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torchosr - A PyTorch extension package for Open Set Recognition models evaluation in Python
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DOI：10.1016/j.neucom.2023.127047.png)
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摘要
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The article presents the torchosr module - a Python package compatible with PyTorch library - offering functionality and models dedicated to Open Set Recognition in Deep Neural Networks. Included software offers two frequently used base recognition methods in the field and a set of functions for handling datasets, enabling the generation of derived datasets, where some classes are considered unknown and used only in the testing process. Code base is enhanced with a set of helper functions, facilitating model validation process. The main goal of the 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, related to the cardinality of known and unknown classes, and class-to-category assignments. The authors hope that methods available in the package will become a source of a correct and open-source implementation of the relevant baseline and state-of-the-art solutions in the domain.
Keyword:
Open Set Recognition
Out of Distribution Detection
Classification
PyTorch
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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