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<title>CV Model Improvement Journey - Pothole Detection</title>
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</style>
</head>
<body>
<header class="header">
<div class="header-content">
<div class="logo">
<i class="fas fa-road"></i>
<h1>Pothole Detection Experiment</h1>
</div>
<a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" class="built-with">
Built with anycoder
</a>
</div>
</header>
<nav class="nav-tabs">
<button class="nav-tab active" data-tab="overview">
<i class="fas fa-home"></i> Overview
</button>
<button class="nav-tab" data-tab="methodology">
<i class="fas fa-flask"></i> Methodology
</button>
<button class="nav-tab" data-tab="data">
<i class="fas fa-database"></i> Data Strategy
</button>
<button class="nav-tab" data-tab="benchmarks">
<i class="fas fa-chart-bar"></i> Benchmarks
</button>
<button class="nav-tab" data-tab="insights">
<i class="fas fa-lightbulb"></i> Key Insights
</button>
<button class="nav-tab" data-tab="resources">
<i class="fas fa-link"></i> Resources
</button>
</nav>
<main class="main-content">
<!-- Overview Section -->
<section id="overview" class="section active">
<div class="hero">
<h2>Domain Shift Problem in Computer Vision</h2>
<p>A systematic experiment to improve pothole detection from overhead aerial imagery, addressing the challenge of models trained on street-level data failing on different perspectives.</p>
<div class="usecase-badge">
<i class="fas fa-bullseye"></i>
Use Case: Detect potholes from overhead view on dirt roads with little traffic
</div>
</div>
<div class="stats-grid">
<div class="stat-card">
<div class="stat-value">95x</div>
<div class="stat-label">Improvement in mAP50</div>
</div>
<div class="stat-card">
<div class="stat-value">1,444</div>
<div class="stat-label">Manually Annotated Potholes</div>
</div>
<div class="stat-card">
<div class="stat-value">23</div>
<div class="stat-label">Benchmark Frames</div>
</div>
<div class="stat-card">
<div class="stat-value">0.429</div>
<div class="stat-label">Final mAP50 Score</div>
</div>
</div>
<div class="chart-container">
<h3 class="chart-title">Model Performance Progression (mAP50)</h3>
<div class="bar-chart">
<div class="bar-item">
<div class="bar-label">Baseline (1 Epoch)</div>
<div class="bar-wrapper">
<div class="bar-fill baseline" data-width="1"></div>
</div>
<div class="bar-value danger">0.0045</div>
</div>
<div class="bar-item">
<div class="bar-label">20 Epochs (Control)</div>
<div class="bar-wrapper">
<div class="bar-fill epoch20" data-width="1"></div>
</div>
<div class="bar-value danger">0.0042</div>
</div>
<div class="bar-item">
<div class="bar-label">New Dataset (1 Epoch)</div>
<div class="bar-wrapper">
<div class="bar-fill newds" data-width="24"></div>
</div>
<div class="bar-value warning">0.102</div>
</div>
<div class="bar-item">
<div class="bar-label">New Dataset (20 Epochs)</div>
<div class="bar-wrapper">
<div class="bar-fill final" data-width="100"></div>
</div>
<div class="bar-value success">0.429</div>
</div>
</div>
</div>
<div class="conclusion-box">
<h3><i class="fas fa-check-circle"></i> Key Finding</h3>
<p>
<strong>Domain-specific data is the primary driver of model performance improvement.</strong>
While increasing training epochs on the original dataset showed minimal improvement (0.0045 → 0.0042),
training on domain-specific aerial imagery resulted in a <strong>20x improvement</strong> after just 1 epoch.
Combining domain-specific data with extended training achieved a <strong>95x improvement</strong> over baseline.
