title: how to set up the appropriate model setting and layers for high intra-class variation
body: <p>all experts</p>
  
  <p>I am new in CNN and Caffe. I have a task in classification between 2 classes. The data set that I have collected is very small about 50 for class A and 50 for class B (I know that it is very very small). It is a human images.
  I took the BVLC model and made a change such as Batch size for testing and training and also the maximum iteration. I try with many various setup, but the model doesn't work. 
  Any idea about how to come up with appropriate model or setting or other solutions ?</p>
  
  <p>remark** I once randomly made a change to the BVLC model setup and it worked, but i lost the set up file.</p>
  
  <p>For the <strong>train.prototxt</strong> and <strong>Solve.prototxt</strong>, I get it from this guy <a href="https://github.com/adilmoujahid/deeplearning-cats-dogs-tutorial/tree/master/caffe_models/caffe_model_1" rel="nofollow noreferrer">Adil Moujahid</a></p>
  
  <p>I did try training batch size as 32,64,128,256 and testing for 5,20,30 but failed</p>
  
  <p>For the data set, it is images of normal women and beautiful women and i will classify it, but Stackoverflow does not allowed me to add more than 2 links</p>
  
  <p>I wonder that is there any formula , equation or steps that I can come up with and choose the right model setting.</p>
  
  <p>Thank you in advance.</p>