README.md

---
license: apache-2.0
base_model:
- Ultralytics/YOLO11
pipeline_tag: object-detection
tags:
- pytorch
---

## YOLOv11n-Face-Detection

A lightweight face detection model based on YOLO architecture ([YOLOv11 nano](https://huggingface.co/Ultralytics/YOLO11)), trained for 225 epochs on the WIDERFACE dataset.

It achieves the following results on the evaluation set:

```
==================== Results ====================
Easy   Val AP: 0.9420471677096086
Medium Val AP: 0.9210357271019756
Hard   Val AP: 0.8099848364072022
=================================================
```

YOLO results:

![Yolov11n results](https://huggingface.co/AdamCodd/YOLOv11-face-detection/resolve/main/result.png)

[Confusion matrix](https://huggingface.co/AdamCodd/YOLOv11-face-detection/blob/main/confusion-matrix.png):

[[23577 2878]

[16098 0]]

### Usage
```python
from huggingface_hub import hf_hub_download
from ultralytics import YOLO

model_path = hf_hub_download(repo_id="AdamCodd/YOLOv11n-face-detection", filename="model.pt")
model = YOLO(model_path)

results = model.predict("/path/to/your/image", save=True) # saves the result in runs/detect/predict
```

### Limitations

- Performance may vary in extreme lighting conditions
- Best suited for frontal and slightly angled faces
- Optimal performance for faces occupying >20 pixels