lane-detection-unet-ncnn

Model Card for lane-detection-unet-ncnn

This model card documents the UNet-based lane segmentation models and NCNN deployment pipeline for the BDD100K dataset.

Model Details

Model Description

• Developed by: Nick Pai
• Model type: UNet, UNetDepthwise, UNetDepthwiseSmall, UNetDepthwiseNano (semantic segmentation)
• Language(s): Not applicable (computer vision)
• License: MIT
• Finetuned from model: Custom UNet architectures

Model Sources

• Repository: https://github.com/nick8592/lane-detection-unet-ncnn
• Paper: None (see repo for references)
• Demo: See README for C++ and Python inference examples

Uses

Direct Use

• Lane segmentation for autonomous driving, road scene analysis, and research
• Python and C++ inference (PyTorch, NCNN)

Downstream Use

• Can be fine-tuned for other road segmentation tasks or datasets

Out-of-Scope Use

• Not suitable for non-road or non-lane segmentation tasks
• Not designed for medical or non-automotive domains

Bias, Risks, and Limitations

• Trained on BDD100K, so performance may degrade on out-of-distribution scenes
• Lane annotations may contain labeling errors or bias
• Not robust to extreme weather, occlusions, or unusual camera angles

Recommendations

• Users should validate model performance on their own data before deployment
• Consider retraining or fine-tuning for new domains

How to Get Started with the Model

See the README for setup, training, and inference instructions. Example:

# Python inference
python3 scripts/test.py

Training Details

Training Data

• BDD100K lane segmentation subset
• Images: 100k train/val/test split
• Masks: binary lane masks

Training Procedure

• Standard UNet training with Adam optimizer
• Image size: 256x256
• Batch size: 8-32
• Epochs: 10-20
• Augmentation: random flip

Training Hyperparameters

• fp32 precision
• Learning rate: 1e-3 (default)

Speeds, Sizes, Times

• Training time: ~10 hours on RTX 4060 for 10 epochs

Evaluation

Testing Data, Factors & Metrics

• BDD100K val set
• Metrics: IoU, Dice/F1, Pixel Accuracy, MAE

Model Examination

• Visualizations and overlays available in README and assets/

Technical Specifications

Model Architecture and Objective

• UNet encoder-decoder, depthwise variants for efficiency
• Objective: binary lane segmentation

Compute Infrastructure

• Hardware: RTX 3090, Intel
• Software: PyTorch 1.13, NCNN, OpenCV

Citation

BibTeX:

@software{Pai_UNet_Lane_Segmentation_2025,
author = {Pai, Nick},
license = {MIT},
month = sep,
title = {{UNet Lane Segmentation \& NCNN Deployment}},
url = {https://github.com/nick8592/lane-detection-unet-ncnn},
version = {1.0.0},
year = {2025}
}