Datasets:
File size: 3,064 Bytes
0f47701 33257b0 0f47701 33257b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | ---
license: cc-by-4.0
language:
- en
task_categories:
- object-detection
task_ids:
- object-detection
tags:
- yolo
- yolov8
- computer-vision
- object-detection
- traffic
- road
- vehicles
- intelligent-transportation
- traffic-monitoring
- autonomous-driving
pretty_name: Road Object Detection Dataset (YOLOv8)
annotations_creators:
- expert-generated
source_datasets:
- Roboflow
---
# 🚦 Road Object Detection Dataset (YOLOv8)
<div align="center">
**A multi-class road scene object detection dataset for training and benchmarking modern object detection models.**
</div>
---
## 📌 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:
```text
class x_center y_center width height
```
where all coordinates are **normalized** between **0 and 1**.
Example:
```text
2 0.523 0.418 0.247 0.182
```
---
# 🚀 Training Example
```python
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.
```bibtex
@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.
---
<div align="center">
### ⭐ If you find this dataset useful, consider giving it a star on Hugging Face.
Happy Training!
</div> |