title: How to overcome overfitting in CNN - standard methods don't work
body: <p>I've been recently playing around with car data set from Stanford (<a href="http://ai.stanford.edu/~jkrause/cars/car_dataset.html" rel="noreferrer">http://ai.stanford.edu/~jkrause/cars/car_dataset.html</a>).
  From the very beginning I had an overfitting problem so decided to:</p>
  
  <ol>
  <li>Add regularization (L2, dropout, batch norm, ...)</li>
  <li>Tried different architectures (VGG16, VGG19, InceptionV3, DenseNet121, ...)</li>
  <li>Tried trasnfer learning using models trained on ImageNet</li>
  <li>Used data augmentation</li>
  </ol>
  
  <p>Every step moved me a little bit forward. However I finished with 50% validation accuracy (started below 20%) compared to 99% train accuracy.</p>
  
  <p>Do you have an idea what more can I do to get to around 80-90% accuracy?</p>
  
  <p>Hope this can help some people!:)</p>