# Transfer learning fine-tuning

A crop disease recognition model fine-tuned based on Google Vision Transformer (ViT) architecture with 70% accuracy

Plant seedling classification model based on BEiT architecture, fine-tuned on a balanced dataset with 96% test accuracy

Fine-tuned vision model based on Swin Transformer V2 architecture, excelling in image classification tasks

A visual classification model fine-tuned on an unknown dataset based on Google's ViT model, excelling in image classification tasks

An image classification model fine-tuned based on microsoft/resnet-18, performing excellently on image folder datasets.

A visual classification model fine-tuned on the bean image dataset based on Google's ViT base model, with an accuracy rate of 97.74%

Image classification model based on Vision Transformer architecture, fine-tuned on an unknown dataset

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base for Persian language datasets, primarily used for Persian speech-to-text tasks.

Portuguese biomedical text generation model based on OpenAI GPT-2, trained with transfer learning and fine-tuning techniques