fast-neural-style-transfer

用pytorch实现的快速风格迁移

快速风格迁移

python neural_style/neural_style.py eval --content-image </path/to/content/image> --model </path/to/saved/model> --output-image </path/to/output/image> --cuda 0

• --content-image: path to content image you want to stylize.
• --model: saved model to be used for stylizing the image (eg: mosaic.pth)
• --output-image: path for saving the output image.
• --content-scale: factor for scaling down the content image if memory is an issue (eg: value of 2 will halve the height and width of content-image)
• --cuda: set it to 1 for running on GPU, 0 for CPU.

训练模型

python neural_style/neural_style.py train --dataset </path/to/train-dataset> --style-image </path/to/style/image> --save-model-dir </path/to/save-model/folder> --epochs 2 --cuda 1

There are several command line arguments, the important ones are listed below

• --dataset: path to training dataset, the path should point to a folder containing another folder with all the training images. I used COCO 2014 Training images dataset [80K/13GB] (download).
• --style-image: path to style-image.
• --save-model-dir: path to folder where trained model will be saved.
• --cuda: set it to 1 for running on GPU, 0 for CPU.