Condiment Occlusion Image Dataset
#Object Detection #Image Classification #Product Recognition #Visual Search #Inventory Management
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-04-17
AI Analysis & Value Prop
The retail e-commerce industry is facing significant challenges in accurately recognizing products due to occlusions that frequently occur in images. Existing solutions often fail to handle partial visibility effectively, leading to misclassifications and increasing operational costs. This dataset addresses the need for high-quality training data specifically focused on images of condiment packaging (like Heinz, Hunt's, and Hellmann’s) that are partially obstructed. The data is collected using high-resolution cameras in controlled lighting conditions to ensure clarity despite occlusions. Quality control measures include multi-round annotations, consistency checks, and expert reviews to maintain high standards. The dataset is organized in JPG format and stored in a structured manner for easy access and processing.
Dataset Insights
Sample Examples
90514454**.jpg|1224*1632|153.75 KB
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acda93b9**.jpg|480*640|32.28 KB
a577c674**.jpg|1061*1884|163.17 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| bounding_box | json | The coordinates of the bounding box of the object in the image. |
| object_class | string | The identified category of the condiment. |
| occlusion_level | int | The degree to which the object is occluded, in a percentage from 0 to 100. |
| image_brightness | float | Overall brightness value of the image. |
| image_contrast | float | Overall contrast value of the image. |
| image_sharpness | float | The sharpness of the image. |
| image_saturation | float | The color saturation of the image. |
| object_count | int | The number of condiments identified in the image by text recognition. |
| background_complexity | int | The complexity of the background, used to assess the recognition difficulty. |
| angle_of_view | float | The viewing angle at the time of shooting. |
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 |
Frequently Asked Questions
- What is the main purpose of the Sauce Product Occlusion Recognition Image Dataset?
- The main purpose of the sauce product occlusion recognition image dataset is to improve product recognition accuracy and address occlusion challenges in the e-commerce industry.
- Why is sauce product detection important in the retail industry?
- In the retail industry, sauce product detection is crucial for boosting product recognition accuracy, optimizing inventory management, and enhancing customer experience.
- How does this dataset help solve occlusion problems in e-commerce?
- This dataset aids in solving occlusion problems by providing images with various occlusion scenarios, offering samples for training and testing to improve model performance in occluded scenes.
- What is the role of Object Detection datasets in machine learning?
- Object Detection datasets are used to train machine learning models to recognize, locate, and label objects within images.
- What potential business values can be derived from using the sauce product occlusion recognition dataset?
- Using the dataset can optimize automated product recognition systems, thereby improving e-commerce platform efficiency, reducing human errors, and enhancing customer satisfaction.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Condiment Occlusion Image Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/de583411ac44f45ec28611a0dccb0815?dataset_scene_id=9},
urldate={2025-08-28},
keywords={condiment dataset,image recognition,occlusion dataset,retail e-commerce,object detection},
version={1.0}
}Using this in research? Please cite us.
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