The MUAD dataset (Multiple Uncertainties for Autonomous Driving), consisting of 10,413 realistic synthetic images with diverse adverse weather conditions (night, fog, rain, snow), out-of-distribution objects, and annotations for semantic segmentation, depth estimation, object, and instance detection. Predictive uncertainty estimation is essential for the safe deployment of Deep Neural Networks in real-world autonomous systems and MUAD allows to a better assess the impact of different sources of uncertainty on model performance.
LUMA is a multimodal dataset that consists of audio, image, and text modalities. It allows controlled injection of uncertainties into the data and is mainly intended for studying uncertainty quantification in multimodal classification settings. This repository provides the Audio and Text modalities. The image modality consists of images from CIFAR-10/100 datasets. To download the image modality and compile the dataset with a specified amount of uncertainties, please use the LUMA compilation tool.
The JAMBO dataset contains 3290 underwater images of the seabed captured by an ROV in temperate waters in the Jammer Bay area off the North West coast of Jutland, Denmark. All the images have been annotated by six annotators to contain one of three classes: sand, stone, or bad. Each of the six annotators have labelled all the images (that is, six individual annotations are provided for each image), which allows for analyzing inter-annotator disagreement and uncertainty.
This dataset extends the Semantic Segmentation of Underwater Imagery: Dataset and Benchmark, adding an uncertainty evaluation component. To facilitate uncertainty analysis, the test set incorporates a comprehensive range of perturbations, inspired by Benchmarking Neural Network Robustness to Common Corruptions and Perturbations, applied at four intensity levels. These perturbations, which preserve the original ground truth labels, encompass variations in Brightness and Contrast (simulating diverse lighting and object coloration), Gaussian and Shot Noise (reflecting low-light and discrete light properties), and Impulse Noise (resulting from bit errors). Additionally, Defocus, Motion, and Zoom Blurs are included, along with Elastic Transformations, Pixelation from upscaling, and JPEG Compression artifacts. This enhanced dataset enables an in-depth evaluation of model robustness, providing valuable insights into performance under a wide range of challenging, real-world underwater conditions.