap | Bright | Moderate | Plastic |
71df8776d53a6d9775d57fe29e19a008.png | 1821*1500 | partially occluded | clearly visible | horizontal | with cap | bright | medium | plastic |
ab3a8dbc29b2c8d231dfe2a05647de6b.png | 937*1280 | Partially Occluded | Visible | Vertical | Without Cap | Bright | Moderate | Plastic |
Mineral Water Product Occlusion Image Dataset
The retail e-commerce industry is evolving rapidly, yet it faces significant challenges in accurately identifying products in images, particularly those with occluded packaging, such as Evian and Fiji. Existing solutions often struggle with occlusion detection and fail to provide sufficient training data for machine learning models. This dataset aims to address these technical challenges by providing a comprehensive collection of images featuring mineral water products in various states of occlusion. The images were collected using high-resolution cameras in controlled environments, ensuring clarity and consistency. Quality control measures included multiple rounds of annotation, consistency checks, and expert reviews to ensure data integrity. The dataset is stored in JPG format, organized systematically by product type and occlusion level.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| occlusion_degree | float | The proportion of mineral water bottles that are occluded in the image. |
| label_presence | boolean | Whether the label on the mineral water bottle is visible. |
| bottle_orientation | string | The orientation of the mineral water bottle in the image, such as 'horizontal', 'vertical', etc. |
| cap_presence | boolean | Whether the mineral water bottle in the image has a cap. |
| lighting_condition | string | The lighting condition in the image, such as 'bright', 'dim', etc. |
| reflection_intensity | float | The intensity of reflection caused by the transparency of the mineral water bottle in the image. |
| material_type | string | The material of the mineral water bottle, such as 'plastic', 'glass', etc. |
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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