VGG 19 is a convolutional neural network architecture that is 19 layers deep. The main purpose for which the VGG net was designed was to win the ILSVRC imagenet competition.
Let’s take a brief look at the architecture of VGG19.
- Input: The VGG-19 takes in an image input size of 224×224.
- Convolutional Layers: VGG’s convolutional layers leverage a minimal receptive field, i.e., 3×3, the smallest possible size that still captures up/down and left/right. This is followed by a ReLU activation function. ReLU stands for rectified linear unit activation function, it is a piecewise linear function that will output the input if positive otherwise, the output is zero. Stride is fixed at 1 pixel to keep the spatial resolution preserved after convolution
- Fully-Connected Layers: The VGG19 has 3 fully connected layers. Out of the 3 layers, the first 2 have 4096 nodes each, and the third has 1000 nodes, which is the total number of classes the imagenet dataset has.
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