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Annotated object detection dataset with bounding boxes for training YOLO, R-CNN, SSD, and other object detection models. Perfect for computer vision research and deep learning projects.

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object_detection.py
Sample Image
Person (95%)
Car (89%)
Dog (92%)
Detection Confidence
92%
Real-time Detection
30+ FPS
High Accuracy
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Features

## Dataset Features

Everything you need for object detection model training

### Bounding Box Annotations

Precise bounding box coordinates for every object in each image with pixel-perfect accuracy.

Accuracy: 99.5%

### Multiple Object Classes

10 different object categories including persons, vehicles, animals, and common objects.

10 Classes

### Training & Validation Sets

Pre-split dataset with 80% training and 20% validation data for immediate use.

80/20 Split

### YOLO Format Compatible

Ready-to-use annotations in YOLO format (txt files) for YOLOv5, YOLOv7, and YOLOv8.

YOLOv8 Ready

### COCO Format Available

Complete COCO JSON annotations for compatibility with Detectron2 and other frameworks.

JSON Export

### High Quality Images

High resolution PNG and JPG images with diverse lighting and background conditions.

HD Quality
Analytics

## Dataset Statistics

Interactive visualization of dataset distribution

### Class Distribution

### Annotations per Class

### Performance Metrics

92%
mAP@0.5
88%
Precision
85%
Recall
30ms
Inference
Interactive

## Annotation Playground

Draw bounding boxes and explore annotation formats

Click and drag to draw bounding boxes

Select a class from the dropdown above
Boxes: 0 Canvas: 640 x 480

    # Draw boxes to see annotations here
    # Format: class_id x_center y_center width height

Gallery

## Dataset Preview

Sample images with annotations from the dataset

Person
3 annotations
Car
5 annotations
Dog
2 annotations
Cat
4 annotations
Bicycle
2 annotations
Motorcycle
3 annotations
Structure

## Dataset Structure

Organized directory structure for easy integration

File Explorer
object-detection-dataset/ root
train/ Training data
images/ 800 files
labels/ 800 files
valid/ Validation data
images/ 200 files
labels/ 200 files
data.yaml YOLO config
annotations.json COCO format
classes.txt 10 classes
README.md Documentation

    # YOLO Annotation Format
    # class_id x_center y_center width height
    # All values are normalized (0-1)

    0 0.453125 0.546875 0.234375 0.687500
    1 0.765625 0.421875 0.312500 0.234375
    2 0.156250 0.718750 0.187500 0.250000

    // COCO JSON Annotation Format
    {
      "images": [{
        "id": 1,
        "file_name": "image_001.jpg",
        "width": 640,
        "height": 480
      }],
      "annotations": [{
        "id": 1,
        "image_id": 1,
        "category_id": 0,
        "bbox": [145, 97, 150, 330],
        "area": 49500
      }],
      "categories": [
        {"id": 0, "name": "person"}
      ]
    }

    # YOLOv5/YOLOv8 Dataset Configuration

    path: ./object-detection-dataset
    train: train/images
    val: valid/images

    nc: 10  # number of classes

    names:
      0: person
      1: car
      2: dog
      3: cat
      4: bicycle
      5: motorcycle
      6: bus
      7: truck
      8: bird
      9: chair

Categories

## Object Classes

10 annotated object categories in the dataset

Person 1,200+ instances
Car 800+ instances
Dog 450+ instances
Cat 400+ instances
Bicycle 350+ instances
Motorcycle 300+ instances
Bus 250+ instances
Truck 280+ instances
Bird 350+ instances
Chair 420+ instances
Quick Start

## Get Started in Minutes

Follow these steps to train your object detection model

### Download Dataset

Download and extract the dataset zip file to your project directory.

`unzip object-detection.zip -d ./data/`

### Install Dependencies

Install required Python packages for your chosen framework.

`pip install ultralytics opencv-python`

### Train Your Model

Start training using YOLOv8 or your preferred framework.

`yolo train model=yolov8n.pt data=data.yaml epochs=100`

### Run Inference

Use your trained model for object detection on new images.

`yolo predict model=best.pt source=image.jpg`

## Ready to Download?

Get the complete object detection dataset with all annotations and start training your models today!

~150 MB
1000+ Images
Free License
Download Dataset (ZIP)
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