---
tags:
- element_type:detect
- model:yolov11-nano
- object:vehicle
manako:
  description: YOLO11n vehicle detector for CCTV surveillance
  input_payload:
  - name: frame
    type: image
    description: RGB frame
  output_payload:
  - name: detections
    type: detections
    description: List of detections
---

# Score Vision SN44 — VehicleDetect Miner

**Wallet:** LukeTao | **Hotkey:** default | **UID:** 128 | **Netuid:** 44

## Model

| Property | Value |
|---|---|
| Architecture | YOLO11-nano |
| Input size | 640×640 |
| Model file | `weights.onnx` |
| Size | ~11 MB (well under 30 MB limit) |
| Framework | ONNX Runtime (CUDA EP) |
| mAP\@50 | **63.05%** (COCO val2017, vehicle classes) |

## Classes

| Output ID | Class | COCO Index |
|---|---|---|
| 0 | car | 2 |
| 1 | bus | 5 |
| 2 | truck | 7 |
| 3 | motorcycle | 3 |

## Performance

Measured on RTX 4090, COCO val2017 images (640×640 letterbox):

| Metric | Value | Target |
|---|---|---|
| Mean FPS (CUDA) | ~371 | ≥ 30 |
| Mean FPS (CPU)  | ~34  | ≥ 30 |
| P95 latency (CUDA) | 2.83 ms | < 50 ms |
| Inference (GPU) | 2.70 ms | — |

## Output Format

```json
[{
  "x": 320.5,
  "y": 240.1,
  "width": 150.0,
  "height": 90.0,
  "confidence": 0.91,
  "class_id": 0,
  "class": "car"
}]
```

## Files

| File | Purpose |
|---|---|
| `weights.onnx` | ONNX model (YOLO11-nano, opset 12) |
| `main.py` | Inference runner (reads class_names.txt automatically) |
| `class_names.txt` | One class name per line |
| `model_type.json` | Model metadata |
| `pyproject.toml` | Python package dependencies |
| `README.md` | This file |