DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image

A complete easy to follow implementation of Google's Vision Transformer proposed in "AN IMAGE IS WORTH 16X16 WORDS". This pytorch implementation has comments for better understanding.

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This notebook is designed to plot the attention maps of a vision transformer trained on MNIST digits.

Standalone Vision Transformer implementation.

Repository for Master thesis project investigating classification of 3D chest CT scans using Vision Transformer.

A Pytorch implementation of ViT - VisionTransformer

Implementing the Vision Transformer paper from scratch for course project.

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ViT fine-tuning and iterative erasing prediction algorithm using self-attention weights in PyTorch

设计了同ResNet18，ResNet50以及ResNet101，相似参数量的VisionTransformer网络模型，对CIFAR100数据集进行分类。

Vision Transformers that you need.

A self-implemented Vision Transformer in tensorflow

Repository that covers the implementation of the Vision Transformer for HAL Trainings at the NCSA

Deep Fake Detection using Vision Transformer and Neural Network

Utilize Vision Transformer Feature Extraction to enhance features robustness and train these features with YOLO

Experimental removal / shuffling of layers in CLIP ViT + Text Transformer

Vision transformer and CNN implementations for image classification using PyTorch.

This is a warehouse for ViT-Pytorch-model, can be used to train your image-datasets for vision tasks.

Implementations of Vision Transformer, Compact Vision Transformer and Compact Convolutional Transformer in PyTorch

Unofficial implementation of Vision Transformer ViT

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This repository presents a radiodosiomics framework for personalized [¹⁷⁷Lu]Lu-PSMA-617 RLT in mCRPC. It includes feature selection and ML models using clinical, radiomic, and dosiomic features, plus nnU-Net and Swin UNETR DL models with SSL to predict Monte Carlo–based dose rate maps.

VisionTransformer for Tensorflow2

Implementation of Vision Transformer on Image Classification

Implementation of Vision Transformer Including Patch and Positional Embedding for Multi-Class Classification

Synaptic" perception capabilities converge with adaptive, real-time analytics, integrating modalities Here are six technical suffixes for VisionTransformer projects:

Several implementations of a ViT architecture, and trainning on different frameworks.

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