# david-gpu/srez

Image super-resolution through deep learning

Repository: https://github.com/david-gpu/srez
Canonical: https://ross.abutalabs.com/products/srez
Language: Python
License: MIT
License Family: permissive
Archived: true
Last push: 2017-08-16T10:48:45+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3659, "days_push": 3304, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5270, forks 654 (observed 2026-08-28T04:09:14.003679+00:00)

## What it is
A deep learning project that performs 4x image super-resolution on 16x16 images using a DCGAN-based architecture with ResNet generator modules, trained on the CelebA face dataset. It is a research/demo codebase written in Python with TensorFlow.

## Use cases
- upscale low-resolution face images with deep learning
- train a GAN for image super-resolution
- generate plausible 64x64 faces from 16x16 inputs
- experiment with DCGAN architectures in TensorFlow
- reproduce super-resolution results on the CelebA dataset

## When to choose
- you want a compact, readable reference implementation of GAN-based super-resolution
- you are working with well-lit, frontal face images similar to CelebA
- you want to learn how adversarial plus L1 loss improves upscaling

## When to avoid
- you need a maintained, production-ready super-resolution tool
- you need general-purpose image upscaling beyond frontal faces
- you need modern TensorFlow 2.x or PyTorch compatibility

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, deep-learning, image-processing, llm-training
- domain: machine-learning, computer-vision, image-processing, deep-learning
- platform: python, windows
- tags: super-resolution, gan, dcgan, tensorflow, face-images, celeba, image-upscaling, gpu, linux, macos

## Member repositories
- david-gpu/srez (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:14.003679+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 2026-08-29T17:59:28.380835+00:00, confidence not recorded.
  - readme: https://github.com/david-gpu/srez (fetched 2026-08-28T04:09:14.003679+00:00, sha 737ca33c2284)
- Data as of 2026-08-30T08:39:29.467469+00:00.