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Preprint Link: Escaping the Big Data Paradigm with Compact Transformers
The main features of shi-labs/compact-transformers are: Efficient Vision Architectures, Efficient Vision Transformers, Vision Transformers.
Projects with overlapping indexed features include: ibm/crossvit — Official implementation of CrossViT. https://arxiv.org/abs/2103.14899. 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… facebookresearch/deit — DeiT is a PyTorch vision transformer framework designed for image classification. It implements a transformer-based… microsoft/cream — This is a collection of our NAS and Vision Transformer work. ofsoundof/localvit — This repository contains the PyTorch training and evaluation code for LocalViT.
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 repository contains PyTorch code for ConViT. It builds on code from the Data-Efficient Vision Transformer and from timm.
This is a collection of our NAS and Vision Transformer work.
