Jacket Occlusion Image Dataset

#object detection #image segmentation #fashion recognition #e-commerce analysis #computer vision
Records
15000 records
Size
1.5G
Format
JPG/PNG/JSON
Provider
MOBIUSI INCMOBIUSI INC
Updated
v1.02026-11-04
AI Analysis & Value Prop
The retail e-commerce industry faces significant challenges in accurately recognizing and classifying apparel items when they are occluded by jackets or other outerwear. Existing solutions often struggle with occlusion detection, leading to misclassification and poor user experience. This dataset aims to address specific technical issues related to occlusion in images, enabling better object detection algorithms and improving the accuracy of automated systems in recognizing partially hidden clothing items. The dataset is collected using high-resolution cameras in various retail environments, capturing images with diverse lighting and angles. A rigorous quality control process is employed, which includes multiple rounds of annotations, consistency checks among different annotators, and expert reviews to ensure high annotation precision. The images are stored in JPG format and organized in a structured directory based on occlusion types and categories.

Overview

Dataset Insights

Preview

Sample Examples

603f99a9**.jpg|1280*1706|472.46 KB

8cc5827f**.jpg|1280*1396|372.16 KB

8baa6f32**.jpg|1280*1598|224.32 KB

3ac3b4ac**.jpg|1280*1180|543.20 KB

b6d778e7**.jpg|1280*1255|349.79 KB

Schema

Technical Specifications

file_namestringFile name
qualitystringResolution
occlusion_levelintegerIndicates the degree of occlusion, where 0 is no occlusion, 1 is partial occlusion, and 2 is severe occlusion.
clothing_typestringThe specific type of clothing, such as shirt, pants, or skirt.
pose_angledoubleThe angle of the clothing in the image, measured in degrees, describing the rotation relative to the camera.
background_complexityintegerThe complexity of the background, with 0 being a simple background, 1 being a moderately complex background, and 2 being a complex background.

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 Occlusion Detection Image Dataset for Garments?
The Occlusion Detection Image Dataset for Garments contains images of clothing in various occlusion scenarios, primarily aimed at improving image recognition technology in the e-commerce retail sector.
What types of images are included in this dataset?
This dataset includes images of garments under various occlusion scenarios, suitable for studying object detection technology.
How does this dataset support the retail e-commerce sector?
This dataset aids in improving image recognition algorithms by providing garment images in real occlusion scenarios, which enhances product presentation and user experience in the retail e-commerce sector.
What are the applications of object detection datasets in the retail industry?
Object detection datasets are used in the retail industry for various applications such as automated product recognition, inventory management, and virtual try-on scenarios.
Why is occlusion detection important for image recognition technology?
Occlusion detection is crucial for enhancing the accuracy of image recognition systems when handling complex scenes, thereby increasing the overall robustness and reliability of the system.

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Cite this Work

@dataset{Mobiusi2025,
  title={Jacket Occlusion Image Dataset},
  author={MOBIUSI INC},
  year={2025},
  url={https://www.mobiusi.com/datasets/50577b0ab2ee2758f2d50a710017d48b?dataset_scene_id=9},
  urldate={2025-08-28},
  keywords={occlusion dataset,retail image dataset,fashion image recognition,e-commerce image analysis},
  version={1.0}
}

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