Ironside35/Rice_VGG16_Model.keras

🌾 VGG16 Transfer Learning Rice Classifier

This is the 6th application in my series, showcasing the power of Transfer Learning using the VGG16 architecture.

🧠 Model Strategy

• —Backbone: VGG16 (Pre-trained on ImageNet).
• —Strategy: Frozen convolutional base with a custom-trained dense head for specific rice variety identification.
• —Epochs: Only 3 (Proving the efficiency of pre-trained feature extractors).

📊 Key Metrics

• —Training Accuracy: %99.06.
• —Validation Accuracy: %99.00.
• —Efficiency: Achieved peak performance in just 3 training cycles.

🎯 Conclusion

VGG16 demonstrated superior feature extraction capabilities for biological textures, reaching over 99% accuracy almost instantly compared to traditional CNN approaches.