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---
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:
[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 |