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--- |
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license: afl-3.0 |
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--- |
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## DeepLab v3 plus - ResNet101 model trained on MUAD dataset |
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This is a DeepLab v3 plus model with ResNet101 backbone trained on the MUAD dataset. The training is based on PyTorch. |
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MUAD is a synthetic dataset with multiple uncertainties for autonomous driving [[Paper]](https://arxiv.org/abs/2203.01437) [[Website]](https://muad-dataset.github.io/) [[Github]](https://github.com/ENSTA-U2IS/MUAD-Dataset). |
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### ICCV UNCV 2023 | MUAD challenge |
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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](https://iccv2023.thecvf.com/) workshop, [Uncertainty Quantification for Computer Vision (UNCV)](https://uncv2023.github.io/). Go and have a try! 🚀 🚀 🚀 [[Challenge link]](https://codalab.lisn.upsaclay.fr/competitions/8007) |
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### Reference |
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If you find this work useful for your research, please consider citing our paper: |
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``` |
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@inproceedings{franchi22bmvc, |
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title = {MUAD: Multiple Uncertainties for Autonomous Driving benchmark for multiple uncertainty types and tasks}, |
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author = {Gianni Franchi and Xuanlong Yu and Andrei Bursuc and Angel Tena and Rémi Kazmierczak and Severine Dubuisson and Emanuel Aldea and David Filliat}, |
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booktitle = {33rd British Machine Vision Conference, {BMVC}}, |
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year = {2022} |
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} |
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``` |
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``` |
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@inproceedings{deeplabv3plus2018, |
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title = {Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation}, |
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author = {Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam}, |
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booktitle = {ECCV}, |
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year = {2018} |
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} |
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``` |
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### Copyright |
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Copyright for MUAD Dataset is owned by Université Paris-Saclay (SATIE Laboratory, Gif-sur-Yvette, FR) and ENSTA Paris (U2IS Laboratory, Palaiseau, FR). |
