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Segformer Trainer Test Bis

Developed by nielsr
An image segmentation model fine-tuned from nvidia/mit-b0 for sidewalk semantic segmentation tasks
Downloads 14
Release Time : 4/19/2022

Model Overview

This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset, primarily used for semantic segmentation tasks in urban road scenes.

Model Features

Lightweight Architecture
Lightweight Transformer architecture based on mit-b0, suitable for resource-constrained environments
Multi-class Segmentation
Supports recognition of 40+ road scene elements including roads, sidewalks, buildings, etc.
Fine-tuning Optimization
Specially optimized on the segments/sidewalk-semantic dataset, suitable for urban road scenes

Model Capabilities

Semantic Image Segmentation
Road Scene Analysis
Multi-class Recognition

Use Cases

Smart City
Sidewalk Infrastructure Analysis
Automatically identify and classify various infrastructure elements in urban sidewalk areas
Can recognize elements such as roads, sidewalks, buildings, etc.
Road Safety Monitoring
Detect traffic signs, pedestrians, and other elements on roads to assist safety monitoring
Can detect targets such as traffic signs and pedestrians
Autonomous Driving
Road Scene Understanding
Provide semantic understanding of road scenes for autonomous driving systems
Can identify key elements such as roads, sidewalks, vehicles, etc.
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