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Segformer B5 640x640 Ade 160k

Developed by smp-hub
PyTorch-based Segformer image segmentation model, suitable for semantic segmentation tasks on the ADE20K dataset
Downloads 274
Release Time : 11/29/2024

Model Overview

Segformer is an efficient semantic segmentation model that employs a hybrid Transformer architecture and performs excellently on the ADE20K dataset.

Model Features

Efficient Transformer Architecture
Utilizes a hybrid Transformer architecture that combines the strengths of CNN and Transformer
Pre-trained Support
Provides pre-trained weights for direct inference or fine-tuning
Multi-scale Feature Fusion
Effectively handles image features at different scales

Model Capabilities

Image Semantic Segmentation
Scene Understanding
Pixel-level Classification

Use Cases

Computer Vision
Scene Parsing
Performs pixel-level classification of various elements in complex scenes
Performs well on the ADE20K dataset
Autonomous Driving
Used for road scene understanding to identify elements such as roads, vehicles, and pedestrians
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