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MUAD: Multiple Uncertainties for Autonomous Driving Dataset

This repository contains some development kits including the scripts for the evaluation (in PyTorch) that we used for our BMVC 2022 paper:

MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks.

[Paper] [Website] [Data]

Download and use MUAD on a headless server with TorchUncertainty

You will find a PyTorch dataset for MUAD's training and validation sets in semantic segmentation and depth prediction with automated download in TorchUncertainty.

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]

How to download the MUAD dataset?

If you need MUAD Dataset, please Click and Fill in this Google form.

We provide you with permanent download links as soon as you finish submitting the form.

[Note] We will release all the test sets (with the RGB images and the ground truth maps) after the MUAD challenge on the Codalab. Currently, only a small part of the test sets is released with only the RGB images.

Semantic segmentation

Training and Evaluation on MUAD

We provide here a training and evaluation example, as well as a checkpoint based on DeepLab v3 plus. Github link: [DeepLabv3Plus-MUAD-Pytorch].

Evaluation metrics

See folder ./evaluation_seg/. This scoring program is also used in our Challenge. Check ./evaluation_seg/evaluation.py for details.

We provide the implementations on mECE, mAUROC, mAUPR, mFPR, mIoU, mAccuracy, etc., see ./evaluation_seg/stream_metrics.py for details.

You have to make some modifications in the codes according to your own needs. For instance, we by default set our output confidence map as type .pth, and set only mIOU for semantic segmentation performance, etc.

Prediction format

The predictions we generate for semantic segmentation task follow the following format.

For each image_id in the test set, we predict a confidence score and a predicted class label for each pixel. The prediction results are saved as dictionary objects in .pth form. Here is an example to show the components in a .pth prediction result:

import torch
prediction = torch.load('000000_leftImg8bit.pth')
print(prediction.keys()) # should output: dict_keys(['conf', 'pred'])
print(prediction['conf']) # should output: torch.Size([1024, 2048])
print(prediction['pred']) # should output: torch.Size([1024, 2048])

The confidence score should be torch.float16 and the predicted class labels should be torch.int64.

More information

The indexes of the classes of semantic segmentation are the following (in leftLabel):

class names ID
road 0
sidewalk 1
building 2
wall 3
fence 4
pole 5
traffic light 6
traffic sign 7
vegetation 8
terrain 9
sky 10
person 11
rider 12
car 13
truck 14
bus 15
train 16
motorcycle 17
bicycle 18
bear deer cow 19
garbage_bag stand_food trash_can 20

Monocular depth estimation

Evaluation metrics

See folder ./evaluation_depth/. We provide two scripts, ./evaluation_depth/depth_metrics.py is for depth prediction performance, and ./evaluation_depth/sparsification.py is for sparsification curves and its corresponding uncertainty estimation performance metrics, more details on this metric can be found in this paper and the git repo.

More information

The depth groundtruth data is in the form of .exr files. The depth in the image is annotated as min/max depth value. To load and transfer the depth in the image to the depth in meters, you can try the following codes:

import cv2
from PIL import Image
import numpy as np
depth_path = 'xxx.exr'
depth = cv2.imread(depth_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
depth = Image.fromarray(depth)
depth = np.asarray(depth, dtype=np.float32)
depth = 400 * (1 - depth) # the depth in meters

Object/Instance detection

TODO

Citation

If you find this work useful for your research, please consider citing our paper:

@inproceedings{franchi22bmvc,
  author    = {Gianni Franchi and 
              Xuanlong Yu and 
              Andrei Bursuc and 
              Angel Tena and 
              Rémi Kazmierczak and 
              Severine Dubuisson and 
              Emanuel Aldea and 
              David Filliat},
  title     = {MUAD: Multiple Uncertainties for Autonomous Driving benchmark for multiple uncertainty types and tasks},
  booktitle = {33rd British Machine Vision Conference, {BMVC} 2022},
  year      = {2022}
}

Copyright

Copyright for MUAD Dataset is owned by Université Paris-Saclay (SATIE Laboratory, Gif-sur-Yvette, FR) and ENSTA Paris (U2IS Laboratory, Palaiseau, FR).