✋ This model is not published yet.

You can claim this model if you're @progamergov on GitHub. Contact us.

progamergov / neural-dream

PyTorch implementation of DeepDream algorithm

  • Public
  • 66 runs
  • GitHub
  • License
Run with an API
Playground API Examples README Versions

Readme

neural-dream

This is a PyTorch implementation of DeepDream. The code is based on neural-style-pt.

Options:

  • -image_size: Maximum side length (in pixels) of the generated image. Default is 512.
  • -gpu: Zero-indexed ID of the GPU to use; for CPU mode set -gpu to c.

Optimization options: * -dream_weight: How much to weight DeepDream. Default is 1e3. * -tv_weight: Weight of total-variation (TV) regularization; this helps to smooth the image. Default is set to 0 to disable total-variation (TV) regularization. * -num_iterations: Default is 10.

Layer options: * -dream_layers: Comma-separated list of layer names to use for DeepDream reconstruction.

Channel options: * -channel_mode: The DeepDream channel selection mode; all, strong, avg, weak, or ignore; default is all. The strong option will select the strongest channels, while weak will do the same with the weakest channels. The avg option will select the most average channels instead of the strongest or weakest. The number of channels selected by strong, avg, or weak is based on the first value for the -channels parameter. The ignore option will omit any specified channels.

Other options: * -original_colors: If you set this to 1, then the output image will keep the colors of the content image. * -seed: An integer value that you can specify for repeatable results. By default this value is random for each run.