# ViT fine-tuned model

An image classification model based on Google Vision Transformer (ViT) architecture, specifically fine-tuned for the beans dataset

A fine-tuned tomato leaf disease classification model based on Google Vision Transformer (ViT) architecture, achieving 99.67% accuracy on the evaluation set

A facial expression recognition model fine-tuned based on a pre-trained model, which can effectively recognize eight different facial expressions.

This model is a fine-tuned image classification model based on Google's ViT-base-patch16-224 on an image folder dataset, achieving 89.21% accuracy on the validation set.

A fine-tuned emotion image classification model based on Google's ViT model, achieving an accuracy of 59.38% on the validation set.

An image classification model fine-tuned on the cats_vs_dogs dataset using Google's ViT model, designed to distinguish between images of cats and dogs.

Fine-tuned based on Google's ViT model for fecal image classification with an accuracy of 94.17%

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

A vision model fine-tuned based on google/vit-base-patch16-224, achieving an F1 score of 0.9931 on the evaluation set

A vision model based on the ViT architecture for detecting watermarks in images

An image classification model based on Google's Vision Transformer architecture, fine-tuned specifically for content moderation tasks, achieving 90.43% accuracy on the test set

A vision Transformer model fine-tuned based on google/vit-base-patch16-224, suitable for image classification tasks

Fine-tuned version based on Google's ViT model, achieving 99.4% validation accuracy on image classification tasks

A vision model fine-tuned based on google/vit-base-patch16-224, specific purpose not clearly stated

Vision Transformer model fine-tuned on flower image dataset based on Google's ViT model

A tomato disease image classification model based on Vision Transformer architecture, achieving 99.18% accuracy on the evaluation set

A tomato disease image classification model based on Google Vision Transformer (ViT) architecture with 98.87% accuracy

This is an image classification model fine-tuned based on google/vit-base-patch16-224-in21k, specifically designed for plant seedling classification tasks, achieving 95.67% accuracy on the test set.

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

A pre-trained model based on Google's Vision Transformer (ViT) architecture, fine-tuned on the EuroSAT dataset, suitable for remote sensing image classification tasks.

A vision Transformer model fine-tuned on a custom image dataset based on Google's ViT model, achieving an evaluation accuracy of 97.7%

An image classification model based on ViT architecture, fine-tuned on the FER2013 dataset for facial expression recognition tasks.

A facial expression recognition model fine-tuned based on Google's ViT model, trained on the FER2013 dataset with an accuracy of 70.22%.

An image classification model fine-tuned based on google/vit-base-patch16-224-in21k, trained on an image folder dataset with an evaluation accuracy of 71.79%.

Image classification model fine-tuned based on google/vit-base-patch16-224-in21k, achieving 91% accuracy on the test set

An image classification model based on Google's Vision Transformer (ViT) architecture, fine-tuned on the EuroSAT dataset for satellite image classification tasks

A cat and dog image classification model fine-tuned based on Google Vision Transformer (ViT) architecture, achieving 99% accuracy on the test set

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

Image classification model fine-tuned on CIFAR-10 dataset based on ViT architecture

This model is a fine-tuned version of google/vit-base-patch16-224-in21k, designed for image classification tasks to distinguish between cats and dogs.

A vision Transformer model fine-tuned based on Google's ViT foundation model, suitable for image classification tasks

Image classification model based on ViT architecture, specifically fine-tuned for trash classification tasks

A vision Transformer model fine-tuned based on google/vit-base-patch16-224, with unspecified specific use case and training data

Vision Transformer model based on ViT architecture, achieving 98.89% accuracy after fine-tuning on image classification tasks

An image classification model fine-tuned based on Google's ViT-base model, achieving an accuracy of 87.98% on the evaluation set.

Image classification model based on Google Vision Transformer (ViT) architecture, fine-tuned on image folder dataset with 87.06% accuracy

Vision Transformer model fine-tuned from google/vit-base-patch16-224, suitable for image classification tasks

This model is a fine-tuned Vision Transformer based on google/vit-base-patch16-224-in21k on an unknown dataset, primarily used for image classification tasks.

This model is a fine-tuned Vision Transformer based on google/vit-base-patch16-224-in21k on an unknown dataset, primarily used for image classification tasks.

High-precision image classification model fine-tuned based on Google's ViT-base model, achieving 91.81% accuracy on the evaluation set