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Segformer B2 Cloth Parse 9

Developed by yolo12138
A garment segmentation model based on the SegFormer architecture, fine-tuned on the cloth_parsing_mix dataset for precise identification and segmentation of different clothing parts.
Downloads 156
Release Time : 12/15/2023

Model Overview

This model is specifically designed for garment image segmentation tasks, capable of identifying and segmenting various components of clothing such as sleeves, pant legs, collars, etc. Suitable for fashion analysis, virtual try-on, and similar scenarios.

Model Features

High-Precision Garment Segmentation
Achieves high accuracy on multiple garment parts, particularly excelling in major components like upper body, pant legs, and sleeves.
Multi-Part Recognition
Capable of identifying and segmenting up to 11 different garment parts, including detailed components like inner/outer collars and left/right sleeves.
Efficient Training
Uses relatively small batch sizes (12) and moderate learning rates (1e-05) for efficient training, achieving good results in just 5 epochs.

Model Capabilities

Garment Image Segmentation
Multi-Part Recognition
Pixel-Level Classification

Use Cases

Fashion Technology
Virtual Try-On Systems
Used for virtual try-on features in online shopping platforms, precisely segmenting garment parts to achieve realistic fitting effects.
Fashion Design Analysis
Assists designers in analyzing garment structure and component proportions to optimize design solutions.
E-Commerce
Automatic Product Image Tagging
Automatically adds part labels to garment product images on e-commerce platforms, improving search and recommendation accuracy.
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