Comments (1)
They are basically the same except for 2 major things:
- This repo uses the original VGG networks used in the original fast-neural-style paper. In contrast, the Pytorch Team example uses VGG networks from Pytorch Image Model Zoo.
- This repo uses (1) Content Loss, (2) Style Loss, and (3) Total Variation Loss, while Pytorch Team example uses only (1) and (2). Total Variation Loss smooths the pixels of the output image by minimizing the difference of pixel values of adjacent pixels.
TL;DR This repo is a more faithful reproduction of the original fast-neural-style paper, but both this and Pytorch's should more or less have similar results! :)))
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Related Issues (20)
- vgg16_features.load_state_dict(torch.load(vgg_path), strict=False) HOT 4
- "AttributeError: can't set attribute" HOT 2
- VGG weight file not available on AWS HOT 4
- How to run stylize.py HOT 3
- IndentationError
- Value error while trying to run video.py HOT 9
- Custom model HOT 1
- train my own style image HOT 2
- Share style images? HOT 1
- video.py wrong line
- Trying TransformerNetworkTanh and error when running train.py HOT 1
- What is the License schema ? HOT 2
- Train Notebook: Request for Improvements HOT 3
- Fixing the save/load generated images HOT 3
- About content loss HOT 4
- Can you share the pre-trained model? HOT 3
- webcam.py: can't adjust resolution? HOT 3
- new style image HOT 2
- I cannot find 'USE_FFMPEG' in video.py. HOT 4
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