Pytorch Object Localization

This project is a custom trained network for my Deep Learning in Med Imaging Course at Vanderbilt

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    Layer (type)               Output Shape         Param #

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        Conv2d-1         [-1, 64, 256, 256]           1,792
          ReLU-2         [-1, 64, 256, 256]               0
     MaxPool2d-3         [-1, 64, 128, 128]               0
        Conv2d-4        [-1, 128, 128, 128]          73,856
          ReLU-5        [-1, 128, 128, 128]               0
     MaxPool2d-6          [-1, 128, 64, 64]               0
        Conv2d-7          [-1, 256, 64, 64]         295,168
          ReLU-8          [-1, 256, 64, 64]               0
        Conv2d-9          [-1, 256, 64, 64]         590,080
         ReLU-10          [-1, 256, 64, 64]               0
    MaxPool2d-11          [-1, 256, 32, 32]               0
       Conv2d-12          [-1, 512, 32, 32]       1,180,160
         ReLU-13          [-1, 512, 32, 32]               0
       Conv2d-14          [-1, 512, 32, 32]       2,359,808
         ReLU-15          [-1, 512, 32, 32]               0
    MaxPool2d-16          [-1, 512, 16, 16]               0
       Conv2d-17          [-1, 512, 16, 16]       2,359,808
         ReLU-18          [-1, 512, 16, 16]               0
       Conv2d-19          [-1, 512, 16, 16]       2,359,808
         ReLU-20          [-1, 512, 16, 16]               0
    MaxPool2d-21            [-1, 512, 8, 8]               0
    AdaptiveAvgPool2d-22    [-1, 512, 7, 7]               0
       Linear-23                   [-1, 64]       1,605,696
      Sigmoid-24                   [-1, 64]               0
      Dropout-25                   [-1, 64]               0
       Linear-26                    [-1, 2]             130

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The implementation deals with localizing a single cell phone in images.

It is built in a VGG model style with increasing convolutional layers and decreasing dimensions.