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| 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) |
| Output ID | Class | COCO Index |
|---|---|---|
| 0 | car | 2 |
| 1 | bus | 5 |
| 2 | truck | 7 |
| 3 | motorcycle | 3 |
| 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 | — |
1[{
2 "x": 320.5,
3 "y": 240.1,
4 "width": 150.0,
5 "height": 90.0,
6 "confidence": 0.91,
7 "class_id": 0,
8 "class": "car"
9}]| 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 |