Can imbalance data create overfitting? I am doing human activity recognition project. I have total of 12 classes. The class distribution look like this:

$\color{red}{If \ you \ watch \ carefully, you \ can \ see \ that \ I \ have \ no \ data \ points \ for \ class \ 11 \ and \ class \ 8.}$ Also, the dataset is highly imbalanced. So, I took minimum data points (in this case 2028) for all of the classes. Now my balanced data look like this:

After doing this it looks like a balance data. $\color{red}{But \ still, \ I \ think \ it \ not, \ because \ I \ have \ zero \ datapoints \ for \ class \ 11 \ and \ class \ 8}$. In my opinion the classes are still imbalance.
I am using CNN model to solve this activity project. My model summary is following:

The main problem is, my model starts overfitting heavily when I train it.

Is it due to my imbalance data( class 8 and 11 has zero data points) or something else?
$\textbf{Hyperperameter:}$
$\textbf{features:}$ X, Y, Z of mobile accelerometer
$\textbf{frame size:}$ 80
$\textbf{optimizer:}$ Adam, $\textbf{Learning rate:}$ 0.001
$\textbf{Loss:}$ Sparse categorical cross-entropy
Your zero-classes just do not exist for the model. There is no information about them in training and test sets. I think the reason for overfitting is that your have very small size of the training set comparing to the number of parameters in your model. If it is easier for a model to remember all training entries - it will just do that. You need to prune your model to force it to make some generalizations about the data.