-
Notifications
You must be signed in to change notification settings - Fork 437
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Training from scratch doesn't reach the same loss #22
Comments
Same here. I noticed that the network on the notebook is different from the model you've saved. Even using the same network, the performances are way worse than the ones you achieved using the same code. Maybe you had some clever training trick? |
Hello @nicolov and @gianlucahmd. I wish I had some magic tricks go increase the accuracy. There is only one thing that comes to my mind right now, which is try to augment the data that you have available and try to retrain the model. |
Did you perform data augmentation yourself? Maybe that's the problem: I just downloaded the data from the links but end up having ~900 samples in the training set whereas you had ~1300. Thanks for getting back to me! |
Sure, I was just wondering how you got to the accuracy in the pretrained model that's in repo, as I can't seem to reproduce its accuracy using your training code. |
Can someone pls tell how you have organized data in the folder.have you created any subfolders |
Hey @MITESHPUTHRANNEU, I have the same problem as nicolov but it can't be a different number of features, otherwise your model wouldn't work at inference time. It must be something different during training and/or different data. My first bet is different data, as downloading the data from the link you provided I get ~900 samples in the training set whereas from your notebook I see you had ~1300. Can you double-check that all the data you used is the one available from these links? |
Hi @gianlucahmd, yes if you change the sampling rate then you can't use my model. I have used used the data from the described sources. They may have changed the audio files as I had done this project in 2017. |
Hey, thanks a lot for the release. I've tried training the model from scratch using the datasets, but I can't reach the same validation loss. I noticed that the pre-trained network in the repo has two more convolutional layers compared to the code in the notebook, but adding them back doesn't help either.
Did you se any additional tricks for training?
For reference, above is what I see, below is what you have in the dataset:
The text was updated successfully, but these errors were encountered: