facebookresearch/moco-v3

Health v2 · maintenance only

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Adoption not part of the score

1323 stars · 174 forks observed · 2026-08-28

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

A PyTorch implementation of MoCo v3, a self-supervised contrastive learning method for ResNet and Vision Transformer (ViT) models. It includes training code, configs, and pre-trained models based on ImageNet-1k pre-training.

Use cases

• pretrain vision transformers with self-supervised learning
• reproduce MoCo v3 paper results on ImageNet
• get pretrained ResNet or ViT backbones without labels
• run contrastive pretraining on GPUs instead of TPUs
• evaluate self-supervised models with linear probing or fine-tuning

When to choose

• you need self-supervised pretrained ViT or ResNet backbones
• you want to reproduce or build on MoCo v3 research
• you have large-scale GPU resources for ImageNet-scale pretraining

When to avoid

• you need a maintained production library rather than research code
• you lack multi-GPU resources for large-batch contrastive training
• you need self-supervised learning for non-image modalities

Facets

library · maturity maintenance

machine-learning deep-learning llm-training machine-learning deep-learning computer-vision image-processing python self-supervised-learning vision-transformer resnet pytorch imagenet contrastive-learning pretrained-models research-code gpu linux

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