Datasets:

SobanHM
/

Road-Objects-Detection-Dataset

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YAML Metadata Warning:The task_ids "object-detection" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation

🚦 Road Object Detection Dataset (YOLOv8)

A multi-class road scene object detection dataset for training and benchmarking modern object detection models.

📌 Overview

This dataset contains annotated road scene images in YOLOv8 format for multi-class object detection.

It is suitable for developing and evaluating deep learning models for:

🚗 Vehicle Detection
🚦 Traffic Monitoring
🏙️ Smart City Applications
🚘 Autonomous Driving Research
📹 Intelligent Transportation Systems (ITS)

📂 Dataset Structure

Road_Object_Detection_Dataset/
│
├── data.yaml
│
├── train/
│   ├── images/
│   └── labels/
│
├── valid/
│   ├── images/
│   └── labels/
│
└── test/
    ├── images/
    └── labels/

🏷️ Classes

ID	Class
0	🚲 Bike
1	🚌 Bus
2	🚗 Car
3	🚶 Person
4	🚦 Traffic Signal
5	🚚 Truck

📝 Annotation Format

The dataset follows the YOLOv8 annotation format.

Each label file contains one object per line:

class x_center y_center width height

where all coordinates are normalized between 0 and 1.

Example:

2 0.523 0.418 0.247 0.182

🚀 Training Example

from ultralytics import YOLO

model = YOLO("yolov8n.pt")

model.train(
    data="data.yaml",
    epochs=100,
    imgsz=640
)

🎯 Applications

Object Detection
Vehicle Detection
Traffic Analysis
Road Scene Understanding
Smart Transportation
Autonomous Driving
AI Surveillance
Academic Research

📋 Dataset Information

Property	Value
Task	Object Detection
Annotation Format	YOLOv8
Number of Classes	6
Data Split	Train / Validation / Test
License	CC BY 4.0

📜 Citation

If you use this dataset in your research, please cite it appropriately.

@dataset{road_object_detection_yolov8,
  title={Road Object Detection Dataset (YOLOv8)},
  author={Soban Hussain},
  year={2026},
  publisher={Hugging Face},
}

📄 License

This dataset is distributed under the CC BY 4.0 License.

⭐ If you find this dataset useful, consider giving it a star on Hugging Face.

Happy Training!

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