Fine-Grained Food Image Recognition: A Study on Optimising Convolutional Neural Networks for Improved Performance

Keyphrases

• Performance Improvement 100%
• Convolutional Neural Network 100%
• Food Items 100%
• Food Image Recognition 100%
• Mobile Application 40%
• Parameter Tuning 40%
• Validation Accuracy 40%
• DenseNet 40%
• Dropout Rate 40%
• Food Waste 40%
• Training Accuracy 40%
• Food Waste Reduction 40%
• Image Size 40%
• Validation Loss 40%
• Training Loss 40%
• Optimiser 40%
• Computer Vision 20%
• Performance Evaluation 20%
• Multi-class 20%
• Baseline Performance 20%
• Meat 20%
• Existing Technology 20%
• Dairy 20%
• Food Image 20%
• Environmental Sustainability 20%
• Activation Function 20%
• Generalization Ability 20%
• Convolutional Neural Network Architecture 20%
• Pressing Issues 20%
• Resource Conservation 20%
• Performance Training 20%
• Optimized Model 20%
• Comprehensive Exploration 20%
• Environmental Resources 20%
• DenseNet Architecture 20%
• Food Image Dataset 20%
• Custom Dataset 20%
• Parameter Influence 20%
• RMSprop 20%
• ReLU Activation Function 20%
• Food Image Classification 20%

Food Science

• Food Waste 100%