Segformer B2 Finetuned Cityscapes 1024 1024
SegFormer is a semantic segmentation model based on Transformer architecture, fine-tuned on the CityScapes dataset, suitable for image segmentation tasks at 1024x1024 resolution.
Downloads 2,179
Release Time : 3/2/2022
Model Overview
This model employs a hierarchical Transformer encoder with a lightweight all-MLP decoder head, specifically designed for semantic segmentation tasks, excelling in urban scenes and similar scenarios.
Model Features
Efficient Transformer Architecture
Uses a hierarchical Transformer encoder to achieve excellent semantic segmentation performance while maintaining efficiency.
Lightweight MLP Decoder Head
Employs an all-MLP structured decoder head, making it more lightweight and efficient compared to traditional decoders.
High-Resolution Support
Optimized specifically for 1024x1024 resolution images, suitable for high-precision segmentation tasks.
Model Capabilities
Image Semantic Segmentation
Urban Scene Recognition
Road Scene Parsing
Use Cases
Intelligent Transportation
Road Scene Segmentation
Performs pixel-level semantic segmentation of urban road scenes, identifying elements such as roads, vehicles, and pedestrians.
Performs excellently on the Cityscapes dataset.
Urban Planning
Urban Landscape Analysis
Analyzes urban landscape composition, identifying regions such as buildings, green spaces, and roads.
Featured Recommended AI Models
Š 2025AIbase