torchosr -- a PyTorch extension package for Open Set Recognition models evaluation in Python
This package addresses the need for standardized and open-source tools in the Open Set Recognition domain, primarily for researchers, but it is incremental as it focuses on implementation and evaluation rather than new algorithmic breakthroughs.
The authors introduced torchosr, a PyTorch extension package for evaluating Open Set Recognition models in Python, which includes state-of-the-art methods and tools to simplify and promote correct experimental evaluation on derived sets with varying Openness and class assignments.
The article presents the torchosr package - a Python package compatible with PyTorch library - offering tools and methods dedicated to Open Set Recognition in Deep Neural Networks. The package offers two state-of-the-art methods in the field, a set of functions for handling base sets and generation of derived sets for the Open Set Recognition task (where some classes are considered unknown and used only in the testing process) and additional tools to handle datasets and methods. The main goal of the package proposal is to simplify and promote the correct experimental evaluation, where experiments are carried out on a large number of derivative sets with various Openness and class-to-category assignments. The authors hope that state-of-the-art methods available in the package will become a source of a correct and open-source implementation of the relevant solutions in the domain.