PHD Person + Head Detection — YOLOv11 ONNX

Dual-class detection model (Person Head Detection) based on YOLOv11, exported to ONNX and configured for DeepStream/ONNX Runtime inference. Detects both persons (class 0) and heads (class 1) simultaneously.

Files

File	Description
yolov11_phd_s.onnx	YOLOv11-small ONNX model weights
inference.py	Standalone ONNX Runtime inference script

Model Details

Property	Value
Architecture	YOLOv11-small
Task	Dual-class detection (person + head)
Classes	0 — person, 1 — head
Dataset	CrowdHuman
Input format	BGR, NCHW
Scale factor	0.0039215697906911373 (≈ 1/255)

Running Standalone Inference

Requirements

pip install onnxruntime-gpu opencv-python numpy

For CPU-only:

pip install onnxruntime opencv-python numpy

Usage

Place a test image in the same directory, then:

python inference.py

By default the script reads test_image.jpg, runs inference, and writes output.jpg with bounding boxes drawn.

To change the input image or thresholds, edit the config block at the top of inference.py:

CONF_THRESHOLD = 0.2   # pre-cluster-threshold
IOU_THRESHOLD  = 0.6   # nms-iou-threshold
TOPK           = 300

Output

• Console: detection count, bounding boxes, and confidence scores
• output.jpg: input image with green bounding boxes and labels overlaid