Style transfer using Deep Neural Network.
References
This implementation is based off of a combination of:
A Neural Algorithm of Artistic Style by Gatys et al. (2015)
Perceptual Losses for Real-Time Style Transfer and Super-Resolution by Johnson et al. (2016)
Instance Normalization: The Missing Ingredient for Fast Stylization by Ulyanov et al. (2017)
The project borrow some logic from Logan Engstrom’s Fast Style Transfer and Justin Johnson’s Fast Neural Style.
Documentation
Full documentation, including installation and setup guides, can be found at http://stylish.readthedocs.io/en/stable/
1 maintainer
Release files for stylish 0.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| stylish-0.4.0.tar.gz | 20.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| stylish-0.4.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 38.6 kB
Release files / stylish-0.4.0.tar.gz
| Download URL | stylish-0.4.0.tar.gz |
|---|---|
| Size | 20.9 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3
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Release files / stylish-0.4.0-py3-none-any.whl
| Download URL | stylish-0.4.0-py3-none-any.whl |
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| Size | 17.8 kB |
| Tags | Python 3 |
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twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3
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