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---
license: apache-2.0
base_model:
- Ultralytics/YOLO11
pipeline_tag: object-detection
tags:
- pytorch
---
## YOLOv11x-Face-Detection
A lightweight face detection model based on YOLO architecture ([YOLOv11 xlarge](https://huggingface.co/Ultralytics/YOLO11)), trained for 100 epochs on the WIDERFACE dataset. It's way more accurate than my [YOLOv11n](https://huggingface.co/AdamCodd/YOLOv11n-face-detection) model, but slower.
It achieves the following results on the evaluation set:
```
==================== Results ====================
Easy Val AP: 0.9629194049702874
Medium Val AP: 0.9519172409689101
Hard Val AP: 0.8800338681974709
=================================================
```
YOLO results:
[Confusion matrix](https://huggingface.co/AdamCodd/YOLOv11x-face-detection/blob/main/confusion_matrix.png):
[[27338 3110]
[12337 0]]
### Usage
```python
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
model_path = hf_hub_download(repo_id="AdamCodd/YOLOv11x-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 |