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YOLOv11n Face Detection

A lightweight face detection model based on YOLO architecture (YOLOv11 nano), trained for 225 epochs on the WIDERFACE dataset.

Sourcehuggingface.co/Tsaicrab/YOLOv11n-face-detection/tree/ab2d17080cabbf70ead944579f473dcb5e86f265
TypeOtherModèle de baseUltralytics/YOLO11VersionRevision ab2d170CClasse

Source du modèle

Type
Other
Taille du fichier
5,11–10,1 MB
Modèle de base
Ultralytics/YOLO11
Version
vRevision ab2d170
Mis à jour
16 sept. 2026
Téléchargements
3
Source
Hugging Face

Description de la source

YOLOv11n-Face-Detection

A lightweight face detection model based on YOLO architecture (YOLOv11 nano), 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

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

Sources

1 source
Hugging FacePrincipalVersion ab2d17080cabbf70ead944579f473dcb5e86f265
huggingface.co/Tsaicrab/YOLOv11n-face-detection/tree/ab2d17080cabbf70ead944579f473dcb5e86f265

Vérifié 2 sept.

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Artefacts du modèle

5 artefacts

model_fp16.onnx

onnx · 5,11 MB · SHA-256 e6408b78c733…87f8 · Hugging Face

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model_gpu.onnx

onnx · 10,0 MB · SHA-256 9ff8375151b7…3dad · Hugging Face

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model_nms.onnx

onnx · 10,1 MB · SHA-256 de40d3835c79…eceb · Hugging Face

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model.onnx

onnx · 10,1 MB · SHA-256 2dfe14171f5b…5719 · Hugging Face

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model.pt

pt · 5,22 MB · SHA-256 f5b3aaa834fd…4c81 · Hugging Face

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Extraits de sources

2 extraits
Readme2 sept.huggingface.co/Tsaicrab/YOLOv11n-face-detection

--- 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

Note2 sept.huggingface.co/Tsaicrab/YOLOv11n-face-detection

Source context: 3 downloads · 0 likes · Pipeline object-detection · Repo Tsaicrab/YOLOv11n-face-detection