Comments (4)
emadeldeen24
commented on May 28, 2024
Usually it is done in the beginning to make training faster.
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vamsi231297
commented on May 28, 2024
As we are initializing the dataset class only once; we are using augmentation only once.But we need to do augmentation at each epoch otherwise our data would get fixed on the initial augmentations; in self supervised training shouldn't we augment at each epoch? Sorry let me know if I am missing something here :)
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emadeldeen24
commented on May 28, 2024
I'm not sure if it will make a difference, but you can test and let me know.
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vamsi231297
commented on May 28, 2024
sure
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Related Issues (20)
- Produce macro-averaged F1-score (MF1) results HOT 2
- About training a new dataset HOT 3
- Some Questions
- question regarding the implementation of your temporal contrasting loss HOT 1
- there might be code error for augmentation? HOT 2
- Contextual Contrasting Loss Function HOT 1
- Badly in need of a pretrained model of epilepsy.Could anyone help? HOT 1
- Augmentations and # of training epochs HOT 1
- Obtaining labels on a completly unsupervised dataset HOT 1
- data augment HOT 1
- Can not repeat FD dataset preprocess HOT 3
- the process of self-supervised experiment HOT 5
- Nan question in SupConLoss HOT 4
- Loss cannot decrease HOT 1
- Problem with Augmentation HOT 4
- how to handle overfitting problem? HOT 2
- Problem with self_supervised mode training HOT 1
- Request for training logs and detailed settings HOT 5
- Add license HOT 1
- Something wrong with the code when i use self-supervised mode HOT 1
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