DeepLab v3 plus - ResNet101 model trained on MUAD dataset
This is a DeepLab v3 plus model with ResNet101 backbone trained on the MUAD dataset. The training is based on PyTorch.
MUAD is a synthetic dataset with multiple uncertainties for autonomous driving [Paper] [Website] [Github].
ICCV UNCV 2023 | MUAD challenge
MUAD challenge is now on board on the Codalab platform for uncertainty estimation in semantic segmentation. This challenge is hosted in conjunction with the ICCV 2023 workshop, Uncertainty Quantification for Computer Vision (UNCV). Go and have a try! 🚀 🚀 🚀 [Challenge link]
Reference
If you find this work useful for your research, please consider citing our paper:
@inproceedings{franchi22bmvc,
title = {MUAD: Multiple Uncertainties for Autonomous Driving benchmark for multiple uncertainty types and tasks},
author = {Gianni Franchi and Xuanlong Yu and Andrei Bursuc and Angel Tena and Rémi Kazmierczak and Severine Dubuisson and Emanuel Aldea and David Filliat},
booktitle = {33rd British Machine Vision Conference, {BMVC}},
year = {2022}
}
@inproceedings{deeplabv3plus2018,
title = {Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
author = {Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
booktitle = {ECCV},
year = {2018}
}
Copyright
Copyright for MUAD Dataset is owned by Université Paris-Saclay (SATIE Laboratory, Gif-sur-Yvette, FR) and ENSTA Paris (U2IS Laboratory, Palaiseau, FR).