Overview YOLOv11n-face-detection is a lightweight face-detection model from AdamCodd, built on the YOLOv11 nano architecture and fine-tuned for 225 epochs on the WIDERFACE dataset. It detects faces in images through the Ultralytics YOLO framework and is distributed as a model.pt PyTorch checkpoint. The most important consideration is its target range: it performs well on the WIDERFACE evaluation categories for easy and medium examples, but performance drops on hard examples and the model card states that it works best with frontal or slightly angled faces larger than 20 pixels. The supplied information does not report parameter count, input resolution, training batch size, hardware, inference latency, or VRAM requirements, so those values should not be assumed. Best use cases Face detection in ordinary photographs. The model is suited to finding human faces in images with frontal or slightly angled views. Its WIDERFACE validation AP reaches 0.9420471677096086 on the Easy split and 0.9210357271019756 on the Medium split, which supports use in common portrait, group-photo, and camera-image detection workflows. Lightweight face-presence filtering. A small YOLOv11 nano-based detector can serve as a first-stage filter before a recognition, alignment, or image-processing pipeline. For example, an application can detect whether an image contains a face, crop detected regions, and pass those crops to a separate face-recognition or quality-assessment system. The model performs detection only; it does not identify people. Face cropping for downstream recognition. The detector can provide bounding boxes for a later recognition system. This is useful when the application needs to locate faces before embedding extraction or identity classification. The model card does not provide recognition capability, identity labels, landmarks, or embeddings, so those functions require another model or a fine-tuned downstream system. Moderate-scale crowd and surveillance-image detection. WIDERFACE includes difficult face-detection conditions, and the model reports a Hard validation AP of 0.8099848364072022. That result