| | --- |
| | license: afl-3.0 |
| | --- |
| | |
| | ## 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]](https://arxiv.org/abs/2203.01437) [[Website]](https://muad-dataset.github.io/) [[Github]](https://github.com/ENSTA-U2IS/MUAD-Dataset). |
| |
|
| | ### 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](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) |
| |
|
| | ### 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). |
