Breakfast Cereal Product Occlusion Image Dataset
#object detection #image classification #product recognition #image segmentation #computer vision
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-17
AI Analysis & Value Prop
The retail e-commerce industry faces significant challenges in accurately recognizing products that are partially occluded on shelves. Current solutions primarily focus on fully visible items, leading to a lack of robust models for occluded scenarios. The Breakfast Cereal Product Occlusion Image Dataset aims to bridge this gap by providing a diverse set of occluded images of popular brands like Kellogg's, Quaker, and Nestlé. This dataset features images collected under various conditions, using high-resolution cameras in retail environments that simulate real-world conditions. Quality control measures include multiple rounds of annotations, consistency checks, and expert reviews to ensure data accuracy. The dataset is organized in JPG format, with structured folders categorized by product types and occlusion levels.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| cereal_type | string | The type of breakfast cereal products identified based on the image content. |
| occlusion_level | string | The degree of obstruction of the cereal product in the image, such as unobstructed, partially obstructed, severely obstructed. |
| brand_name | string | The brand name of the breakfast cereal product. |
| package_color | string | The main color of the breakfast cereal product packaging. |
| package_shape | string | The shape of the packaging of the identified breakfast cereal product. |
| position_in_frame | string | The position description of the breakfast cereal product in the image, such as centered, top left corner, etc. |
| visibility_percentage | float | The percentage of the breakfast cereal product visible in the image. |
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 is the Breakfast Cereal Product Occlusion Recognition Image Dataset?
- The Breakfast Cereal Product Occlusion Recognition Image Dataset is an image dataset designed to improve product recognition accuracy, containing images of breakfast cereal products with varying degrees of occlusion.
- In which industry is the Breakfast Cereal Product Occlusion Recognition Image Dataset most widely applied?
- The Breakfast Cereal Product Occlusion Recognition Image Dataset is most widely applied in the retail industry, primarily for improving product recognition accuracy.
- How to use the Breakfast Cereal Product Occlusion Recognition Image Dataset for object detection?
- You can use object detection algorithms to train and test on the Breakfast Cereal Product Occlusion Recognition Image Dataset to improve the recognition accuracy of products in images.
- What contribution does the Breakfast Cereal Product Occlusion Recognition Image Dataset make to machine learning research?
- This dataset provides practical training data for image object detection tasks in machine learning, with significant contributions to addressing partially occluded object recognition problems.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Breakfast Cereal Product Occlusion Image Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/a977409b27479c98e1ba116dac9b3ff1?dataset_scene_id=9},
urldate={2025-08-28},
keywords={breakfast cereal dataset,occlusion image dataset,retail e-commerce images,computer vision dataset},
version={1.0}
}Using this in research? Please cite us.
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