# Transfer learning

This is a transformers model published on the Hugging Face Hub. Specific information is to be supplemented.

This model is an audio classification model based on the Audio Spectrogram Transformer (AST) architecture. After pre-training on the Audioset dataset, it was fine-tuned on the GTZAN music genre classification dataset.

An image classification model fine-tuned based on the ResNet-50 architecture, specifically designed to distinguish between images of cats and dogs with an accuracy of 96.88%.

Geneformer is a Transformer model pre-trained on large-scale single-cell transcriptome data, specifically designed for scenarios with scarce network biology data, enabling context-aware predictions.

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.

An image classification model fine-tuned based on Google Vision Transformer (ViT) architecture, used to distinguish AI-generated images from real 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

This is an audio classification model based on the AST (Audio Spectrogram Transformer) architecture, fine-tuned on the GTZAN music genre classification dataset with an accuracy rate of 92%.

An image classification model fine-tuned from google/vit-base-patch16-224-in21k, achieving 97.74% accuracy on the bean dataset

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

A Vision Transformer model fine-tuned from google/vit-base-patch16-224-in21k, achieving 99.26% accuracy on the evaluation set

This model is a fine-tuned image classification model based on microsoft/beit-base-patch16-224-pt22k-ft22k, achieving an accuracy of 93.59% on the evaluation set.

Pre-trained model based on BEiT architecture, fine-tuned on the cats_vs_dogs dataset for image classification tasks

AASL is an image classification model based on the Creative Commons license, suitable for visual 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%

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%.

A vision Transformer model based on the BEiT architecture, fine-tuned on FER2013CKPlus and SFEW datasets for facial expression recognition tasks.

Model fine-tuned on the Zaid/xquad_ar Arabic QA dataset based on bert-base-multilingual-cased

A model fine-tuned based on bert-base-uncased for aspect-based sentiment analysis tasks

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

This model is a fine-tuned version of ConvNeXt-Tiny based on the image folder dataset, suitable for image classification tasks.

A visual classification model fine-tuned on the CIFAR-10 test set based on the Google Vision Transformer (ViT) model

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

This is an image classification model based on the Vision Transformer (ViT) architecture, fine-tuned on the CIFAR10 dataset, achieving 98.76% accuracy.

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

This model is a fine-tuned version based on Google's ViT (Vision Transformer) architecture on an unknown dataset, potentially for lung-related image analysis tasks.

An image classification model fine-tuned on the ImageFolder dataset based on ResNet-50 architecture, achieving 82.4% accuracy

An image classification model based on Google Vision Transformer (ViT) architecture, fine-tuned on an image folder dataset with an accuracy of 88.55%

An image classification model fine-tuned based on microsoft/beit-base-patch16-224-pt22k-ft22k, achieving 98.92% accuracy on the evaluation set.

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

A question-answering model fine-tuned on Vietnamese QA datasets based on PhoBERT-base

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 image classification model based on google/vit-base-patch16-224-in21k on the Human_Action_Recognition dataset, achieving an accuracy of 93.84%.

A vision classification model fine-tuned based on Google's Vision Transformer (ViT) foundation model, 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.