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VIT Food101 Image Classifier
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VIT Food101 Image Classifier

Developed by StatsGary
Food image classification model based on Vision Transformer architecture, trained on the Food101 dataset with an accuracy of 93.3%
Image Classification
Transformers
#High-precision food classification#ViT architecture#Food recognition
Downloads 41
Release Time : 1/30/2023

Model Overview

This model is specifically designed for food image classification tasks, capable of recognizing 101 different food categories. Suitable for image recognition needs in scenarios such as catering and health management.

Model Features

High accuracy
Achieves 93.3% classification accuracy on the Food101 test set
Based on ViT architecture
Utilizes the advanced Vision Transformer architecture to effectively capture global image features
Multi-category recognition
Capable of recognizing 101 different food categories

Model Capabilities

Food image classification
Multi-category recognition
High-precision image analysis

Use Cases

Catering industry
Automatic menu recognition
Automatically identifies dish categories by taking photos of food
93.3% accuracy
Health management
Diet record analysis
Automatically identifies and records the types of food consumed by users
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