# Li-Chongyi/Zero-DCE

Zero-DCE code and model

Repository: https://github.com/Li-Chongyi/Zero-DCE
Canonical: https://ross.abutalabs.com/products/zero-dce
Language: HTML
License Family: other
Last push: 2024-01-12T02:00:12+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": 2418, "days_push": 965, "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 1067, forks 229 (observed 2026-08-28T04:03:27.302218+00:00)

## What it is
PyTorch implementation of Zero-DCE (Zero-Reference Deep Curve Estimation), a CVPR 2020 model that enhances low-light images without paired or reference training data. It includes training/testing scripts, a pretrained snapshot, and a MindSpore variant.

## Use cases
- brighten dark photos with a deep learning model
- low-light image enhancement without reference data
- reproduce Zero-DCE CVPR 2020 results
- train a zero-reference enhancement network
- enhance underexposed images in a research pipeline

## When to choose
- you need research-grade low-light enhancement with a pretrained model
- you want a lightweight, zero-reference enhancement approach
- you're studying or extending CVPR 2020 enhancement methods

## When to avoid
- you need a commercially licensed solution (code is CC BY-NC)
- you need production-maintained software with releases and support
- you need a general-purpose image editing toolkit

## Facets
- artifact type: library
- maturity: maintenance
- function: image-processing, deep-learning, machine-learning
- domain: computer-vision, image-processing, deep-learning
- platform: python
- tags: low-light-enhancement, zero-reference-learning, cvpr-2020, pytorch, research-code, non-commercial-license, gpu, linux

## Member repositories
- Li-Chongyi/Zero-DCE (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:27.302218+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 2026-08-30T06:55:08.874730+00:00, confidence not recorded.
  - readme: https://github.com/Li-Chongyi/Zero-DCE (fetched 2026-08-28T04:03:27.302218+00:00, sha 095d2f1039d2)
- Data as of 2026-08-30T08:39:29.467469+00:00.