Creating a image classification model I am working on a dataset to classify facial expressions.
Dataset has 7 classes, training images 28000 and test images 7000. I created 2 models
Model1:
this model has 11 layers. Initially model was working fine but now accuracy is increasing very slowly after 60% and drooping sometimes. model 1 link
then I thought that due large dataset my model is over fitting and I reduced the images by randomly deleting in each class and reducing train set images to 15000 and test set images to 3900. and created another model.
Model 2:
This model has 9 layers. The graph for first 40 epochs was fine but after then when I resumed training its seems to overfit.model 2 link
first 40 epoch


this seems fine i guess
after resuming training


I have also tried model with fewer layers nut cant figure out why my accuracy is stuck at and model is not improving
dataset link, I don't understand is there a problem with dataset, can anyone provide me link to another dataset. Figure out what changes are required in model.
1.) You should not change your test set, even if you downsample the training set.
2.) At least in the plots you show there is no clear indication of overfitting. Did you see what happens if you train for longer epochs, and tried different learning rates ?
3.) If you have some overfitting, normally you would simplify the model, and not reduce the size of the dataset. Also you can think about data augmentation to prevent overfitting.
4.) You can compare your solution to the solutions available at kaggle.
5.) With such a black-box solution (vanilla CNN) you will not get super high results.  Though I do not know the upper bound. You can compare to kaggle for that..
6.) To further improve you have to integrate some prior knowledge (e.g. facial key points).