README.md

metadata

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

[{
  "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