# Food Image Classification

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

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

This model is used to classify common refrigerator items, supporting recognition of multiple food and beverage categories.

Food image classification model based on Google Vision Transformer (ViT) architecture, fine-tuned on the Food101 dataset with an accuracy of 90.9%

This is a food classification model fine-tuned on the food101 dataset based on Google's ViT model, achieving an accuracy of 89.5%.

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

Food image classification model based on ViT architecture, fine-tuned on the Food101 dataset with an accuracy of 96%

Food image classification model based on ViT architecture, fine-tuned on the Food101 dataset with an accuracy of 89.7%

A food classification model based on Google Vision Transformer (ViT) architecture, fine-tuned on an unknown dataset

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 a Vision Transformer (ViT) fine-tuned on the Food-101 dataset, statically quantized to INT8 using the Optimum tool and exported in OpenVINO Intermediate Representation format, suitable for efficient image classification tasks.

This is a food image classification model trained on the food101 dataset using AutoTrain, achieving an accuracy of 89%

This is a fine-tuned Indian food image classification model based on Google's ViT model, achieving an accuracy of 93.3%.

An image classification model fine-tuned on the Food101 dataset based on the Swin Transformer architecture, achieving an accuracy of 92.14%

A food image classification model fine-tuned based on the Swin Transformer architecture, achieving 92.1% accuracy on the Food101 dataset

An image classification model fine-tuned on the Food101 dataset based on Google's ViT model, achieving an accuracy of 85.39%

This is an image classification model generated based on PyTorch and HuggingPics, with an accuracy rate of 90.18%.

An image classification model based on the PyTorch framework, capable of distinguishing between hot dogs and sandwiches.