# ViT Architecture

This is a Vision Transformer-based model for classifying facial skin types into 5 categories.

This paper proposes a method to reinterpret Vision Transformer (ViT) as an image segmentation model, demonstrating ViT's potential in image segmentation tasks.

This paper proposes a method to reinterpret Vision Transformer (ViT) as an image segmentation model, revealing ViT's potential in image segmentation tasks.

This paper proposes an image segmentation model based on Vision Transformer (ViT), revealing the potential of ViT in image segmentation tasks.

This paper proposes a novel perspective by treating Vision Transformer (ViT) as an image segmentation model and explores its potential in image segmentation tasks.

This model reveals the potential of Vision Transformer (ViT) in image segmentation tasks by adapting its architecture for segmentation purposes.

This paper proposes a method to reinterpret Vision Transformer (ViT) as an image segmentation model, demonstrating ViT's potential in image segmentation tasks.

By rethinking the architecture of Vision Transformer (ViT), this model demonstrates its potential in image segmentation tasks.

A fine-tuned model based on Vision Transformer (ViT) architecture for classifying chest X-rays, trained on the CheXpert dataset.

C-RADIOv2 is a visual feature extraction model developed by NVIDIA, offering multiple size versions suitable for image understanding and dense visual tasks.

An image classification model based on Vision Transformer architecture, pre-trained on the ImageNet-21k dataset, suitable for multi-category image classification tasks

A plant identification model fine-tuned based on Google Vision Transformer (ViT) architecture, achieving 80.96% accuracy on the evaluation set

Vision Transformer model trained on the LAION-2B dataset, supporting zero-shot image classification tasks

DUSt3R is a model for easily achieving geometric 3D vision from images, capable of reconstructing 3D scenes from single or multiple images.

DUSt3R is a deep learning model for generating 3D geometric models from images, capable of easily handling geometric 3D vision tasks.

DUSt3R is a model for easily achieving geometric 3D vision from images, capable of reconstructing 3D scenes from single or multiple images.

Face recognition model based on keypoint relative position encoding, using ViT architecture and trained on the WebFace12M dataset

A clothing classification model fine-tuned based on Google's ViT model, supporting image classification for 7 clothing categories

Vision Transformer (ViT)-based skin cancer image classification model capable of identifying 7 types of skin lesions

A fashion collection classification model fine-tuned based on Google Vision Transformer (ViT), capable of recognizing clothing collections from 15 top fashion brands.

An image classification model based on Vision Transformer architecture, used to detect real images versus AI-generated fake images.

This model is a fine-tuned image classification model based on Google's ViT-base-patch16-224 on an image folder dataset, achieving an accuracy of 85.76% on the evaluation set.

Driver fatigue detection model based on ViT architecture, fine-tuned on the UTA RLDD dataset with an accuracy of 97.5%

High-precision fruit image classification model fine-tuned on the Fruits-360 dataset based on CLIP ViT-Large

A helicopter image classification model based on the Vision Transformer architecture, capable of identifying different types of helicopters

A ViT model fine-tuned on the preprocessed 1024 configuration dataset for image classification tasks

A fine-tuned image classification model based on ViT architecture, excelling on the FER2013 dataset

This model is a vision model based on the Vision Transformer (ViT) architecture, focusing on the classification task of algae images.

A model fine-tuned based on facebook/deit-small-patch16-224, with no specific use case clearly stated

A fine-tuned cartoon image emotion classification model based on Google Vision Transformer (ViT) architecture, achieving 88% accuracy on the test set

An image classification model fine-tuned on the beans dataset based on Google's ViT model, achieving an accuracy of 96.99%

An image classification model based on Vision Transformer (ViT) architecture, pretrained on the ImageNet-21k dataset, suitable for feature extraction and fine-tuning scenarios.

A Vision Transformer (ViT) based model fine-tuned on the CIFAR-10 dataset for image classification tasks

An Audio Spectrogram Transformer (AST) fine-tuned on the AudioSet dataset, using ViT architecture to process audio spectrograms, achieving excellent performance on multiple audio classification benchmarks.

A visual classification model fine-tuned based on Google's ViT base model, suitable for domain-specific image recognition tasks

Fine-tuned model based on Google Vision Transformer (ViT) architecture, suitable for image classification tasks

A small vision Transformer model based on the YOLOS architecture, fine-tuned specifically for license plate detection tasks

A small-scale Vision Transformer model based on YOLOS architecture, fine-tuned specifically for mask detection tasks, trained on COCO and mask detection datasets

This is an image classification model based on the Vision Transformer architecture, achieving an accuracy of 95.02%.

This model is an image classification model fine-tuned on an image folder dataset based on facebook/deit-tiny-patch16-224