title: Overfitting on image classification
body: <p>I'm working on image classification problem of sign language digits dataset with 10 categories (numbers from 0 to 10). My models are highly overfitting for some reason, even though I tried simple ones (like 1 Conv Layer), classical ResNet50 and even state-of-art NASNetMobile.</p>
  
  <p>Images are colored and 100x100 in size. I tried tuning learning rate but it doesn't help much, although decreasing batch size results in earlier increase of val accuracy.</p>
  
  <p>I applied augmentation to images and it didn't help too: my train accuracy can hit 1.0 when val accuracy can't get higher than 0.6.</p>
  
  <p>I looked at the data and it seems to load just fine. Distribution of classes in validation set is fair too. I have 2062 images in total.</p>
  
  <p>When I change my loss to <code>binary_crossentropy</code> it seems to give better results for both train accuracy and val accuracy, but that doesn't seem to be right.</p>
  
  <p>I don't understand what's wrong, could you please help me find out what I'm missing? Thank you.</p>
  
  <p>Here's a link to my notebook: <a href="https://colab.research.google.com/drive/1ujAU0Mj-nFlHbxmdNOM7RfQSz4FYsLth" rel="nofollow noreferrer">click</a></p>