Where's Waldo AI Detection

Advanced computer vision system using YOLOv8 to automatically detect and locate Waldo in complex images

YOLOv8Computer VisionFlask APIStreamlit

View on GitHub Live Demo

System Architecture

Image Input

Upload via API or Web UI

YOLOv8 Model

Object Detection Engine

Detection

Locate Waldo with Confidence

Results

Bounding Box & Confidence

Data Analysis

Comprehensive EDA on 106 images revealing size variations, aspect ratios, and contrast distributions

Model Training

YOLOv8 training with 45 epochs, data augmentation, and custom dataset optimization for single-class detection

Deployment

Multi-interface deployment with Flask REST API and Streamlit web application for different use cases

Project Documentation

Exploratory Data Analysis

PDF

Comprehensive analysis of the Waldo dataset including data distribution, image characteristics, and preprocessing insights.

Flask API Implementation

Code

Production-ready Flask API with YOLOv8 integration for real-time Waldo detection with Swagger documentation.

Model Training Notebook

PDF

Complete YOLOv8 training pipeline including dataset preparation, model configuration, training process, and evaluation metrics.

Technical Deep Dive

Dataset Characteristics

# Dataset Statistics
Total Images: 106
Image Sizes: 500x700 to 4000x2500 pixels
Aspect Ratios: Consistent majority
Contrast Range: 0.09 - 0.14 (high quality)
Classes: 1 (Waldo/Wally)

Size Variation Challenge

Handles images from small 500px to large 4000px+ dimensions

Quality Consistency

Excellent contrast distribution enables reliable feature detection

Model Configuration

# YOLOv8 Training Setup
model = YOLO("yolov8s.pt")
epochs = 45
imgsz = 640
batch_size = 16
data = "waldo_dataset/data.yaml"

Single-Class Optimization

Specialized for Waldo detection with custom anchor configurations

Transfer Learning

Built on pre-trained COCO weights for enhanced feature extraction

Development Journey & Problem Solving

🎯 Computer Vision Mastery

YOLOv8 Implementation: Advanced object detection with custom dataset training

Data Augmentation: Sophisticated techniques to handle size variations

Model Optimization: Fine-tuned hyperparameters for single-class detection

🔧 Full-Stack Development

Created REST API with Flask for programmatic access

Built interactive web interface with Streamlit

Implemented proper image handling and color space conversion

⚡ Technical Challenges Solved

Dataset Imbalance: Addressed limited training data with strategic augmentation

Scale Variation: Handled extreme image size differences through preprocessing

Deployment Integration: Seamlessly connected ML model with web frameworks

📊 Data-Driven Approach

Comprehensive EDA revealed dataset characteristics

Statistical analysis guided training decisions

Performance metrics validated model effectiveness

Key Features & Capabilities

AI Detection

Advanced YOLOv8 model with high accuracy

Real-time Processing

Fast inference with instant results

Multiple Interfaces

API and web UI for different use cases

Data Analytics

Comprehensive EDA and performance metrics

Project Impact & Technical Achievement

Training Epochs

Optimized model performance

106

Training Images

Diverse dataset coverage

Deployment Options

API and Web interfaces

This project showcases end-to-end machine learning development from data exploration through model deployment. By combining computer vision expertise with full-stack development skills, I created a production-ready system that demonstrates both technical depth in AI and practical software engineering capabilities.