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.
- 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.
- VGG16
- YOLOv5
- YOLOv7
- YOLOv8
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 |
- Download the dataset for custom training
- https://universe.roboflow.com/upc-tf3xi/guns-dataset-j8cz1
- 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