M

Mit B5 Finetuned Sidewalk Semantic

Developed by zoheb
This SegFormer model has been fine-tuned on the SegmentsAI sidewalk-semantic dataset for semantic segmentation tasks.
Downloads 14
Release Time : 10/8/2022

Model Overview

SegFormer is a Transformer-based semantic segmentation model featuring a hierarchical Transformer encoder and a lightweight all-MLP decoder head, designed for sidewalk scene semantic segmentation.

Model Features

Hierarchical Transformer Encoder
Employs a hierarchical Transformer structure to effectively capture multi-scale feature information.
Lightweight MLP Decoder Head
Uses an all-MLP decoder head to maintain efficiency while achieving accurate semantic segmentation.
Sidewalk Scene Optimization
Fine-tuned on the sidewalk-semantic dataset, specifically optimized for sidewalk scene semantic segmentation tasks.

Model Capabilities

Image Semantic Segmentation
Sidewalk Scene Recognition
Multi-Class Classification

Use Cases

Urban Infrastructure
Sidewalk Analysis
Used to identify and analyze different components of urban sidewalks.
Can accurately segment 35 categories including sidewalks, roads, and obstacles.
Autonomous Driving
Road Scene Understanding
Assists autonomous driving systems in understanding sidewalk and road environments.
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