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# face2
## Model Overview
**Architecture:** YOLOv11
**Training Epochs:** 75
**Batch Size:** 32
**Optimizer:** auto
**Learning Rate:** 0.0005
**Data Augmentation Level:** Moderate
## Training Metrics
- **[email protected]:** 0.99373
## Class IDs
| Class ID | Class Name |
|----------|------------|
| 0 | Face |
## Datasets Used
- face-detection-mik1i_v24
- faces-bfigz_v3
- head-qug6h_v2
## Class Image Counts
| Class Name | Image Count |
|------------|-------------|
| Face | 15349 |
## Description
This model was trained using the YOLOv11 architecture on a custom dataset. The training process involved 75 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('face2.pt')
# Perform inference on an image
results = model('path_to_image.jpg')
# Display results
results.show()
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