This repository contains the code of "MTNAS: Search Multi-Task Networks for Autonomous Driving", which is accepted in Asian Conference on Computer Vision (ACCV), 2020.
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Use Anaconda create a python environment
conda create -n test python=3.6
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Activate the environment and install dependencies
source activate test conda install pytorch conda install torchvision conda install -c menpo opencv3
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Quick installation
conda env create /code/conda_config/environment.yaml
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Evaluation dataset directory structure:
+ data + images + images_id1.jpg + iamges_id2.jpg + seg_label + images_id1.png + iamges_id2.png + det_gt.txt + det_val.txt + seg_val.txt + demo.txt + det_log.txt + seg_log.txt images: images for detection and segmentation evaluation seg_label: segmentation ground truth det_gt.txt: detectioin ground truth image_name label_1 xmin1 ymin1 xmax1 ymax1 image_name label_2 xmin2 ymin2 xmax2 ymax2 det_val.txt: images id for detection evaluation seg_val.txt: images id for segmentation evaluation demo.txt: images id for demo visualization det_log.txt: save detection evaluation results seg_log.txt: save segmentation evaluation results
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Evaluate detection performance
cd ./code/test/ bash ./eval_det.sh #the results will be saved at /data/det_log.txt
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Evaluate segmentation performance
./code/test/ bash ./eval_seg.sh #the results will be saved at /data/seg_log.txt
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Demo
./code/test/ bash ./run_demo.sh #the demo pics will be saved at /code/test/result/demo
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Performance
Detection test images: bdd100+Waymo val 10000 Segmentation test images: bdd100+CityScapes val 1500 Model: MT-NAS Classes-detection: 4 Classes-segmentation: 16 mAP: 43.67% mIou: 46.15%
If you find the code and pre-trained model useful in your research, please cite our paper:
@InProceedings{Liu_2020_ACCV,
author = {Liu, Hao and Li, Dong and Peng, JinZhang and Zhao, Qingjie and Tian, Lu and Shan, Yi},
title = {MTNAS: Search Multi-Task Networks for Autonomous Driving},
booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)},
year = {2020}
}