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star092304/traffic-sign-detection-vietnam-yolo

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

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Model Card

🚦 Traffic Sign Detection — Vietnam (YOLO11s)

![YOLO](https://github.com/ultralytics/ultralytics) ![Dataset](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

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

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Results

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Random Predictions (Inference)

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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)