Class-balanced-loss-pytorch
Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"
Use a special loss function in your image recognition projects to improve performance on classes with fewer examples.
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Free · no card · unsubscribe anytimePytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"
Class-balanced-loss-pytorch has 803 stars on GitHub. It has been forked 123 times. Class-balanced-loss-pytorch is written mainly in Python. It has been in active development since 2019. Class-balanced-loss-pytorch is available under the MIT license. Its main topics are computer-vision, cvpr2019, deep-learning, loss-functions.
What is Class-balanced-loss-pytorch?
Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"
Is Class-balanced-loss-pytorch open source?
Class-balanced-loss-pytorch is an open-source project. It is released under the MIT license.
Is Class-balanced-loss-pytorch free?
Yes. Class-balanced-loss-pytorch is free and open source — you can use, modify and self-host it.
What license does Class-balanced-loss-pytorch use?
Class-balanced-loss-pytorch is available under the MIT license.
What language is Class-balanced-loss-pytorch written in?
Class-balanced-loss-pytorch is written mainly in Python.
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