# Transfer Learning

Pretrained model based on Hugging Face Transformers library

This model is based on the transformers library, and its specific functions and uses require further information to determine.

This model is based on the Transformers library, and its specific purpose and functionality require further information for confirmation.

A lightweight text classification model based on DistilBERT for location classification tasks

This model is based on the transformers library, with unspecified specific functionalities

This model is based on the transformers library, and its specific functions and uses require further information for confirmation.

An EfficientNetB4 image classification model fine-tuned on the Food101 dataset, capable of recognizing 101 food categories

Pre-trained model provided by Hugging Face Transformers library, suitable for various NLP tasks

This model is based on the Transformers architecture, and its specific purpose and functionality require further information for confirmation.

Pretrained model provided by Hugging Face Transformers library, supporting multiple natural language processing tasks

This model is based on the transformers library, and its specific purpose and functionality require further information to determine.

Pretrained Transformer models provided by Hugging Face, supporting various NLP tasks

This model is a pre-trained model based on the Transformers architecture. Its specific purpose and functionality require further information for confirmation.

This is a cat and dog image classification model trained using AutoTrain, designed to distinguish between cats and dogs in images.

A plant identification model fine-tuned based on Google Vision Transformer (ViT) architecture, achieving 80.96% accuracy on the evaluation set

A plant disease image classification model based on MobileNetV2 architecture, achieving 97.77% accuracy in image classification tasks

This model is an audio classification model based on the AST architecture and fine-tuned on the GTZAN music classification dataset, achieving an accuracy of 89%

This model is a fine-tuned version of Audio Spectrogram Transformer (AST) on the GTZAN music classification dataset for audio classification tasks, achieving an accuracy of 88%.

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.

This is a Vision Transformer model fine-tuned on the Food-101 dataset for food image classification tasks.

TabuLa-8B is a foundational model for table data prediction (classification and binned regression), built on Meta Llama 3.

This model is a transformers model hosted on the Hugging Face Hub, and its specific functions and uses require further information to be added

The model card information is incomplete and cannot provide specific details

An image feature extraction model based on the EfficientNet-B0 architecture, primarily used for astronomical image analysis

This model is based on the Transformers library, and its specific functions and uses require further information for confirmation.

A brain tumor detection model based on the Swin Transformer architecture, achieving 98.04% accuracy in image classification tasks

This model is a transformers model hosted on Hugging Face Hub. Specific functionalities and use cases require further information.

A sign language classification model fine-tuned based on Google Vision Transformer (ViT), achieving an accuracy of 80.56%

A speech processing model based on the SpeechT5 architecture, fine-tuned for Taiwanese Mandarin

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

An image classification model fine-tuned based on ResNet-50, achieving 96.45% accuracy in landmark recognition tasks

An audio classification model based on the DistilHuBERT architecture fine-tuned on the GTZAN music classification dataset

A contrastive language-audio pretrained bioacoustic audio-text model supporting zero-shot classification and task fine-tuning

A model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, achieving 88% accuracy

An audio classification model based on AST architecture, fine-tuned on the GTZAN dataset for music genre classification tasks

This model is a fine-tuned audio classification model based on MIT/ast-finetuned-audioset-10-10-0.4593, achieving 60.76% accuracy on the evaluation set.

A vision Transformer model fine-tuned on a flower image dataset based on Google's ViT model

High-precision fruit image classification model fine-tuned on the Fruits-360 dataset based on CLIP ViT-Large