Projects sharing features with LeViT

We propose a conditional positional encoding (CPE) scheme for vision Transformers. Unlike previous fixed or learnable positional encodings, which are pre-defined and independent of input tokens, CPE is dynamically generated and conditioned on the local neighborhood of the input tokens. As a…

DeiT is a PyTorch vision transformer framework designed for image classification. It implements a transformer-based architecture that processes images as sequences of flattened patches using self-attention layers and position-aware sequence modeling instead of convolutional filters. The project focuses on data-efficient training through a knowledge distillation framework. This system allows a student model to mimic the soft labels of a high-performance teacher model to improve accuracy and generalization, particularly when training on smaller datasets. The library covers the full development

CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped, CVPR 2022

Xiaowei Hu^, Min Shi^, Weiyun Wang^, Sitong Wu^, Linjie Xing, Wenhai Wang, Xizhou Zhu, Lewei Lu, Jie Zhou, Xiaogang Wang, Yu Qiao, and Jifeng Dai*.

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This repository contains PyTorch implementation for DynamicViT (NeurIPS 2021).

This repository contains the official code and pretrained models for CoaT: Co-Scale Conv-Attentional Image Transformers. It introduces (1) a co-scale mechanism to realize fine-to-coarse, coarse-to-fine and cross-scale attention modeling and (2) an efficient conv-attention module to realize…

This is an official implementation of CvT: Introducing Convolutions to Vision Transformers.

GhostNet provides a set of efficient AI model architectures and neural network design patterns designed to reduce computation and memory overhead. It serves as a computer vision backbone and a lightweight vision transformer, optimizing the balance between predictive accuracy and inference speed. The project focuses on reducing resource consumption for deployment on mobile devices and edge hardware. It achieves this through the use of lightweight vision transformer implementations and architectures that minimize the total number of parameters. The codebase covers a range of capabilities for i

This repo contains the official PyTorch code and pre-trained models for the Dynamic Vision Transformer (DVT).

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This repository contains PyTorch code for ConViT. It builds on code from the Data-Efficient Vision Transformer and from timm.

This repo contains the PyTorch version of 8 model definitions (Atto, Femto, Pico, Nano, Tiny, Base, Large, Huge), pre-training/fine-tuning code and pre-trained weights (converted from JAX weights trained on TPU) for our ConvNeXt V2 paper.

ECCV 2022Code for paper "DaViT: Dual Attention Vision Transformer"

DiT is a latent diffusion model and transformer-based generative AI framework implemented in PyTorch. It functions as a class-conditional image generator that replaces traditional convolutional backbones with a transformer architecture to synthesize high-fidelity images. The project utilizes patch-based latent processing and latent space compression to operate on low-dimensional image representations. It incorporates class-conditional guidance and adjustable guidance scales to control the visual content of generated images during the sampling process. The framework covers distributed model t

This is a PyTorch library and framework for self-supervised vision learning. It provides an implementation of masked autoencoders and vision transformers designed to learn image representations by reconstructing masked image patches from unlabeled data. The project features a distributed training pipeline that scales workloads across multiple GPU nodes. This infrastructure includes multi-node orchestration and gradient accumulation to manage large batch sizes and coordinate resource requests across clusters. The toolkit covers a complete workflow from self-supervised masked pre-training to d

Official code Cross-Covariance Image Transformer (XCiT)

This is a pytorch implementation for the Visformer models. This project is based on the training code in DeiT and the tools in timm.