Transfer learning usin pytorch
style transfer uses the features found in the 19-layer VGG Network, which is comprised of a series of convolutional and pooling layers, and a few fully-connected layers. In the image below, the convolutional layers are named by stack and their order in the stack. Conv_1_1 is the first convolutional layer that an image is passed through, in the first stack. Conv_2_1 is the first convolutional layer in the second stack. The deepest convolutional layer in the network is conv_5_4.
Style transfer relies on separating the content and style of an image. Given one content image and one style image, we aim to create a new, target image which should contain our desired content and style components:
- objects and their arrangement are similar to that of the content image
- style, colors, and textures are similar to that of the style image
In this notebook, we'll use a pre-trained VGG19 Net to extract content or style features from a passed in image. We'll then formalize the idea of content and style losses and use those to iteratively update our target image until we get a result that we want. You are encouraged to use a style and content image of your own and share your work on Twitter with @udacity; we'd love to see what you come up with!