# cysmith/neural-style-tf

TensorFlow (Python API) implementation of Neural Style

Repository: https://github.com/cysmith/neural-style-tf
Canonical: https://ross.abutalabs.com/products/neural-style-tf
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: style-transfer, tensorflow, convolutional-neural-network, deep-learning
Last push: 2020-12-24T19:45:09+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3616, "days_push": 2078, "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 3104, forks 808 (observed 2026-08-28T04:07:43.722318+00:00)

## What it is
A TensorFlow implementation of neural style transfer based on Gatys et al.'s convolutional neural network approach, with support for video style transfer, color preservation, multiple styles, and semantic segmentation. It is used as a command-line tool to synthesize images combining the content of one image with the artistic style of another.

## Use cases
- apply an artistic painting style to a photo
- transfer style of an artwork onto images using CNNs
- stylize videos with neural style transfer
- blend multiple art styles into one image
- preserve original colors while transferring style
- recreate Gatys et al. neural style paper results

## When to choose
- you want a proven, scriptable implementation of Gatys-style neural style transfer in TensorFlow
- you need video style transfer or multi-style blending features
- you want to reproduce classic neural style research results

## When to avoid
- you need real-time or fast style transfer models
- you prefer PyTorch or modern maintained frameworks
- you need active development or recent updates

## Facets
- artifact type: cli-tool
- maturity: maintenance
- function: image-processing, deep-learning, machine-learning
- domain: image-processing, deep-learning, computer-vision
- platform: python, cross-platform
- tags: style-transfer, tensorflow, neural-style, artistic-filters, computer-vision, gpu

## Member repositories
- cysmith/neural-style-tf (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:43.722318+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 2026-08-29T18:46:11.014207+00:00, confidence not recorded.
  - readme: https://github.com/cysmith/neural-style-tf (fetched 2026-08-28T04:07:43.722318+00:00, sha 114c8247590b)
- Data as of 2026-08-30T08:39:29.467469+00:00.