facebookresearch/ConvNeXt-V2

Health v2 · maintenance only

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Adoption not part of the score

2069 stars · 177 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of ConvNeXt V2, a family of pure convolutional neural network models co-designed with a fully convolutional masked autoencoder (FCMAE) framework and a Global Response Normalization (GRN) layer. It provides model definitions in eight sizes, pre-training and fine-tuning code, and pre-trained ImageNet-1K weights.

Use cases

• classify images with a pretrained ConvNet backbone
• fine-tune ConvNeXt V2 on my own image dataset
• run self-supervised masked autoencoder pre-training on images
• get a lightweight vision model for edge devices
• use a convolutional backbone for downstream vision tasks like detection or segmentation
• compare ConvNet vs Vision Transformer performance on ImageNet

When to choose

• you need a pure-ConvNets image classification model with pretrained weights
• you want a range of model sizes from tiny (3.7M params) to huge (660M params)
• you want to reproduce or build on the ConvNeXt V2 paper's self-supervised FCMAE approach

When to avoid

• you need a non-vision or multimodal model
• you require a permissively licensed library since the license is custom/non-standard
• you want a maintained framework with frequent updates, as this is a research code release

Facets

library · maturity stable

machine-learning deep-learning image-processing computer-vision deep-learning machine-learning python convnext pytorch masked-autoencoder image-classification self-supervised-learning pretrained-models facebook-research gpu

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