Weapon-Detection

Ensuring effective gun detection is vital in contemporary times to address escalating concerns about public safety and combat the increasing occurrences of crimes.This project aims to enhance gun detection models by fine-tuning various deep learning architectures.

🎯 Goals

• Achieve superior accuracy compared to existing gun detection models.
• Experiment with hyperparameter tuning to find optimal configurations.
• Focus on reducing false positive predictions while increasing precision.
• Develop a robust model that can adapt to different scenarios and perform well in various environments.

Architectures Used

• VGG16
• YOLOv5
• YOLOv7
• YOLOv8

✨ Model Performance Comparison

In this section, we compare the performance of all four models on the test set

Model	Precision	Recall	F1 Score	mAP_0.5	Test Time
Yolov5s	0.89	0.77	0.83	0.84	10s
Yolov7	0.81	0.73	0.80	0.80	13s
Yolov8s	0.91	0.75	0.83	0.83	9s
VGG16	0.86	0.85	0.82	0.81	20s

📚 Dataset

• Download the dataset for custom training
• https://universe.roboflow.com/upc-tf3xi/guns-dataset-j8cz1

Download Object Detection Models

• Download the object detection models manyally through this link:
• https://drive.google.com/drive/folders/1o37wWBGuH7HGw9sc2HK1VMCc3maSKQuM?usp=sharing
• Download the weight file and place it in "models" folder