LeapLabTHU/ViTTT

[ICML 2024] Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

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

MambaOut: Do We Really Need Mamba for Vision? (CVPR 2025)

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

PyTorch implementations of recent Computer Vision tricks (ReXNet, RepVGG, Unet3p, YOLOv4, CIoU loss, AdaBelief, PolyLoss, MobileOne). Other additions: AdEMAMix

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

Reference PyTorch implementation and models for DINOv3

Official codebase used to develop Vision Transformer, SigLIP, MLP-Mixer, LiT and more.

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.