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| Property | Value |
|---|---|
| Model | YOLO11s |
| Classes | 82 Vietnamese traffic signs |
| Image size | 640×640 |
| Framework | Ultralytics |
| Dataset Source | Hugging Face |
evalution/summary.json:| Metric | Value |
|---|---|
| Model | yolo11s.pt |
| Epochs Trained | 50 |
| Number of Classes | 82 |
| Device | GPU |
| Precision | 96.42% (0.9642) |
| Recall | 96.15% (0.9615) |
| mAP50 | 98.06% (0.9806) |
| mAP75 | 93.37% (0.9337) |
| mAP50-95 | 83.57% (0.8357) |
| FPS | 61.5 |
| Mean Latency | 16.25 ms |
| p50 Latency | 15.03 ms |
| p95 Latency | 22.59 ms |
| Min Latency | 13.44 ms |
| Max Latency | 23.24 ms |
| File | Description |
|---|---|
best.pt | PyTorch weights (main model) |
best.onnx | ONNX export (CPU/edge deploy) |
data.yaml | Dataset config with class names |
config.json | Training hyperparameters |
metrics.json | Test-set evaluation results |
benchmark.json | FPS / latency results |
summary.json | All metrics combined |
1from ultralytics import YOLO
2
3# PyTorch
4model = YOLO("best.pt")
5results = model("image.jpg", conf=0.25)
6results[0].show()
7
8# ONNX (faster on CPU)
9model_onnx = YOLO("best.onnx")
10results = model_onnx("image.jpg")1python infer.py --source image.jpg
2python infer.py --source video.mp4 --save
3python infer.py --source 0 --show # webcam