# mingyuliutw/UNIT

Unsupervised Image-to-Image Translation

Repository: https://github.com/mingyuliutw/UNIT
Canonical: https://ross.abutalabs.com/products/mingyuliutw-unit
Language: Python
License: NOASSERTION
License Family: other
Topics: gan, deep-learning, pix2pix, image-translation
Last push: 2021-09-02T20:38:43+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": 3467, "days_push": 1826, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2025, forks 358 (observed 2026-08-28T04:06:06.799606+00:00)

## What it is
UNIT is NVIDIA's research implementation of unsupervised image-to-image translation networks (NIPS 2017), translating images across domains like day-to-night or snowy-to-summery without paired data. It is a research codebase superseded by the Imaginaire reimplementation and licensed CC BY-NC-SA 4.0 for non-commercial use.

## Use cases
- translate images between domains without paired training data
- convert day photos to night scenes
- change weather in street scene images
- translate between dog breeds or cat species
- reproduce NIPS 2017 image translation paper results
- convert synthetic images to realistic ones

## When to choose
- you need the original UNIT method for research reproduction
- you want pretrained models like synthia-to-cityscapes translation
- you're studying unsupervised GAN-based image translation

## When to avoid
- you need a maintained or performant implementation - use NVlabs/imaginaire instead
- you need commercial licensing - the CC BY-NC-SA 4.0 license is non-commercial
- you need multimodal or many-to-many translation - use MUNIT instead
- you need Python 3 support - the code targets Python 2.7

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing
- domain: deep-learning, computer-vision, image-processing
- platform: python
- tags: gan, image-translation, pix2pix, nvidia, research-code, pytorch, linux, gpu

## Member repositories
- mingyuliutw/UNIT (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:06.799606+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 2026-08-30T02:59:46.445099+00:00, confidence not recorded.
  - readme: https://github.com/mingyuliutw/UNIT (fetched 2026-08-28T04:06:06.799606+00:00, sha ef798fb16ba2)
- Data as of 2026-08-30T08:39:29.467469+00:00.