# ViT Fine-tuned Model

A pet breed classification model fine-tuned based on Google Vision Transformer (ViT), achieving 94.45% accuracy on the Oxford-IIIT Pets dataset

Fine-tuned version of Google's ViT model for cat emotion classification

An image classification model fine-tuned on the BrainMRI dataset based on Google's ViT base model

Fine-tuned based on Google's ViT model for accommodation room image classification, achieving 87.5% accuracy on the validation set

Image classification model fine-tuned based on google/vit-base-patch16-224-in21k, suitable for plant classification tasks

A plant image classification model based on the ViT architecture, achieving 99.95% validation accuracy after fine-tuning on an unknown dataset

This model is a fine-tuned version of Google's ViT-base model on a chest X-ray classification dataset, designed for medical image analysis.

A vision Transformer model fine-tuned on a flower image dataset based on Google's ViT model

An image classification model based on ViT architecture, fine-tuned on a game advertisement dataset

A vision Transformer model fine-tuned on a flower image dataset based on Google's ViT model, suitable for image classification tasks.

This is a medical image classification model based on the ViT architecture, specifically designed for breast composition analysis, fine-tuned on the preprocessed 1024 configuration dataset.

Art style classification model based on ViT architecture, capable of identifying the art style category of input images

This model is a fine-tuned version based on the Vision Transformer architecture, primarily used for algae image classification tasks, achieving an accuracy of 62.09% on the evaluation dataset.

A fine-tuned model based on Google's Vision Transformer (ViT) architecture, specifically designed for algae image classification tasks

A fine-tuned human activity recognition model based on Google Vision Transformer (ViT) for image classification tasks

This model is a fine-tuned image classification model based on Google's ViT architecture on the beans dataset, achieving an accuracy of 97.74%.

A binary classification model based on ViT architecture for distinguishing between car and motorcycle images, achieving 99.38% accuracy

This is a binary classification model fine-tuned based on the ViT architecture, designed to distinguish images of Bart Simpson and Homer Simpson.

A cat vs. dog classification model based on CLIP-ViT-Large, achieving 60.95% accuracy on the evaluation set

An image classification model fine-tuned on the beans dataset based on Google's ViT model, used to identify the health status of bean leaves

A high-precision image classification model fine-tuned on bean datasets based on Google's ViT model

A Vision Transformer model fine-tuned on an image folder dataset based on Google's ViT base model

An image classification model based on ViT architecture for identifying various fictional creature characters

This model is an image classification model fine-tuned on the sudo-s/herbier_mesuem7 dataset based on google/vit-base-patch16-224-in21k, achieving an evaluation accuracy of 97.31%.

Image classification model fine-tuned on the herbier_mesuem5 dataset based on google/vit-base-patch16-224-in21k

The first Vision Transformer model applied to NASA SDO mission data for solar active region classification

This model is a fine-tuned image classification model based on google/vit-base-patch16-224-in21k on the sudo-s/herbier_mesuem1 dataset, achieving an accuracy of 72.24%.

A chest X-ray pneumonia detection model fine-tuned based on the Vision Transformer (ViT) architecture, achieving 95.51% accuracy in pneumonia detection tasks.

A ViT-based chest X-ray pneumonia classification model, fine-tuned on pneumonia dataset with 90.06% accuracy

This is a vision Transformer model based on Google's ViT base model, fine-tuned on the CIFAR10 dataset for image classification tasks.

A Pokemon image classification model based on the Vision Transformer (ViT) architecture, capable of recognizing different types of Pokemon.