# ResNet Fine-tuning

This model is a fine-tuned version of microsoft/resnet-50 on an unknown dataset, primarily used for image classification tasks, achieving an accuracy of 84.86% on the evaluation set.

A font recognition model fine-tuned on ResNet-18, achieving 78.1% accuracy on the test set

This is a ResNet model fine-tuned on a Hebrew handwriting dataset for the classification task of Hebrew handwritten characters.

An OCR model fine-tuned from facebook/detr-resnet-50 for object detection tasks

A face classification model fine-tuned based on ResNet-50, achieving an accuracy of 94.16% on the evaluation set

A face classification model fine-tuned based on Microsoft ResNet-50, achieving 99.84% accuracy on the validation set

A brain tumor image classification model fine-tuned based on microsoft/resnet-50, achieving an accuracy of 91.71% on the evaluation set

An image classification model fine-tuned based on microsoft/resnet-18, trained on the fl_image_category_ds dataset

A meme classification model fine-tuned based on Microsoft's ResNet-50 architecture, achieving 64.5% accuracy on image classification tasks

An image classification model fine-tuned on image folder dataset based on Microsoft's ResNet-50 model, achieving 97.64% accuracy

An image classification model fine-tuned on the fashion_mnist_quality_drift dataset based on ResNet-50 architecture, with 73% accuracy

An image classification model fine-tuned on the EuroSAT dataset based on the ResNet-18 architecture

A dog food image classification model fine-tuned based on the ResNet-18 architecture, achieving 89.6% accuracy on the lewtun/dog_food dataset.