Clothes Pattern Classification Dataset

Project Overview:

Objective

Our latest venture involved developing an extensive Clothes Pattern Classification Dataset. This dataset is a testament to our proficiency in handling large-scale data projects, crucial for advancing e-commerce filtering, virtual wardrobe arrangement, and AI-driven fashion design models.

Scope

Our team successfully amassed over 45,000 images featuring a wide range of clothing items, each with distinctive patterns such as stripes, polka dots, checks, and floral designs.

Sources

Engaged in collaborative initiatives with electronics manufacturing units for access to images of defective boards, resulting in a carefully collected and successfully curated assortment of visuals.
Established partnerships with carpentry workshops and lumber mills for samples of damaged wooden boards, contributing to a thoughtfully collected and well-curated dataset.
Crowdsourced submissions from DIY enthusiasts and hobbyists have been meticulously collected and successfully curated for a diverse compilation.
Conducted controlled photography sessions for capturing specific types of damage and wear, resulting in a successfully collected and professionally curated set of images.

Data Collection Metrics

Total Images Collected: 45,000
Tops & Shirts: 13,000
Dresses: 11,000
Pants & Skirts: 11,000
Traditional/Ethnic Wear: 10,000

Annotation Process

Stages

Image Pre-processing: Standardizing image resolution, lighting, and orientation.
Detailed Pattern Annotation: Assigning specific pattern labels like “stripes,” “floral,” and “geometric.”
Rigorous Validation: Fashion industry experts verified each annotation for pattern accuracy.

Annotation Metrics

Total Pattern Annotations: 45,000
Average Annotation Time per Image: 3 minutes

Quality Assurance

Automated Verification: Utilize early-stage pattern classification models to contrast their results with human annotations, highlighting potential discrepancies.

Peer Review: A select portion of annotated images is re-evaluated by different experts to corroborate consistency.

Inter-annotator Agreement: Several images, especially those with intricate patterns, are inspected by multiple annotators to ensure a consensus on pattern classification.

QA Metrics:

Patterns Validated through Automated Checks: 20,000 (50% of total images)
Peer-reviewed Annotations: 12,000 (30% of total images)
Inconsistencies Detected and Rectified: 600 (1.5% of total images)

Conclusion

Our Clothes Pattern Classification Dataset stands as a pioneering resource, driving automation in fashion-oriented AI applications. With our precise pattern classifications covering a diverse range of clothing, we enable enhanced search algorithms, accurate inventory management, and tailored style recommendations, benefiting both consumers and retailers.