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Segformer Finetuned Lane 10k Steps

Developed by Efferbach
A lightweight lane line detection model based on the SegFormer architecture, fine-tuned for 10,000 steps on the lane_master dataset
Downloads 1,077
Release Time : 4/7/2023

Model Overview

This model is a fine-tuned version of NVIDIA SegFormer-b0, pre-trained on the Cityscapes dataset, specifically optimized for lane line detection tasks. It is designed to identify left and right lane markings in road scenes.

Model Features

Efficient lane line detection
Optimized for road scenes, accurately identifying left and right lane lines
Lightweight architecture
Designed based on SegFormer-b0's lightweight structure, suitable for real-time applications
Transfer learning optimization
Fine-tuned on a Cityscapes pre-trained model to enhance lane line detection performance

Model Capabilities

Road scene image segmentation
Left and right lane line recognition
Pixel-level semantic segmentation

Use Cases

Autonomous driving
Lane keeping assist system
Real-time detection of vehicle lane position
Average IoU 0.49, accuracy 0.737
Road scene analysis
Extracting structured road information for high-definition map construction
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