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README.md
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# face2
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## Model Overview
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**Architecture:** YOLOv11
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**Training Epochs:** 75
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**Batch Size:** 32
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**Optimizer:** auto
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**Learning Rate:** 0.0005
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**Data Augmentation Level:** Moderate
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## Training Metrics
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- **mAP@0.5:** 0.99373
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## Class IDs
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| Class ID | Class Name |
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|----------|------------|
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| 0 | Face |
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## Datasets Used
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- face-detection-mik1i_v24
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- faces-bfigz_v3
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- head-qug6h_v2
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## Class Image Counts
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| Class Name | Image Count |
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|------------|-------------|
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| Face | 15349 |
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## Description
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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.
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## Usage
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To use this model for inference, follow the instructions below:
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```python
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from ultralytics import YOLO
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# Load the trained model
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model = YOLO('face2.pt')
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# Perform inference on an image
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results = model('path_to_image.jpg')
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# Display results
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results.show()
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