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TwinLiteNet is a lightweight and efficient deep learning model designed for Drivable Area Segmentation and Lane Detection in self-driving cars. This repository provides the code and resources needed to train, evaluate, and deploy TwinLiteNet.
Make sure you have the required dependencies installed:
pip install -r requirements.txt
- Download images from BDD100K Dataset.
- Download annotations:
- Drivable Area Segmentation: Google Drive
- Lane Line Segmentation: Google Drive
/data
bdd100k
images
train/
val/
test/
segments
train/
val/
lane
train/
val/
Train the model using the command:
python3 train.py
Evaluate the model performance using:
python3 val.py
Perform inference on images:
python3 test_image.py
This work is inspired by:
If you find our work helpful, please consider starring ⭐ this repository and citing our paper:
@INPROCEEDINGS{10288646,
author={Che, Quang-Huy and Nguyen, Dinh-Phuc and Pham, Minh-Quan and Lam, Duc-Khai},
booktitle={2023 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)},
title={TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars},
year={2023},
pages={1-6},
doi={10.1109/MAPR59823.2023.10288646}
}