Human And Accessories Segmentation Dataset
Project Overview:
Objective
Creating a versatile dataset for the segmentation of human figures and their accessories is crucial. This dataset plays a vital role in applications such as fashion tech, augmented reality, and retail analytics. To ensure its effectiveness, however, the dataset should encompass a wide range of scenarios.
Scope
Our project focused on collecting a wide range of images that show people in various positions and settings, as well as with different accessories.
Sources
- Models in studio settings, adorned with a plethora of accessories, showcase a diverse array of poses and expressions
- Everyday fashion and accessories are candidly captured in street photography.
- Diverse accessories and attire for special events like weddings and ceremonies.
- Accessories specific to various sports, captured during sports events.
- In professional settings, it’s important to showcase work-related accessories. Moreover, these accessories not only enhance productivity but also reflect personal style. Furthermore, they often include items like sleek laptops, stylish notebooks, and ergonomic desk chairs. Additionally, having the right accessories can create a positive impression during meetings and presentations.
Data Collection Metrics
- Studio Shots: 105,000
- Candid Street Shots: 95,000
- Special Event Photographs: 75,000
- Sports Events: 65,000
Annotation Process
Stages
- Initial raw data cleaning to eliminate any unsuitable images.
- Precise segmentation of the human figures.
- Additionally, Detailed segmentation of various accessories like hats, sunglasses, and more.
- Finally, A thorough review of annotations for accuracy and completeness.
Annotation Metrics
- Total Annotations: 2,400,000 (6 segments per image on average considering a human plus an average of 5 accessories)
- Human Segmentation: 400,000
- Accessory Segmentations: 2,000,000
- Annotations Reviewed: 240,000
Quality Assurance
Stages
Expert Review: A team of fashion and segmentation experts thoroughly reviewed some of the annotations. Additionally, their feedback ensured high-quality results.
Automated Checks: Moreover, algorithms flagged potential errors or overlaps in segmentation, which was crucial for maintaining accuracy.
Inter-annotator Agreement: Furthermore, several annotators were given the same images to ensure segmentation was consistent across the team.
QA Metrics
- Annotations Reviewed by Experts: 240,000 (10% of total annotations)
- Inconsistencies Identified and Rectified: 24,000 (1% of total annotations)
Conclusion
Quality Data Creation
Guaranteed TAT
ISO 9001:2015, ISO/IEC 27001:2013 Certified
HIPAA Compliance
GDPR Compliance
Compliance and Security
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