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This is a collection of our NAS and Vision Transformer work.
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
This is a pytorch implementation for the Visformer models. This project is based on the training code in DeiT and the tools in timm.
This repository contains PyTorch code for ConViT. It builds on code from the Data-Efficient Vision Transformer and from timm.
Official implementation of CrossViT. https://arxiv.org/abs/2103.14899
The main features of ibm/crossvit are: Efficient Vision Architectures, Efficient Vision Transformers, Vision Backbones and Classification, Vision Transformers.
Open-source alternatives to ibm/crossvit include: microsoft/cream — This is a collection of our NAS and Vision Transformer work. facebookresearch/deit — DeiT is a PyTorch vision transformer framework designed for image classification. It implements a transformer-based… microsoft/vision-longformer — This project provides the source code for the vision longformer paper. facebookresearch/convit — This repository contains PyTorch code for ConViT. It builds on code from the Data-Efficient Vision Transformer and… danczs/visformer — This is a pytorch implementation for the Visformer models. This project is based on the training code in DeiT and the… ofsoundof/localvit — This repository contains the PyTorch training and evaluation code for LocalViT.