facebookresearch/mae

PyTorch code and models for the DINOv2 self-supervised learning method.

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

Reference PyTorch implementation and models for DINOv3

Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"

Datasets, Transforms and Models specific to Computer Vision

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.