</p>
</div>
</section>
<!-- Methodology Section -->
<section id="methodology" class="section">
<div class="section-header">
<h2>Experimental Methodology</h2>
<p>A structured 8-step approach to systematically improve model performance</p>
</div>
<div class="methodology-steps">
<div class="method-card">
<div class="method-number">1</div>
<div class="method-content">
<h4>Define the Problem</h4>
<p>Identify a specific, narrow use case to focus the experiment.</p>
<div class="method-details">
<ul>
<li><i class="fas fa-check"></i> Recognized domain shift issue from street-level to overhead imagery</li>
<li><i class="fas fa-check"></i> Defined use case: Overhead pothole detection on dirt roads</li>
<li><i class="fas fa-check"></i> Scope limited to low-traffic environments</li>
</ul>
</div>
</div>
</div>
<div class="method-card">
<div class="method-number">2</div>
<div class="method-content">
<h4>Hypothesize Data Requirements</h4>
<p>Determine what data characteristics are needed to solve the problem.</p>
<div class="method-details">
<ul>
<li><i class="fas fa-check"></i> Diverse overhead images of dirt roads</li>
<li><i class="fas fa-check"></i> Various weather conditions and angles</li>
<li><i class="fas fa-check"></i> Manual annotation requirement identified</li>
</ul>
</div>
</div>
</div>
<div class="method-card">
<div class="method-number">3</div>
<div class="method-content">
<h4>Define Success Metrics</h4>
<p>Establish measurable criteria to validate the hypothesis.</p>
<div class="method-details">
<ul>
<li><i class="fas fa-check"></i> Created benchmark set: 23 frames, 1,444 annotated potholes</li>
<li><i class="fas fa-check"></i> Target: Achieve mAP50 > 0.5 (baseline was 0.0045)</li>
<li><i class="fas fa-check"></i> Implemented <code>model.val()</code> for validation</li>
</ul>
</div>
</div>
</div>
<div class="method-card">
<div class="method-number">4</div>
<div class="method-content">
<h4>Source Training Data</h4>
<p>Acquire or create the necessary dataset for training.</p>
<div class="method-details">
<ul>
<li><i class="fas fa-check"></i> 3 stock videos from Pexels (free)</li>
<li><i class="fas fa-check"></i> 1 synthetic video from Nano Bananas</li>
<li><i class="fas fa-check"></i> Explored synthetic data generation options</li>
</ul>
</div>
</div>
</div>
<div class="method-card">
<div class="method-number">5</div>
<div class="method-content">
<h4>Prepare & Annotate Data</h4>
<p>Process and label the data for training.</p>
<div class="method-details">
<ul>
<li><i class="fas fa-check"></i> Used Roboflow with Meta's SAM3 for initial labeling</li>
<li><i class="fas fa-check"></i> Manual labeling for challenging videos</li>
<li><i class="fas fa-check"></i> Applied augmentation: brightness & exposure</li>
<li><i class="fas fa-check"></i> Converted bounding boxes to polygons for better precision</li>
</ul>
</div>
</div>
</div>
<div class="method-card">
<div class="method-number">6</div>
<div class="method-content">
<h4>Train Models</h4>
<p>Train multiple model variants to test hypotheses.</p>
<div class="method-details">
<ul>
<li><i class="fas fa-check"></i> Baseline: yolo11n.pt pretrained model</li>
<li><i class="fas fa-check"></i> Control: 1 & 20 epochs on original dataset</li>
<li><i class="fas fa-check"></i> Experimental: 1 & 20 epochs on new dataset</li>
</ul>
</div>
</div>
</div>
<div class="method-card">
<div class="method-number">7</div>
<div class="method-content">
<h4>Test Hypotheses</h4>
<p>Validate results against the benchmark dataset.</p>
<div class="method-details">
<ul>
<li><i class="fas fa-check"></i> Ran validation on all model variants</li>
<li><i class="fas fa-check"></i> Compared mAP50 and inference times</li>
<li><i class="fas fa-check"></i> Created "proof of life" side-by-side comparisons</li>
</ul>
</div>
</div>
</div>
<div class="method-card">
<div class="method-number">8</div>
<div class="method-content">
<h4>Draw Conclusions</h4>
<p>Analyze results and determine next steps.</p>
<div class="method-details">
<ul>
<li><i class="fas fa-check"></i> Hypothesis validated: Domain-specific data is critical</li>
<li><i class="fas fa-check"></i> 95x improvement achieved with new data + training</li>
<li><i class="fas fa-check"></i> Contributed validation scripts to OSS repo</li>
</ul>
</div>
</div>
</div>
</div>
</section>
<!-- Data Section -->
<section id="data" class="section">
<div class="section-header">
<h2>Data Strategy</h2>
<p>Understanding the importance of domain-specific training data</p>
</div>
<div class="data-cards">
<div class="data-card">
<div class="data-card-header">
<i class="fas fa-vial"></i>
<h4>Benchmark Dataset</h4>
</div>
<div class="data-card-body">
<div class="data-stat">
<span class="data-stat-label">Total Frames</span>
<span class="data-stat-value">23</span>
</div>
<div class="data-stat">
<span class="data-stat-label">Annotated Potholes</span>
<span class="data-stat-value">1,444</span>
</div>
<div class="data-stat">
<span class="data-stat-label">Annotation Type</span>
<span class="data-stat-value">Polygons</span>
</div>
<div class="data-stat">
<span class="data-stat-label">Source</span>
<span class="data-stat-value">Target Video</span>
</div>
</div>
</div>
<div class="data-card">
<div class="data-card-header">
<i class="fas fa-graduation-cap"></i>
<h4>Training Dataset</h4>
</div>
<div class="data-card-body">
<div class="data-stat">
<span class="data-stat-label">Video Sources</span>
<span class="data-stat-value">4</span>
</div>
<div class="data-stat">
<span class="data-stat-label">Stock Videos</span>
<span class="data-stat-value">3 (Pexels)</span>
</div>
<div class="data-stat">
<span class="data-stat-label">Synthetic Videos</span>
<span class="data-stat-value">1</span>
</div>
<div class="data-stat">
<span class="data-stat-label">Split Ratio</span>
<span class="data-stat-value">90/10 Train/Val</span>
</div>
</div>
</div>
<div class="data-card">
<div class="data-card-header">
<i class="fas fa-magic"></i>
<h4>Augmentation</h4>
</div>
<div class="data-card-body">
<div class="data-stat">
<span class="data-stat-label">Brightness</span>
<span class="data-