Fine-Tuned Weapon vs. Not-Weapon Classifier (VGG16)
This repository contains a fine-tuned image classification model designed to distinguish between "Weapon" and "Not-Weapon" classes in images. The model was built using Transfer Learning on the VGG16 architecture.
Model Structure and Training
Base Architecture: VGG16 (pre-trained on ImageNet).
Training Method: Transfer Learning (Feature Extraction) followed by Fine-Tuning.
File:weapon_classifier_final_tuned.keras (This is the final, fine-tuned model file).
Input Size: Images must be resized to (224, 224) pixels before prediction.
Output Activation: Sigmoid (yielding a single probability value between 0 and 1).
How to Use the Model (Inference)
To use this model, you need a Python environment with TensorFlow installed.
