star092304/Traffic-sign-detection-VietNam

Vietnam Traffic Sign Detection Dataset

This repository contains the dataset for detecting road traffic signs in Vietnam using the state-of-the-art YOLO object detection model.

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📂 Repository Structure

The dataset is structured in the standard YOLO format, containing images and corresponding annotations divided into training, validation, and testing sets.

├── classid.xlsx        # Excel file mapping class IDs to names
├── dataset/
│   ├── train/          # Training split
│   │   ├── images/     # Raw images (.jpg)
│   │   └── labels/     # Normalized bounding box coordinates (.txt)
│   ├── val/            # Validation split
│   │   ├── images/
│   │   └── labels/
│   └── test/           # Testing split
│       ├── images/
│       └── labels/
└── README.md

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📊 Dataset Distribution

The dataset statistics below show the division of images and labels across the different splits:

Split	Images	Details
Train	8,125	8,124 labeled images + 36 background (negative) images
Validation	1,016	1,014 labeled images + 2 background (negative) images
Test	1,016	1,014 labeled images + 2 background (negative) images
Total	10,157

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🏷️ Annotation Format

Annotations follow the standard YOLO format. For every image (e.g., 0002.jpg), there is a corresponding label file (e.g., 0002.txt) in the labels/ directory.

Each row in the text file represents a single bounding box formatted as:

<class_id> <x_center> <y_center> <width> <height>

• class_id: Integer representing the target class index (ranging from 0 to 81).
• x_center, y_center: Center coordinates of the bounding box, normalized to [0.0, 1.0] by dividing by the image's width and height.
• width, height: The width and height of the bounding box, normalized to [0.0, 1.0].

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🚦 Supported Traffic Sign Classes (82 Classes)

The dataset contains 82 categories of road signs commonly found on Vietnamese streets:

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