pytorch-metric-learning
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KevinMusgrave
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
demo code here, test device: macos m2 chip ``` import numpy as np from pytorch_metric_learning.utils.accuracy_calculator import AccuracyCalculator import umap if __name__ == '__main__': umapper = umap.UMAP() test_embeddings = np.random.normal(size=(100, 64))...
Would it be possible to update the github page section _How to extend this library_ (or even just a file in this repository cited in the README) with some practical...
Also, the existing MoCo notebook should probably use SelfSupervisedLoss.
It doesn't make sense anymore
Hi, recently I've come across the implementation of a 2018 paper [GE2E](https://arxiv.org/pdf/1710.10467.pdf), which basically computed the loss with the corresponding similarity matrix given embedding vectors and all centroids. I wrote...
See: - https://github.com/KevinMusgrave/pytorch-metric-learning/issues/547#issuecomment-1304907510 - https://github.com/KevinMusgrave/pytorch-metric-learning/issues/6#issuecomment-1416870096
As originally discussed here https://github.com/KevinMusgrave/pytorch-metric-learning/issues/411
See this discussion: https://github.com/KevinMusgrave/pytorch-metric-learning/discussions/561#discussioncomment-4580712
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The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.