# andersbll/neural_artistic_style

Neural Artistic Style in Python

Repository: https://github.com/andersbll/neural_artistic_style
Canonical: https://ross.abutalabs.com/products/neural_artistic_style
Language: Python
License: MIT
License Family: permissive
Last push: 2016-10-23T10:30:58+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4019, "days_push": 3601, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2168, forks 472 (observed 2026-08-28T04:06:21.487224+00:00)

## What it is
A Python command-line implementation of the Neural Algorithm of Artistic Style (Gatys et al., 2015) that transfers the style of one image onto the content of another using a pretrained VGG-19 network. It depends on the author's DeepPy and CUDArray libraries with cuDNN for GPU acceleration.

## Use cases
- apply a painting style to a photo
- neural style transfer with vgg19
- turn a picture into a starry night style image
- generate artistic images from two input photos
- run style transfer from the command line

## When to choose
- you want a simple, readable reference implementation of the original Gatys style transfer algorithm
- you want to run style transfer on a CUDA GPU with a pretrained VGG-19 model

## When to avoid
- you need a maintained tool or modern framework support (last release 2016, depends on abandoned DeepPy/CUDArray)
- you need fast or real-time style transfer (iterative optimization is slow)
- you have no NVIDIA GPU with cuDNN

## Facets
- artifact type: cli-tool
- maturity: abandoned
- function: image-processing, deep-learning, cli
- domain: machine-learning, image-processing, computer-vision
- platform: python, windows
- tags: neural-style-transfer, vgg19, cudnn, deeppy, art-generation, gpu, linux, macos

## Member repositories
- andersbll/neural_artistic_style (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:21.487224+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:49:29.667873+00:00, confidence not recorded.
  - readme: https://github.com/andersbll/neural_artistic_style (fetched 2026-08-28T04:06:21.487224+00:00, sha 24d8497a4a22)
- Data as of 2026-08-30T08:39:29.467469+00:00.