Python implementation of our paper ...

Uncertainty quantification for semantic image segmentation models using Monte Carlo dropout method

YOLOv3 object detection architecture with uncertainty estimation.

Code for "Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors." (ICLR 2021)

[ECCVW 2022] UAD: Localization Uncertainty Estimation for Anchor-Free Object Detection

Code for our paper titled: "A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving"

The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a neural network.

PyTorch implementation of Gaussian YOLOv3 (including training code for COCO dataset)

This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.

[CVPR 2023] Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection

Code for <Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection> in IEEE TMM 2021

The official implementation of Self-aware Object Detection [CVPR 2023]