Segformer-finetuned-segments-cmp-facade Open-source Model - A Building Façade Segmentation Tool for Identifying 12 Types of Building Elements

Segformer Finetuned Segments Cmp Facade

Developed by Xpitfire

A building facade semantic segmentation model based on SegFormer architecture, capable of recognizing 12 types of architectural elements

Image Segmentation

Transformers

EnglishOpen Source License:MIT #Building facade segmentation #Zero-shot learning #Transformer architecture

Downloads 379

Release Time : 1/8/2023

Model Overview

This model employs a hierarchical Transformer encoder and a lightweight MLP decoder, specifically designed for pixel-level classification tasks of building front views. It can identify 12 types of architectural elements including facades, windows, doors, etc.

Model Features

Hierarchical Transformer Encoding

Position-encoding-free hierarchical feature extraction that effectively captures multi-scale architectural features

Lightweight MLP Decoder

Simplifies traditional complex decoder structures while maintaining high performance and reducing computational costs

Multi-element Recognition

Simultaneously identifies 12 types of architectural elements (facades, cornices, windows, etc.) and their spatial relationships

Model Capabilities

Building facade semantic segmentation

Pixel-level classification

Multi-scale feature extraction

Street view image analysis

Use Cases

Architectural Digitization

Historic Building Preservation

Automatically labels facade elements to assist restoration design

Accurately identifies decorative components and other detailed features

Urban Planning

Batch analysis of street architectural style composition

Statistical distribution of architectural elements across different areas

3D Reconstruction

Building Reverse Modeling

Generates preliminary 3D structures based on single front views

Provides accurate facade element segmentation masks

🚀 Semantic Segmentation for Building Facades

Semantic segmentation is the process of categorizing each pixel in an image, with broad applications in multiple fields. This project focuses on classifying building facades using advanced models.

🚀 Quick Start

Semantic segmentation involves classifying each pixel in an image into a corresponding category. It has numerous applications in fields like medical imaging, autonomous driving, and robotics. For the facade dataset, we aim to classify the front - view of buildings into 12 distinct classes: facade, molding, cornice, pillar, window, door, sill, blind, balcony, shop, deco, and background.

✨ Features

Advanced Model Usage: We utilize the SegFormer, a state - of - the - art model architecture for semantic segmentation introduced in 2021. It features a hierarchical Transformer encoder that doesn't rely on positional encodings and a simple multi - layer perceptron decoder.
Zero - Shot Approach: Yin et al.'s novel approach enables state - of - the - art performance in a zero - shot setting. The model can achieve results comparable to supervised methods on various semantic segmentation datasets without being trained on them. It replaces class labels with vector - valued embeddings of short paragraphs describing the class, allowing the merging of multiple datasets with different class labels and semantics.

📚 Documentation

Model Background

In 2014, Long et al. published a fundamental paper on using convolutional neural networks for semantic segmentation. More recently, Transformers have been successfully applied in computer vision, including image classification tasks (e.g., [ViT](https://huggingface.co/blog/fine - tune - vit)). Transformers have also shown excellent performance in semantic segmentation on several widely used datasets.

SegFormer

We specifically focus on the SegFormer for classifying street view images of buildings. It offers a unique architecture that enhances segmentation performance.

Zero - Shot Semantic Segmentation

Yin et al.'s approach allows the model to achieve performance equal to state - of - the - art supervised methods on 7 benchmark datasets. When fine - tuned on standard semantic segmentation datasets, it can even outperform other methods.

Tutorial

See the full tutorial here.

📄 License

This project is licensed under the MIT license.

Property	Details
Model Type	SegFormer
Training Data	Xpitfire/cmp_facade
Metrics	accuracy
Pipeline Tag	image - segmentation
Tags	building

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