A high-performance YOLOv8-nano model specifically trained for firearm detection in images and videos. This model achieves exceptional accuracy with 89.0% mAP@0.5 and is optimized for real-time inference applications in security and surveillance systems.
Model Overview
This model is trained on a comprehensive firearm detection (A custom dataset) containing over 7k high-quality images. The model can detect various types of firearms including pistols, rifles, shotguns, and other weapon types unified under a single "Gun" class for detection of fire-armed weapons
Key Features:
Single-class detection (Gun) for simplified integration.
High accuracy: 89.0% mAP@0.5, 60.2% mAP@0.5-0.95.
Optimized for real-time inference.
Comprehensive training on diverse firearm types.
Robust performance across different lighting and background conditions.
Sample Demo
Here is an example output by the model:
Performance Metrics
The model demonstrates exceptional performance on the validation dataset after 100 epochs of training:
Metric
Value
mAP@0.5
0.890
mAP@0.5-0.95
0.602
Precision
0.864
Recall
0.824
F1-Score
0.84
Training Results
Training and Validation Curves
The training progression over 100 epochs shows consistent improvement across all metrics:
Training Results
The graphs demonstrate:
Loss Reduction: Steady decrease in box_loss, cls_loss, and dfl_loss
Metric Improvement: Consistent increase in precision, recall, mAP50, and mAP50-95
Convergence: Stable performance indicating optimal training completion
Dataset Distribution
The dataset contains 6,800 gun instances across 7,068 training images, with balanced spatial distribution and varied object sizes for robust detection capabilities.
