Clothes Classification Dataset

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Project Overview:

Objective

In our ongoing commitment to advancing machine learning applications, we have successfully developed a robust dataset specifically for classifying various clothing items. This Clothes Segmentation plays a crucial role in enhancing e-commerce search engines, virtual dressing rooms, and fashion trend analytics.

Scope

Our team has meticulously designed a comprehensive repository of clothing images. This Clothes Segmentation is unique for its detailed segmentation and labeling, covering aspects such as type, material, design, and brand.

Sources

E-commerce Platforms: Collaborate with online clothing retailers to obtain high-quality images of their product range.
Fashion Magazines: Extract images from both digital and printed fashion publications to capture the latest trends.
Public Submissions: Create a portal where individuals can upload images of their clothing items, ensuring a wide array of styles and patterns.

Data Collection Metrics

Total Images Collected: 250,000
E-commerce Contributions: 150,000
Fashion Magazine Extractions: 70,000
Public Submissions: 30,000

Annotation Process

Stages

Type Classification: Each clothing item is meticulously labeled by its type.
Material Annotation: We identify and tag the primary materials.
Design Pattern Annotation: Our team describes the design and pattern details.
Brand Information: Where available, we include brand details.

Annotation Metrics

Images with Type Labels: 250,000
Material Annotations: 230,000
Design Pattern Tags: 250,000
Brand Information Tags: 180,000

Quality Assurance

Stages

Classification Accuracy: We engage fashion industry experts for dataset review.
Privacy Protocols: We take utmost care in ensuring privacy in public submissions.

QA Metrics

Classification Review Cases: 25,000
Material Authentication Reviews: 23,000
Privacy Audits: 30,000

Conclusion

Our Clothes Segmentation Initiative is a testament to our capability in gathering and annotating diverse datasets. This project not only showcases our proficiency in handling large-scale image datasets but also underlines our contribution to the evolving landscape of fashion technology. We are proud to offer this dataset, a rich compilation of fashion elements, to propel forward the innovations in various tech applications.