# Transfer Learning Fine-tuning

An image classification model fine-tuned on a chest X-ray dataset, achieving 97.13% accuracy

An audio classification model fine-tuned on audio datasets based on MIT/ast-finetuned-audioset-10-10-0.4593

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.

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

A Vision Transformer model fine-tuned on an image folder dataset based on google/vit-base-patch16-224 for image classification tasks.

An image classification model based on Google's Vision Transformer (ViT) architecture, fine-tuned on the food101 dataset for food image classification tasks

This model is an image classification model fine-tuned on the beans dataset based on timm/resnet18.a1_in1k, achieving an accuracy of 69.17%.

Image classification model based on Google Vision Transformer (ViT) architecture, fine-tuned on the EuroSAT dataset

An image classification model fine-tuned on the beans dataset based on the ResNet-50 architecture, achieving an accuracy of 90.23%