# SegCycle-SPADE: An end-to-end framework for semantic segmentation-based automated extraction and artistic reconstruction of traditional craft patterns using conditional GAN

## TL;DR

This paper introduces SegCycle-SPADE, a new model that accurately extracts and reconstructs traditional craft patterns using advanced AI techniques.

## Contribution

SegCycle-SPADE combines SegFormer, CycleGAN, and SPADE for improved pattern segmentation and generation in traditional crafts.

## Key findings

- SegCycle-SPADE achieves 88.6% PA, 78.1% mIoU, and 73.8% Boundary F1 in segmentation.
- The model outperforms others in PSNR (27.8 dB), SSIM (0.89), and FID (34.2) for pattern generation.
- It offers a robust solution for digital preservation and innovation of traditional handicraft patterns.

## Abstract

This paper addresses the challenge of traditional handicraft pattern extraction and generation, focusing on accurate segmentation and high-quality pattern reconstruction. We propose the SegCycle-SPADE model, combining SegFormer for semantic segmentation, CycleGAN for pattern generation, and SPADE for style transfer, to achieve a balance between segmentation accuracy, generation quality, and inference efficiency. Experiments on datasets such as Batik Nitik 960, Fashion-MNIST, and DeepFashion show that SegCycle-SPADE outperforms baseline models like U-Net and DeepLabV3+ with significant improvements in PA (88.6%), mIoU (78.1%), and Boundary F1 (73.8%). In terms of pattern generation, SegCycle-SPADE also achieves superior results in PSNR (27.8 dB), SSIM (0.89), and FID (34.2), outperforming Pix2Pix, Stable Diffusion, and other models. The model demonstrates its potential for the digital regeneration of traditional handicraft patterns, offering a robust solution for high-quality and efficient pattern generation, with substantial contributions to digital cultural heritage preservation and innovation.