Body: <p>So, I'm using a pretrained <a href="https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py" rel="nofollow noreferrer">PNASNet-5-Large model</a> to do some image classification.</p>
  <p>In the file, it says that the input range is in [0,1] (I'm assuming pixel values of input images). The images I have are already in this range.
  The channel means and standard deviation for RGB channels are stated as [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] respectively.
  Now when I use the <a href="https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Normalize" rel="nofollow noreferrer">torchvision.transforms.Normalize</a> to normalize the images using the stated means and standard deviations, the pixel values get to the range [-1,1].</p>
  <p>The code I wrote for normalization:</p>
  <pre><code>transforms.Normalize([0.5, 0.5, 0.5],[0.5, 0.5, 0.5])
  </code></pre>
  <p>I believe I'm missing something fundamental. Should I normalize the images or should I not? Thanks!</p>
  
Tags: <convolutional-neural-networks><classification>
Title: Relationship between input range and channel means, standard deviations for CNNs
UserDisplayName: john wayne
ThreadText: # Relationship between input range and channel means, standard deviations for CNNs
  
  (Asked by: username_0 on 2018-12-19)
  
  So, I'm using a pretrained PNASNet-5-Large model to do some image classification.
  In the file, it says that the input range is in [0,1] (I'm assuming pixel values of input images). The images I have are already in this range.
  The channel means and standard deviation for RGB channels are stated as [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] respectively.
  Now when I use the torchvision.transforms.Normalize to normalize the images using the stated means and standard deviations, the pixel values get to the range [-1,1].
  The code I wrote for normalization:
  transforms.Normalize([0.5, 0.5, 0.5],[0.5, 0.5, 0.5])
  
  I believe I'm missing something fundamental. Should I normalize the images or should I not? Thanks!
  
  **Tags:** convolutional-neural-networks, classification
  
  ---
  
  ## 0 answers
  
  