| # Guns-100-11m | |
| ## Model Overview | |
| **Architecture:** YOLOv11 | |
| **Training Epochs:** 100 | |
| **Batch Size:** 32 | |
| **Optimizer:** auto | |
| **Learning Rate:** 0.0005 | |
| **Data Augmentation Level:** Moderate | |
| ## Training Metrics | |
| - **[email protected]:** 0.85476 | |
| ## Class IDs | |
| | Class ID | Class Name | | |
| |----------|------------| | |
| | 0 | Gun | | |
| | 1 | Knife | | |
| ## Datasets Used | |
| - gun-detection-xwosb_v8 | |
| - gun-sqvhj_v1 | |
| - people_with_guns_2-pvcnk_v9 | |
| - police1_v1 | |
| - soldier-wclp7_v1 | |
| - test-e46au_v1 | |
| - weapon-rl8c4_v3 | |
| - weapondetection-fdzlo_v4 | |
| - weapons-detection-x9fnq_v3 | |
| - wepon_detection_v1 | |
| ## Class Image Counts | |
| | Class Name | Image Count | | |
| |------------|-------------| | |
| | Gun | 44219 | | |
| | Knife | 4038 | | |
| ## Description | |
| This model was trained using the YOLOv11 architecture on a custom dataset. The training process involved 100 epochs with a batch size of 32. The optimizer used was **auto** with an initial learning rate of 0.0005. Data augmentation was set to the **Moderate** level to enhance model robustness. | |
| ## Usage | |
| To use this model for inference, follow the instructions below: | |
| ```python | |
| from ultralytics import YOLO | |
| # Load the trained model | |
| model = YOLO('best.pt') | |
| # Perform inference on an image | |
| results = model('path_to_image.jpg') | |
| # Display results | |
| results.show() | |