Awesome
Code used in Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors
We deliver all code used for our experimental setups and for the generation of the shown plots. The folders yolov3-torch, faster-rcnn-torch and retinanet-torch contain the object detection pipelines. We are unable to provide weight files for the networks due to upload constraints. The object detectors all implement a pipeline for computing gradient uncertainty metrics, a more detailed structure can be found in the respective README.md-files included in the folders.
The folder uncertainty_aggregation contains the framework we used for aggregating uncertainty metrics in meta classification and meta regression, as well as the main experimental setups once gradient-based and Monte-Carlo dropout-based uncertainty metrics have been produced. We included a more detailed description in the README.md-file in the folder itself.