The VGG (Visual Geometry Group) network greatly influenced the design of deep convolutional neural networks. Although there exist architectures with better performance, VGG is still very useful for many applications such as image classification.
Input image size: 32x32 pixels and larger.
On the Peltarion Platform, the pretrained VGG network is implemented in the following snippet:
VGG16 feature extractor. Same as the VGG16 but without the last part of the model.
The 2D Convolutional blocks all have a 3x3 filter (Width x Height). This is the smallest size to capture the notion of left-right, up-down, and center.
The basic idea is that you first create and train an experiment containing a pretrained snippet and some Dense blocks. Only the Dense blocks will be trained in the first experiment.
Then you duplicate this first experiment, set all blocks to trainable and train the new experiment with a really low learning rate.
The method is described in How to use pretrained snippets with weights.
When using pretrained snippets, additonal terms apply: VGG with weights licence.
Karen Simonyan, Andrew Zisserman: Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014.
Stay in the know by signing up for occasional emails with tips, tricks, deep learning insights, product updates, event news and webinar invitations.
We promise not to spam you or share your email with any third party. You can change your preferences at any time. See our privacy policies.
Please check your email inbox account to confirm, set, or update your communication preferences.