| --- |
| license: openrail++ |
| datasets: |
| - sshao0516/CrowdHuman |
| language: |
| - en |
| base_model: |
| - pyronear/yolov11s |
| pipeline_tag: object-detection |
| tags: |
| - person |
| - head |
| --- |
| # 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 |
|
|
| ```bash |
| pip install onnxruntime-gpu opencv-python numpy |
| ``` |
|
|
| For CPU-only: |
| ```bash |
| pip install onnxruntime opencv-python numpy |
| ``` |
|
|
| ### Usage |
|
|
| Place a test image in the same directory, then: |
|
|
| ```bash |
| 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`: |
|
|
| ```python |
| 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 |
