Segformer B0 Finetuned Cityscapes 768 768
SegFormer is a Transformer-based semantic segmentation model fine-tuned on the CityScapes dataset, suitable for semantic segmentation tasks in urban scene images.
Downloads 566
Release Time : 3/2/2022
Model Overview
This model employs a hierarchical Transformer encoder and a lightweight all-MLP decoder head design, optimized for semantic segmentation of 768x768 resolution urban scene images, demonstrating excellent performance on benchmarks like CityScapes.
Model Features
Hierarchical Transformer Architecture
Utilizes a hierarchical Transformer encoder to effectively capture multi-scale feature information.
Lightweight MLP Decoder Head
Features an all-MLP decoder head design, maintaining high performance while reducing computational complexity.
High-Resolution Support
Specifically optimized for 768x768 high-resolution images, ideal for urban scene analysis.
Model Capabilities
Image Semantic Segmentation
Urban Scene Analysis
Road Scene Understanding
Use Cases
Intelligent Transportation
Road Scene Segmentation
Used for identifying and segmenting elements like roads, vehicles, and pedestrians in autonomous driving systems.
Performs excellently on the CityScapes dataset
Urban Planning
Urban Scene Analysis
Used to analyze the distribution of urban elements such as buildings, roads, and green spaces.
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