YOLOv11n Face Detection

Yolov11n results

Confusion matrix:

[[23577 2878]

[16098 0]]

Usage

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

model_nms.onnx

onnx · 10.1 MB · SHA-256 de40d3835c79…eceb · Hugging Face

--- 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