Backpack Occlusion Image Dataset
#image segmentation #object detection #classification #object recognition #image classification #e-commerce #computer vision
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-06-25
AI Analysis & Value Prop
In the current retail e-commerce sector, visual recognition systems face significant challenges in accurately identifying products due to occlusions, particularly in images where straps obscure logos or buckles. Existing datasets often lack diversity in occluded images, making them insufficient for training robust models. This dataset aims to address these limitations by providing a specialized collection of images featuring backpacks with varying levels of occlusion caused by shoulder straps. The dataset is collected using high-quality cameras in diverse environments, ensuring a variety of angles and lighting conditions. Quality control measures include multi-round annotations and expert reviews to ensure consistency and accuracy. The images are stored in JPG format, organized by occlusion level and category for easy access.
Dataset Insights
Sample Examples
21c33e4e**.jpg|1280*1920|347.12 KB
cf62c175**.jpg|1280*1706|243.27 KB
9e72b367**.jpg|1280*1706|529.75 KB
2caa2717**.jpg|1280*1706|539.23 KB
78f3b8cc**.jpg|1280*1220|430.40 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| backpack_presence | boolean | Whether a backpack is present in the image. |
| backpack_position | string | The approximate position where the backpack appears in the image, such as 'top left', 'bottom right', etc. |
| occlusion_level | string | The extent to which the backpack is obstructed in the image, such as 'partially obstructed' or 'fully obstructed'. |
| background_clutter | string | The level of complexity of the background, such as 'simple', 'medium', or 'complex'. |
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 this backpack occlusion recognition dataset?
- This dataset is mainly used for training and optimizing machine learning models to improve the ability to recognize and detect occluded backpacks in retail environments.
- Why were backpacks chosen as the object for detection?
- Backpacks are common items in the retail industry, and they are often occluded in displays, which makes detection challenging. Therefore, recognizing them has significant practical application value.
- How can the retail industry benefit from this dataset?
- The retail industry can use this dataset to train and improve visual detection systems, enhancing the accuracy of product recognition, thereby optimizing shelf management and customer service.
- Does this dataset support multiple machine learning frameworks?
- Yes, this dataset is designed to be compatible with multiple machine learning frameworks, making it widely applicable across different development environments.
- Under what circumstances are the images in this dataset particularly useful for model training?
- The images in this dataset are particularly useful when there is a need to enhance the model's ability to recognize occluded products in complex scenarios, such as in retail shelves or crowded shopping environments.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Backpack Occlusion Image Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/4f508c98db77a01956903dfa6e124311},
urldate={2025-08-28},
keywords={backpack dataset,image occlusion,e-commerce image dataset,computer vision,object detection},
version={1.0}
}Using this in research? Please cite us.
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