Ready-to-use artistic deep learning algorithms
A ready-to-use implementation of various Artistic Deep Learning Algorithms.
- Image Style Transfer Using Convolutional Neural Networks, Gatys et. al, 2016
# It is recommended to install torch/torchvision manually before this command, according to your hardware configuration (see below) pip install neurartist
Please note that the use of a GPU is recommended, as CNN computations are pretty slow on a CPU.
NB for GPU users: pip ships torch/torchvision with the Cuda Toolkit 9.0. If you use a more recent version of the Cuda Toolkit, see the PyTorch website for instructions on PyTorch installation with another version of the toolkit.
# Then see the builtin help for usage details neurartist --help
To be added.
Anaconda is strongly recommended:
conda create python=3.7 --name neurartist_env conda activate neurartist_env # with gpu conda install pytorch torchvision cudatoolkit=<your cudatoolkit version> -c pytorch conda install --file requirements.txt # with cpu conda install pytorch-cpu torchvision-cpu -c pytorch conda install --file requirements.txt
You can then run the main entrypoint directly using:
python -m neurartist --help
Or build and install the wheel file with the --editable flag.
- Implement the remaining parts of the jupyter notebook.
- Semantic segmentation as described in this article as to limit spillovers: different approach than guided gram matrices, but same idea of using spatial guidance channels that describe a semantic segmentation of our images.
- More deep-artistic algorithms.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
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|Filename, size neurartist-0.2-py3-none-any.whl (21.0 kB)||File type Wheel||Python version py3||Upload date||Hashes View|
|Filename, size neurartist-0.2.tar.gz (8.0 kB)||File type Source||Python version None||Upload date||Hashes View|