Comments (1)
Please have a look at the last part of the model, i.e., the part leading to the classifier. For the VGG-nets, it directly goes from convolution/pooling layers to dense layers, and the first dense layer expects a particular incoming shape that you only receive for input images of 224x224 (and maybe some pixels more or less, if they result in feature maps of the same size later on due to pooling or strides).
In contrast, GoogLeNet has a global average pooling later before the dense layer, so whatever the input image size, the dense layer will see the same incoming shape. That's why it also works for images of a different size.
So to adapt VGG networks to a different size, it's not enough to only change the input layer definition, you also need new weights for the first dense layer (since the weight matrix will need to have a different shape).
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Related Issues (20)
- 3D UNet implementation HOT 7
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