---
tags:
  - object-detection
  - ultralytics
  - yolo
  - traffic-sign
  - vietnam
  - yolo11s
library_name: ultralytics
license: apache-2.0
datasets:
  - traffic-sign-detection-vietnam
---

# 🚦 Traffic Sign Detection — Vietnam (YOLO11s)

[![YOLO](https://img.shields.io/badge/YOLO-v11-00c2cb?style=for-the-badge&logo=ultralytics&logoColor=white)](https://github.com/ultralytics/ultralytics)
[![Dataset](https://img.shields.io/badge/Dataset-Hugging%20Face-orange?style=for-the-badge&logo=huggingface)](https://huggingface.co/datasets/star092304/Traffic-sign-detection-VietNam)

YOLO11s model trained on the Vietnam Traffic Sign Detection dataset.

| Property | Value |
|----------|-------|
| Model | YOLO11s |
| Classes | 82 Vietnamese traffic signs |
| Image size | 640×640 |
| Framework | Ultralytics |
| Dataset Source | [Hugging Face](https://huggingface.co/datasets/star092304/Traffic-sign-detection-VietNam) |

## Evaluation Results

Below is the summary of the evaluation results from `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 |

## Visualizations

### Training Curves
![Training Curves](training/training_curves.png)

### Results
![Results](training/results.png)

### Random Predictions (Inference)
![Random Predictions](inference/random_predictions.png)

## Files

| 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 |

## Quick Start

```python
from ultralytics import YOLO

# PyTorch
model = YOLO("best.pt")
results = model("image.jpg", conf=0.25)
results[0].show()

# ONNX (faster on CPU)
model_onnx = YOLO("best.onnx")
results = model_onnx("image.jpg")
```

## CLI Inference

```bash
python infer.py --source image.jpg
python infer.py --source video.mp4 --save
python infer.py --source 0 --show          # webcam
```

## Training Details

- **Base model**: yolo11s.pt (pretrained COCO)
- **Early stopping**: patience=20
- **Dataset cache**: enabled (faster I/O)
- **Seed**: 42 (reproducible)
- **Optimizer**: auto (AdamW)