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Food Vision 101
F
Food Vision 101
Developed by mhamza-007
An EfficientNetB4 image classification model fine-tuned on the Food101 dataset, capable of recognizing 101 food categories
Downloads 39
Release Time : 2/20/2025
Model Overview
This model is a deep learning model based on the EfficientNetB4 architecture, specifically designed for food image classification tasks. Fine-tuned on the Food101 dataset, it can accurately identify images of 101 different food categories.
Model Features
Efficient Image Classification
Based on the EfficientNetB4 architecture, it provides high-accuracy image classification while maintaining computational efficiency.
Specialized for Food Domain
Optimized specifically for 101 food categories, making it suitable for food recognition applications.
Transfer Learning Optimization
Adopts a fine-tuning strategy that unfreezes the last 10 layers, effectively leveraging the knowledge of the pre-trained model.
Model Capabilities
Food Image Classification
Multi-class Recognition
Computer Vision Task Processing
Use Cases
Food Service Industry
Smart Menu Recognition
Automatically identifies food photos taken by users and recommends similar dishes.
74.28% test accuracy
Nutrition Analysis Assistance
Assists in calculating the nutritional content of meals through food recognition.
Health Management
Automated Dietary Logging
Automatically records the types of food consumed by users daily.
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