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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 |