Mineral Water Product Occlusion Image Dataset
#Object Detection #Image Segmentation #Product Identification #Image Recognition #E-commerce Applications
- Records
- 10000 records
- Size
- 3.0G
- Format
- JPG/PNG/JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-05
AI Analysis & Value Prop
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.
Overview
Dataset Insights
Preview
Sample Examples
71df8776**.png|1821*1500|3.47 MB
3150000d**.png|1481*1500|1.50 MB
ab3a8dbc**.png|937*1280|396.57 KB
4463c5ab**.png|1155*1500|1.27 MB
Schema
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. |
Trust
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
FAQ
Frequently Asked Questions
- What is the Mineral Water Product Occlusion Recognition Image Dataset?
- The Mineral Water Product Occlusion Recognition Image Dataset is an image set used to improve the recognition accuracy of mineral water products, focusing on the detection of items with transparent packaging.
- Which industry field is the Mineral Water Product Occlusion Recognition Image Dataset suitable for?
- This dataset is suitable for the retail industry, especially for the recognition and detection of products with transparent packaging.
- What type of data does the Mineral Water Product Occlusion Recognition Image Dataset contain?
- The Mineral Water Product Occlusion Recognition Image Dataset contains object detection type data focusing on object recognition within image data.
- How can the Mineral Water Product Occlusion Recognition Image Dataset be used for product recognition?
- The dataset can be used to train object detection models to improve the recognition accuracy of mineral water products with transparent packaging.
- What are the advantages of the Mineral Water Product Occlusion Recognition Image Dataset?
- The dataset has advantages of high quality and diversity, which can improve recognition performance for mineral water products with transparent packaging.
Can't find the data you need?
Post a request and let data providers reach out to you.
Get this Dataset
High-quality data, ready for your next AI breakthrough.
Cite this Work
@dataset{Mobiusi2025,
title={Mineral Water Product Occlusion Image Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/f1122456ccb0956e88541196d2b5ff34?dataset_scene_id=9},
urldate={2025-08-28},
keywords={mineral water dataset,image occlusion detection,retail e-commerce images,product recognition dataset},
version={1.0}
}Using this in research? Please cite us.
placeholder
placeholder
placeholder
placeholder
placeholder
placeholder
placeholder