# Res2Net/Res2Net-PretrainedModels

(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"

Repository: https://github.com/Res2Net/Res2Net-PretrainedModels
Canonical: https://ross.abutalabs.com/products/res2net-pretrainedmodels
Homepage: https://mmcheng.net/res2net/
Language: Python
License Family: other
Topics: res2net, backbone, pytorch, multi-scale, jittor
Last push: 2022-12-08T12:54:57+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": 2617, "days_push": 1364, "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 1114, forks 216 (observed 2026-08-28T04:03:38.165362+00:00)

## What it is
Official PyTorch implementation of Res2Net, a multi-scale CNN backbone architecture published in TPAMI, with ImageNet-pretrained model weights. The Res2Net block adds hierarchical residual-like connections within a single residual block and can be plugged into backbones like ResNet, ResNeXt, and DLA.

## Use cases
- load pretrained res2net backbone for image classification
- use res2net as backbone for object detection in mmdetection
- improve semantic segmentation with a multi-scale cnn backbone
- replace resnet backbone with res2net in detectron2
- fine-tune imagenet pretrained res2net on my own dataset
- compare res2net vs hrnet for detection performance

## When to choose
- you need a strong multi-scale CNN backbone for detection or segmentation
- you want ImageNet-pretrained weights that transfer well to downstream vision tasks
- you use PyTorch, MMDetection, Detectron2, or Jittor and want drop-in Res2Net support

## When to avoid
- you need a maintained library with active development or a license
- you want transformer-based backbones like ViT or Swin
- you need training code in this repo (training lives in a separate repository)

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, computer-vision
- domain: deep-learning, computer-vision, machine-learning
- platform: python
- tags: res2net, pretrained-models, backbone-network, pytorch, imagenet, multi-scale-features, model-zoo, gpu

## Member repositories
- Res2Net/Res2Net-PretrainedModels (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:38.165362+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 2026-08-30T06:42:12.976981+00:00, confidence not recorded.
  - readme: https://github.com/Res2Net/Res2Net-PretrainedModels (fetched 2026-08-28T04:03:38.165362+00:00, sha b01dc00ad62d)
  - homepage: https://mmcheng.net/res2net/ (fetched 2026-08-29T12:46:23.788512+00:00, sha f8c73c06e844)
- Data as of 2026-08-30T08:39:29.467469+00:00.