Open-source alternatives to Davit

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

A 3D medical Detection Transformer library. Papers accepted @ MIDL22 & MELBA23/02.

GLiT: Neural Architecture Search for Global and Local Image Transformer Code for GLiT: Neural Architecture Search for Global and Local Image Transformer accepted by ICCV2021

This is a PyTorch implementation of CoAtNet specified in "CoAtNet: Marrying Convolution and Attention for All Data Sizes", arXiv 2021.

This is a PyTorch implementation of MobileViT specified in "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer", arXiv 2021.

SCIS 22 TransCrowd: Weakly-Supervised Crowd Counting with Transformers

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.

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

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

LeViT a Vision Transformer in ConvNet's Clothing for Faster Inference

Official Code Release for Container : Context Aggregation Network

Dual Stream Network

Scenic is a research framework designed for the development and training of deep learning models, with a specific focus on computer vision and multimodal transformer architectures. It provides a comprehensive toolkit for defining neural network structures, managing large-scale data pipelines, and executing training workflows across distributed hardware environments. The framework is built upon a functional programming paradigm that utilizes hardware-agnostic tensor abstractions and just-in-time compilation to maximize computational efficiency. By employing modular layer composition, it allows