Want to know to if the Model is overfitting or underfitting or it is correct

Deep Learning
1

I am training my model on Cifar-10 Dataset with DenseNet Architecture

Here is the info about the model for the first 50 epochs
No of parameters:- 98k
Training loss:- 0.7143
Training acc: 0.7890
Validation loss: 0.7242
Validation acc: 0.8020

After another 50 epochs

Training loss:- 0.5796
Training acc: 0.8353
Validation loss:0.5531
Validation acc: 0.8478

Because the Validation accuracy is greater than the Training Accuracy is the model fitting perfectly rather then Overfitting or Underfitting

2

Those loss values are pretty close in the relative scheme of over/under fitting I’ve seen, so I think you are fitting perfectly. I’m getting as much difference as 0.001 training loss vs. 0.6 validation loss, which is very clearly overfitting BIG TIME

3

things are good as long as your accuracy increases.

However have as look at courses/dl2/cifar10-dawn.ipynb to see example of a much faster convergence

5

I’m wondering how your select your validation set. Is it using fastai built-in get_csv_idxs that randomly selects 20%? Could it be that by chance your validation set just happened to be easier compared to your training set. But seems like a result to be happy about.