# tensorlayer/SRGAN

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Repository: https://github.com/tensorlayer/SRGAN
Canonical: https://ross.abutalabs.com/products/srgan
Homepage: https://github.com/tensorlayer/tensorlayerx
Language: Python
License Family: other
Topics: tensorlayer, tensorflow, super-resolution, gan, cnn, srgan, vgg16, vgg19, vgg
Last push: 2024-02-22T02:05:20+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3422, "days_push": 924, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3466, forks 811 (observed 2026-08-28T04:08:05.816338+00:00)

## What it is
Reference implementation of SRGAN, a generative adversarial network for photo-realistic single image super-resolution, built on TensorLayerX/TensorFlow. It includes training scripts, VGG19 perceptual loss, and DIV2K dataset support.

## Use cases
- upscale low-resolution images with a GAN
- train an SRGAN super-resolution model
- reproduce the SRGAN paper
- image super-resolution with VGG perceptual loss
- enhance photo resolution using deep learning

## When to choose
- you want a faithful SRGAN reference implementation
- you already use TensorLayerX or TensorFlow
- you want to train super-resolution on DIV2K or custom images

## When to avoid
- you need a production-ready super-resolution library
- you need a maintained project with a license
- you use PyTorch exclusively without TensorLayerX

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, computer-vision
- domain: computer-vision, image-processing, deep-learning
- platform: python, cross-platform
- tags: super-resolution, gan, srgan, tensorlayerx, vgg19, example-code, gpu

## Member repositories
- tensorlayer/SRGAN (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:05.816338+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 2026-08-29T18:36:52.280488+00:00, confidence not recorded.
  - readme: https://github.com/tensorlayer/SRGAN (fetched 2026-08-28T04:08:05.816338+00:00, sha c6a6480eed5d)
  - homepage: https://github.com/tensorlayer/tensorlayerx (fetched 2026-08-29T09:30:46.663014+00:00, sha 469f2bdb4d1a)
- Data as of 2026-08-30T08:39:29.467469+00:00.