FrOoDo: Framework for Out-of-Distribution Detection
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866916125315432448 |
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| author | Stieber, Jonathan Fuchs, Moritz Mukhopadhyay, Anirban |
| author_facet | Stieber, Jonathan Fuchs, Moritz Mukhopadhyay, Anirban |
| contents | FrOoDo is an easy-to-use and flexible framework for Out-of-Distribution detection tasks in digital pathology. It can be used with PyTorch classification and segmentation models, and its modular design allows for easy extension. The goal is to automate the task of OoD Evaluation such that research can focus on the main goal of either designing new models, new methods or evaluating a new dataset. The code can be found at https://github.com/MECLabTUDA/FrOoDo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_00963 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | FrOoDo: Framework for Out-of-Distribution Detection Stieber, Jonathan Fuchs, Moritz Mukhopadhyay, Anirban Computer Vision and Pattern Recognition FrOoDo is an easy-to-use and flexible framework for Out-of-Distribution detection tasks in digital pathology. It can be used with PyTorch classification and segmentation models, and its modular design allows for easy extension. The goal is to automate the task of OoD Evaluation such that research can focus on the main goal of either designing new models, new methods or evaluating a new dataset. The code can be found at https://github.com/MECLabTUDA/FrOoDo. |
| title | FrOoDo: Framework for Out-of-Distribution Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2208.00963 |