RSK World - Object Detection Dataset | YOLO | R-CNN | Bounding Box Annotations | RSK World

Bounding Boxes YOLO Format Deep Learning 10 Classes COCO Format PNG/JPG

This project features a comprehensive Object Detection dataset designed for professional computer vision, deep learning, and machine learning applications. The dataset includes 1000+ annotated images across 10 object classes: Person, Car, Dog, Cat, Bicycle, Motorcycle, Bus, Truck, Bird, and Chair. Features 5000+ precise bounding box annotations. Pre-split into Train (80%) and Validation (20%) sets with variable resolution images. Includes YOLO format (.txt) and COCO format (.json) annotations. Compatible with YOLOv5, YOLOv7, YOLOv8, Detectron2, MMDetection, and OpenCV. The package includes Python utility functions (utils.py) for loading annotations, visualizing bounding boxes, and dataset analysis. Features interactive demo website with annotation playground, Chart.js analytics dashboard, dataset statistics visualization, dark/light theme toggle. Complete with data.yaml configuration, annotations.json, classes.txt, comprehensive README.md. Perfect for data scientists, researchers, students, and developers working on object detection, localization, computer vision, and AI projects.

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Dataset Overview

Complete object detection dataset with 1000+ annotated images across 10 object classes with precise bounding box annotations.

1000+ annotated images
10 object classes
Person, Car, Dog, Cat, Bicycle
Motorcycle, Bus, Truck, Bird, Chair
5000+ bounding box annotations
Pre-split: Train (80%), Val (20%)
Variable resolution images (640x480 typical)
PNG/JPG formats supported
Balanced class distribution
Pixel-perfect annotations
Perfect for YOLO & R-CNN training

Dataset Structure & Files

Well-organized folder structure with training and validation splits plus comprehensive annotation files.

train/images/ - 800 training images (80%)
train/labels/ - YOLO format annotations
valid/images/ - 200 validation images (20%)
valid/labels/ - YOLO format annotations
data.yaml - YOLO configuration file
annotations.json - COCO format annotations
classes.txt - Class labels list
utils.py - Python utility functions
Consistent naming convention
Easy to load with YOLOv8/Detectron2
Ready for immediate use

YOLO Model Training

Complete training pipeline with support for YOLOv5, YOLOv7, YOLOv8, and other detection frameworks.

YOLOv8 (Ultralytics) integration
YOLOv5 support included
YOLOv7 compatible
Detectron2 ready
MMDetection support
OpenCV visualization
Model checkpointing
Learning rate scheduling
Early stopping callbacks
Training progress visualization
Model export (ONNX, TensorRT)

Annotation Formats

Multiple annotation formats supported for maximum compatibility with different detection frameworks.

YOLO format (.txt files)
Normalized coordinates (0-1)
class_id x_center y_center w h
COCO format (.json)
Pixel coordinates support
Pascal VOC compatible
Easy format conversion
Annotation visualization tools
Bounding box validation
Multi-object per image
Overlapping box support

Model Evaluation

Comprehensive evaluation tools with detection metrics, visualizations, and performance analysis.

mAP (Mean Average Precision)
Precision-Recall curves
IoU (Intersection over Union)
Per-class AP breakdown
Confusion matrix visualization
Detection confidence analysis
False positive/negative analysis
Inference speed benchmarks
Model comparison framework
Benchmark against baselines
Export evaluation reports

Compatible Frameworks

Works with all major object detection frameworks and libraries out of the box.

YOLOv8 (Ultralytics)
YOLOv5, YOLOv7
Detectron2 (Facebook)
MMDetection
OpenCV DNN
TensorFlow Object Detection API
PyTorch native
ONNX Runtime
TensorRT optimization
Edge deployment ready
Mobile inference support

What You Get

Complete package with all files needed for professional object detection projects.

1000+ annotated images
5000+ bounding box annotations
Python utility functions (utils.py)
Interactive demo website
Annotation playground
Chart.js analytics dashboard
Comprehensive README.md
RELEASE_NOTES.md version history
data.yaml for YOLO training
MIT License included
annotations.json & classes.txt

Interactive Demo Website

Beautiful demo website with annotation playground, analytics dashboard, and dataset visualization.

Modern dark-themed design
Annotation playground demo
Bounding box visualization
Real-time detection preview
Confidence score display
Image gallery with annotations
Class-based filtering
Chart.js analytics dashboard
Dataset statistics visualization
Dark/Light theme toggle
Fully responsive layout

Python Utilities Included

Professional Python utilities for loading annotations, visualization, and dataset analysis.

utils.py - Core utility functions
YOLO annotation loading
COCO format parsing
Bounding box visualization
Annotation format conversion
Dataset statistics analysis
Class distribution analysis
Well-documented code
Type hints included
Easy to customize
OpenCV integration

Object Classes

10 diverse object classes covering common real-world objects for comprehensive detection training.

Person - Human figures (1200+ instances)
Car - Cars and automobiles (800+ instances)
Dog - Various dog breeds (450+ instances)
Cat - Various cat breeds (400+ instances)
Bicycle - Bicycles (350+ instances)
Motorcycle - Motorcycles (300+ instances)
Bus - Buses (250+ instances)
Truck - Trucks (280+ instances)
Bird - Various bird species (350+ instances)
Chair - Chairs and seating (420+ instances)
Total: 5000+ annotations

Credits & Acknowledgments

This dataset is provided for educational and research purposes. Core technologies and libraries are credited below.

Python 3.8+ - Programming language (PSF License)
Ultralytics YOLOv8 - Object detection (AGPL-3.0)
PyTorch - Deep learning framework (BSD License)
NumPy - Numerical computing (BSD License)
OpenCV - Computer vision (Apache 2.0)
Detectron2 - Object detection (Apache 2.0)
COCO Format - Annotation standard
RSK World - Dataset creator and provider
GitHub Repository - Source code and releases
Author: Molla Samser | Designer: Rima Khatun
MIT License - Free for learning & research

Support & Contact

For commercial use, custom datasets, or integration help, please contact us.

Email: help@rskworld.in
Phone: +91 93305 39277
Website: RSKWORLD.in
Location: Nutanhat, Mongolkote, West Bengal, India
Author: Molla Samser
Designer & Tester: Rima Khatun
GitHub: Coming Soon
Object Detection Dataset Documentation
Technical Support Available
Custom Dataset Requests Welcome

Additional Sponsored Content

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Technologies

Object Detection

YOLOv8

Bounding Boxes

PyTorch

Python

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Object Detection Dataset

Dataset Overview

Dataset Structure & Files

YOLO Model Training

Annotation Formats

Model Evaluation

Compatible Frameworks

What You Get

Interactive Demo Website

Python Utilities Included

Object Classes

Credits & Acknowledgments

Support & Contact

Featured Content

Additional Sponsored Content

Download Free Source Code

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Technologies

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