# DingXiaoH/RepVGG

RepVGG: Making VGG-style ConvNets Great Again

Repository: https://github.com/DingXiaoH/RepVGG
Canonical: https://ross.abutalabs.com/products/repvgg
Language: Python
License: MIT
License Family: permissive
Last push: 2023-02-10T08:29:25+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2071, "days_push": 1300, "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 3478, forks 433 (observed 2026-08-28T04:08:06.188551+00:00)

## What it is
RepVGG is a PyTorch implementation of the VGG-style ConvNet architecture from the CVPR 2021 paper, achieving over 84% top-1 ImageNet accuracy via structural re-parameterization. It includes pretrained models, training code, train-to-inference model conversion, and a semantic segmentation example.

## Use cases
- train a fast VGG-style image classification model on ImageNet
- convert a multi-branch training model into a deployable single-branch inference model
- use pretrained RepVGG weights as a backbone for semantic segmentation
- benchmark ConvNets against visual transformers like Swin
- integrate re-parameterized backbones into detection models like YOLOv6/YOLOv7

## When to choose
- you need high-throughput image classification with simple VGG-like architecture
- you want pretrained ConvNet backbones for downstream vision tasks
- you want to apply structural re-parameterization for faster inference

## When to avoid
- you need transformer-based vision models
- you need a framework other than PyTorch without community ports
- you need actively developed features beyond the released models

## Facets
- artifact type: library
- maturity: maintenance
- function: deep-learning, machine-learning, image-processing
- domain: computer-vision, deep-learning, image-processing
- platform: python
- tags: pytorch, repvgg, model-zoo, re-parameterization, image-classification, semantic-segmentation, pretrained-models, convnet, gpu

## Member repositories
- DingXiaoH/RepVGG (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:06.188551+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 2026-08-29T18:36:29.060062+00:00, confidence not recorded.
  - readme: https://github.com/DingXiaoH/RepVGG (fetched 2026-08-28T04:08:06.188551+00:00, sha 67bb00e61eed)
- Data as of 2026-08-30T08:39:29.467469+00:00.