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# 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()
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