Body: <p>I am working on classifying images in &quot;Left&quot;, &quot;Right&quot;, &quot;Center&quot;, &quot;Back&quot;. Training and Validation images look like this:</p>
  <p><a href="https://i.sstatic.net/IXGub.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/IXGub.jpg" alt="enter image description here" /></a>
  <a href="https://i.sstatic.net/FcFso.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/FcFso.jpg" alt="enter image description here" /></a>
  <a href="https://i.sstatic.net/ATmCx.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/ATmCx.jpg" alt="enter image description here" /></a></p>
  <p>The images are &quot;Left&quot;, &quot;Right&quot;, and &quot;Center&quot;. I am following Pytorch transfer learning tutorial with Resnet50 architecture and have not changed anything.</p>
  <p>The transformations I am using is as follows.</p>
  <pre><code>data_transforms = {&quot;train&quot;  : A.Compose([
                             A.Resize(256,256),
                            #  A.ShiftScaleRotate(shift_limit=0.05, scale_limit=0.05, rotate_limit=15, p=0.5),
                            #  A.RandomCrop(height=128, width=128),
                             A.RGBShift(r_shift_limit=15, g_shift_limit=15, b_shift_limit=15, p=0.5),
                             A.RandomBrightnessContrast(p=0.5),
                             A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
                             ToTensorV2(),]),
                    &quot;val&quot;: A.Compose([
                                      A.Resize(256,256),
                                      # A.CenterCrop(height=128, width=128),
                                      A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
                                      ToTensorV2(),])
                  }
  </code></pre>
  <p>Initially I had back images too, but they were getting miss-classified with center a lot with val accuracy 65%.</p>
  <p>Without back images I got 74% accuracy with labels left, right and center.
  Modified it to classify only center and not center, with only three type of images &quot;left&quot;, &quot;right&quot; and &quot;center&quot; I have achieved 91% of val accuracy.</p>
  <p>I am looking for ways to increase accuracy for classifying images in left, right, center.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-13T23:43:00.373Z
Comments.Text: Hi sandepp! does your dataset contain only one object?
Comments.UserDisplayName: malioboro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-14T11:18:30.793Z
Comments.Text: No, there are various products which I gathered from different sources mostly include electrical products like these. One of the sites I scrapped was Eaton
Comments.UserDisplayName: sandepp
Tags: <deep-learning><image-recognition><pytorch>
Title: How to increase accuracy of image orientation classification (Left, Right, Center)?
UserDisplayName: sandepp
ThreadText: # How to increase accuracy of image orientation classification (Left, Right, Center)?
  
  (Asked by: username_0 on 2021-09-13)
  
  I am working on classifying images in "Left", "Right", "Center", "Back". Training and Validation images look like this:
  
  
  
  The images are "Left", "Right", and "Center". I am following Pytorch transfer learning tutorial with Resnet50 architecture and have not changed anything.
  The transformations I am using is as follows.
  data_transforms = {"train"  : A.Compose([
                             A.Resize(256,256),
                            #  A.ShiftScaleRotate(shift_limit=0.05, scale_limit=0.05, rotate_limit=15, p=0.5),
                            #  A.RandomCrop(height=128, width=128),
                             A.RGBShift(r_shift_limit=15, g_shift_limit=15, b_shift_limit=15, p=0.5),
                             A.RandomBrightnessContrast(p=0.5),
                             A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
                             ToTensorV2(),]),
                    "val": A.Compose([
                                      A.Resize(256,256),
                                      # A.CenterCrop(height=128, width=128),
                                      A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
                                      ToTensorV2(),])
                  }
  
  Initially I had back images too, but they were getting miss-classified with center a lot with val accuracy 65%.
  Without back images I got 74% accuracy with labels left, right and center.
  Modified it to classify only center and not center, with only three type of images "left", "right" and "center" I have achieved 91% of val accuracy.
  I am looking for ways to increase accuracy for classifying images in left, right, center.
  
  **Tags:** deep-learning, image-recognition, pytorch
  > **Comments:**
  > **username_1**: Hi sandepp! does your dataset contain only one object?  
  > **username_0**: No, there are various products which I gathered from different sources mostly include electrical products like these. One of the sites I scrapped was Eaton  
  
  ---
  
  ## 0 answers
  
  