Hi guys,
I am trying to perform transfer learning by customizing VGG to do a multi-class and multi-label classification.
Here is how the modified model looks like:
Model(
  (conv_model): VGG(
    (features): Sequential(
      (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): ReLU(inplace=True)
      (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (3): ReLU(inplace=True)
      (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (6): ReLU(inplace=True)
      (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (8): ReLU(inplace=True)
      (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (11): ReLU(inplace=True)
      (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (13): ReLU(inplace=True)
      (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (15): ReLU(inplace=True)
      (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (18): ReLU(inplace=True)
      (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (20): ReLU(inplace=True)
      (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (22): ReLU(inplace=True)
      (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (25): ReLU(inplace=True)
      (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (27): ReLU(inplace=True)
      (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (29): ReLU(inplace=True)
      (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
    (classifier): Sequential(
      (0): Linear(in_features=25088, out_features=4096, bias=True)
      (1): ReLU(inplace=True)
      (2): Dropout(p=0.5, inplace=False)
      (3): Linear(in_features=4096, out_features=4096, bias=True)
      (4): ReLU(inplace=True)
    )
  )
  (classifiers_0): Sequential(
    (0): Linear(in_features=4096, out_features=10, bias=True)
  )
  (classifiers_1): Sequential(
    (0): Linear(in_features=4096, out_features=11, bias=True)
  )
  (classifiers_2): Sequential(
    (0): Linear(in_features=4096, out_features=11, bias=True)
  )
  (classifiers_3): Sequential(
    (0): Linear(in_features=4096, out_features=11, bias=True)
  )
  (classifiers_4): Sequential(
    (0): Linear(in_features=4096, out_features=11, bias=True)
  )
  (classifiers_5): Sequential(
    (0): Linear(in_features=4096, out_features=11, bias=True)
  )
  (classifiers_6): Sequential(
    (0): Linear(in_features=4096, out_features=11, bias=True)
  )
  (classifiers_7): Sequential(
    (0): Linear(in_features=4096, out_features=11, bias=True)
  )
  (classifiers_8): Sequential(
    (0): Linear(in_features=4096, out_features=11, bias=True)
  )
  (classifiers_9): Sequential(
    (0): Linear(in_features=4096, out_features=11, bias=True)
  )
  (classifiers_10): Sequential(
    (0): Linear(in_features=4096, out_features=11, bias=True)
  )
)

I have created a Custom Dataset that returns Tensors. I pass it through the model and calculate loss. I am using Cross Entorpy Loss for multi-label classification, and using adam optimizer. Though, my accuracy value are only coming around 10%. The dataset is quite large.
I have played around with the learning rate to no use… Any pointers are much appreciated.
Thank you.