# facebookresearch/ConvNeXt

Code release for ConvNeXt model

Repository: https://github.com/facebookresearch/ConvNeXt
Canonical: https://ross.abutalabs.com/products/convnext
Language: Python
License: MIT
License Family: permissive
Archived: true
Last push: 2023-01-08T14:23:08+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": 1701, "days_push": 1333, "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 6416, forks 745 (observed 2026-08-28T04:09:43.314292+00:00)

## What it is
Official PyTorch implementation of ConvNeXt, a pure convolutional neural network architecture from the CVPR 2022 paper 'A ConvNet for the 2020s'. It includes training, fine-tuning, and downstream transfer code plus pre-trained ImageNet-1K and ImageNet-22K model checkpoints.

## Use cases
- classify images with a modern pure ConvNet
- train ConvNeXt on ImageNet-1K
- fine-tune ConvNeXt on a custom dataset like CIFAR
- use ConvNeXt as a backbone for object detection or segmentation
- download pre-trained ConvNeXt checkpoints for transfer learning
- compare ConvNet vs Vision Transformer performance

## When to choose
- you need a strong, efficient convolutional image classification backbone
- you want pre-trained ImageNet models for detection or segmentation transfer
- you prefer ConvNet architectures over transformers for vision tasks

## When to avoid
- you need a Vision Transformer instead of a ConvNet
- you want a maintained production inference library rather than research code
- you need non-PyTorch frameworks like TensorFlow or JAX

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, computer-vision
- domain: deep-learning, computer-vision, image-processing, machine-learning
- platform: python
- tags: convnext, convnet, image-classification, pytorch, pretrained-models, research-code, facebook-research, gpu

## Member repositories
- facebookresearch/ConvNeXt (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:43.314292+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 2026-08-29T17:44:54.924274+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/ConvNeXt (fetched 2026-08-28T04:09:43.314292+00:00, sha f48f9deb236e)
- Data as of 2026-08-30T08:39:29.467469+00:00.