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raoyongming avatar

raoyongming/DynamicViT

0
View on GitHub↗
664 stars·83 forks·Jupyter Notebook·MIT·9 viewsdynamicvit.ivg-research.xyz↗

DynamicViT

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

Features

  • Efficient Vision Architectures - Reduces computation through dynamic token sparsification.
  • Efficient Vision Transformers - Uses dynamic token sparsification to accelerate inference speed.
  • Hybrid Vision Architectures - Dynamic spatial sparsification for efficient vision model inference.
  • Vision Transformers - Efficient vision transformers using dynamic token sparsification.

Star history

Star history chart for raoyongming/dynamicvitStar history chart for raoyongming/dynamicvit

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with DynamicViT

These projects share indexed features with DynamicViT. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • microsoft/creammicrosoft avatar

    microsoft/Cream

    1,836View on GitHub↗

    This is a collection of our NAS and Vision Transformer work.

    Python
    View on GitHub↗1,836
  • ibm/crossvitIBM avatar

    IBM/CrossViT

    419View on GitHub↗

    Official implementation of CrossViT. https://arxiv.org/abs/2103.14899

    Python
    View on GitHub↗419
  • facebookresearch/convitfacebookresearch avatar

    facebookresearch/convit

    474View on GitHub↗

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

    Python
    View on GitHub↗474
  • facebookresearch/deitfacebookresearch avatar

    facebookresearch/deit

    4,348View on GitHub↗

    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

    Python
    View on GitHub↗4,348
Compare all 30 related projects→

Frequently asked questions

What does raoyongming/dynamicvit do?

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

What are the main features of raoyongming/dynamicvit?

The main features of raoyongming/dynamicvit are: Efficient Vision Architectures, Efficient Vision Transformers, Hybrid Vision Architectures, Vision Transformers.

Which projects share features with raoyongming/dynamicvit?

Projects with overlapping indexed features include: microsoft/cream — This is a collection of our NAS and Vision Transformer work. 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… ibm/crossvit — Official implementation of CrossViT. https://arxiv.org/abs/2103.14899. ofsoundof/localvit — This repository contains the PyTorch training and evaluation code for LocalViT.