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Automatic segmentation, classification, and color transformation of saree components using deep learning. Published in IEEE Access (Q1). Provides designers and retailers with a fast, accurate pipeline for textile image analysis and instant saree color/pattern changes.

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chandan2300/Automatic-Classification-and-Color-Changing-of-Saree-Components

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Automatic Classification and Color Changing of Saree Components

This repository implements our IEEE Access (Q1 Journal) research:
Automatic Classification and Color Changing of Saree Components Using Deep Learning Techniques.

We present a deep learning pipeline for saree component classification. Our system enables retailers and designers to automatically classify saree parts and visualize color/pattern changes without manual editing. The proposed approach achieves 93.01% classification accuracy, significantly outperforming prior methods for textile image analysis and boosting the speed of design workflows.

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  • Highlights:
    • Automatic segmentation & classification of saree components
    • SaaS-ready pipeline for color & pattern transformation
    • State-of-the-art accuracy using Mask R-CNN & VGG-16
    • Designed for textile retailers, designers, and e-commerce applications

For further details, see our IEEE Access paper (2024).

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Automatic segmentation, classification, and color transformation of saree components using deep learning. Published in IEEE Access (Q1). Provides designers and retailers with a fast, accurate pipeline for textile image analysis and instant saree color/pattern changes.

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