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microsoft avatar

microsoft/Cream

0
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1,836 Stars·243 Forks·Python·MIT·4 Aufrufe

Cream

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

Features

  • Efficient Vision Architectures - Uses cascaded group attention for memory-efficient processing.
  • Efficient Vision Transformers - Uses cascaded group attention for memory-efficient transformer processing.
  • Hybrid Vision Architectures - Fast pretraining distillation for small-scale vision transformers.
  • Vision Backbones and Classification - Searching and compressing transformers for visual recognition.
  • Vision Transformers - Improves relative position encoding for vision transformers.
  • Model Compression - Compresses vision transformers using weight multiplexing.

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Häufig gestellte Fragen

Was macht microsoft/cream?

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

Was sind die Hauptfunktionen von microsoft/cream?

Die Hauptfunktionen von microsoft/cream sind: Efficient Vision Architectures, Efficient Vision Transformers, Hybrid Vision Architectures, Vision Backbones and Classification, Vision Transformers, Model Compression.

Welche Open-Source-Alternativen gibt es zu microsoft/cream?

Open-Source-Alternativen zu microsoft/cream sind unter anderem: ibm/crossvit — Official implementation of CrossViT. https://arxiv.org/abs/2103.14899. raoyongming/dynamicvit — This repository contains PyTorch implementation for DynamicViT (NeurIPS 2021). facebookresearch/deit — DeiT is a PyTorch vision transformer framework designed for image classification. It implements a transformer-based… facebookresearch/levit — LeViT a Vision Transformer in ConvNet's Clothing for Faster Inference. 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…

Open-Source-Alternativen zu Cream

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

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  • facebookresearch/deitAvatar von facebookresearch

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    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

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  • ibm/crossvitAvatar von IBM

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

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

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