# High-precision ViT

This is a Vision Transformer model fine-tuned on the Food-101 dataset for food image classification tasks.

A pathological image classification and feature extraction model built based on the timm library, which performs well on multiple pathological image datasets.

A fine-tuned Alzheimer's disease MRI image classification model based on Google's ViT base model, achieving 92.6% accuracy

A fine-tuned model based on Google's Vision Transformer (ViT) architecture, specifically designed for distinguishing between blurry and clear images

A skin disease classification model fine-tuned based on the DINOv2 base model, achieving 95.57% accuracy on the ISIC 2018+Atlas Dermatology dataset.

An image classification model fine-tuned on a legume dataset based on Google's ViT model, achieving 98.5% accuracy

This is an image classification model fine-tuned from Google's ViT model on the beans dataset, achieving 98.5% accuracy on the validation set.

A Vision Transformer-based image classification model for identifying vegetation in street view images, achieving an accuracy of 92.9%.

This is an image classification model fine-tuned on a bean dataset based on Google's ViT model, achieving a high accuracy of 99.25% on the validation set.

zoalearn2 is a coral species classification model fine-tuned based on Google's ViT model, capable of identifying 32 of the most popular coral species.

A multi-label image classification model fine-tuned based on Google Vision Transformer, specifically designed to identify content in screenshots from the animated series 'Futurama'.

Food classification model based on ViT architecture, fine-tuned on the Food101 dataset with an accuracy of 98.5%

Food image classification model based on ViT architecture, fine-tuned on the Food101 dataset with an accuracy of 96%

A ViT-based multi-class image classification model for lung and colon cancer, achieving 99.94% accuracy on the evaluation set

This model is a fine-tuned image classification model based on Google's ViT-base-patch16-224-in21k on a rock glacier dataset, achieving an accuracy of 92.58%.

A melanoma image classification model fine-tuned based on Google's ViT-Large model, achieving 82.73% accuracy on the evaluation set

A meme classification model fine-tuned from facebook/deit-base-patch16-224, achieving 84.85% accuracy on the imagefolder dataset

An image classification model based on the ViT architecture, trained for 8 epochs on a specific dataset, achieving an accuracy of 94.99%.

An image classification model fine-tuned on the mriDataSet dataset based on facebook/deit-base-distilled-patch16-224, achieving an accuracy of 99.01%

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