title: CNN architecture for binary classification problem (Infrared images)
body: <p>I try to train a model for a binary classification problem with the images being infrared (temperatures) with one-channel. After converting them to three channels (by replicate the 3rd channel) I tried two CNN architecture, VGG-11, and VGG-16, but didn't manage to get a stable training(low accuracy, and after 2-10 epochs(depending on learning rate adjustment) loss freezes in some value. </p>
  
  <p>Standard VGG architecture is used except from <code>AdaptiveAvgPool2d()</code>  which is first used in order to alleviate inputs with an arbitrary size. The input size of images is 340x340.</p>
  
  <p><code>CrossEntropyLoss()</code>  is used with labels [0,1] output from the aforementioned network given. Also, the model is trained from scratch(because of the data's nature).<br>
  Any idea for improving my architecture in the needs of my problem? 
  I haven't found many works on infrared-image classification, so any help would be highly appreciated.</p>