Victor Phan
348 lines
120 KiB
HTML
348 lines
120 KiB
HTML
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<!DOCTYPE html>
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<html lang="vi">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Training Report - 20260105_111347</title>
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<style>
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* {
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margin: 0;
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padding: 0;
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box-sizing: border-box;
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}
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body {
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font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
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background: #f5f5f5;
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padding: 20px;
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line-height: 1.6;
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}
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.container {
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max-width: 1200px;
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margin: 0 auto;
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background: white;
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border-radius: 15px;
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box-shadow: 0 10px 40px rgba(0,0,0,0.1);
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overflow: hidden;
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}
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.header {
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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color: white;
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padding: 40px;
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text-align: center;
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}
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.header h1 {
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font-size: 2.5em;
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margin-bottom: 10px;
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}
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.header .subtitle {
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opacity: 0.9;
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font-size: 1.1em;
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}
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.content {
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padding: 40px;
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}
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.section {
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margin-bottom: 40px;
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}
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.section h2 {
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color: #667eea;
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border-bottom: 3px solid #667eea;
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padding-bottom: 10px;
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margin-bottom: 20px;
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font-size: 1.5em;
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}
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.stats-grid {
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
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gap: 20px;
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margin-bottom: 30px;
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}
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.stat-card {
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background: linear-gradient(135deg, #667eea15 0%, #764ba215 100%);
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padding: 25px;
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border-radius: 10px;
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text-align: center;
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border: 1px solid #667eea30;
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}
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.stat-card .value {
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font-size: 2.5em;
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font-weight: bold;
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color: #667eea;
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}
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.stat-card .label {
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color: #666;
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margin-top: 5px;
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}
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.stat-card.success .value {
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color: #28a745;
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}
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.stat-card.warning .value {
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color: #ffc107;
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}
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table {
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width: 100%;
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border-collapse: collapse;
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margin: 20px 0;
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}
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th, td {
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padding: 12px 15px;
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text-align: left;
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border-bottom: 1px solid #ddd;
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}
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th {
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background: #667eea;
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color: white;
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}
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tr:hover {
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background-color: #f5f5f5;
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}
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.conf-matrix {
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font-size: 14px;
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}
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.conf-matrix th, .conf-matrix td {
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text-align: center;
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padding: 8px;
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}
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.chart-container {
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text-align: center;
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margin: 20px 0;
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}
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.chart-container img {
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max-width: 100%;
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border-radius: 10px;
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box-shadow: 0 4px 15px rgba(0,0,0,0.1);
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}
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.info-box {
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background: #e3f2fd;
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padding: 20px;
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border-radius: 10px;
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border-left: 5px solid #2196f3;
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margin: 20px 0;
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}
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.info-row {
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display: flex;
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margin: 10px 0;
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}
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.info-label {
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font-weight: bold;
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width: 200px;
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color: #555;
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}
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.info-value {
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color: #333;
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}
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.footer {
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background: #f8f9fa;
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padding: 20px;
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text-align: center;
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color: #666;
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font-size: 14px;
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}
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@media print {
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body {
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background: white;
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padding: 0;
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}
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.container {
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box-shadow: none;
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}
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}
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</style>
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</head>
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<body>
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<div class="container">
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<div class="header">
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<h1>📊 Báo Cáo Training Model</h1>
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<p class="subtitle">Land Classification - 05/01/2026 11:13:48</p>
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</div>
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<div class="content">
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<!-- Summary Stats -->
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<div class="section">
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<h2>📈 Tóm Tắt Kết Quả</h2>
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<div class="stats-grid">
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<div class="stat-card success">
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<div class="value">77.1%</div>
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<div class="label">Train Accuracy</div>
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</div>
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<div class="stat-card warning">
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<div class="value">72.5%</div>
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<div class="label">Test Accuracy</div>
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</div>
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<div class="stat-card">
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<div class="value">868</div>
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<div class="label">Training Samples</div>
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</div>
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<div class="stat-card">
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<div class="value">218</div>
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<div class="label">Testing Samples</div>
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</div>
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<div class="stat-card">
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<div class="value">8</div>
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<div class="label">Số Classes</div>
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</div>
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<div class="stat-card">
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<div class="value">20%</div>
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<div class="label">Test Size</div>
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</div>
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</div>
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</div>
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<!-- Configuration Info -->
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<div class="section">
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<h2>⚙️ Cấu Hình Training</h2>
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<div class="info-box">
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<div class="info-row">
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<span class="info-label">🤖 Model Type:</span>
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<span class="info-value">SWIN-UNET</span>
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</div>
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<div class="info-row">
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<span class="info-label">📍 Khu vực (bbox):</span>
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<span class="info-value">[105.561448, 9.264228, 106.298669, 9.931334]</span>
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</div>
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<div class="info-row">
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<span class="info-label">📅 Thời gian:</span>
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<span class="info-value">2023-03-01/2023-12-31</span>
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</div>
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<div class="info-row">
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<span class="info-label">📐 Độ phân giải:</span>
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<span class="info-value">20m</span>
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</div>
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<div class="info-row">
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<span class="info-label">💾 Model Path:</span>
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<span class="info-value">model_train/model_swin-unet_20260105_110354.joblib</span>
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</div>
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</div>
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</div>
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<!-- Classification Report -->
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<div class="section">
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<h2>📋 Classification Report</h2>
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<table>
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<thead>
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<tr>
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<th>Loại đất</th>
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<th>Precision</th>
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<th>Recall</th>
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<th>F1-Score</th>
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<th>Support</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td><strong>0</strong></td>
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<td>0.000</td>
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<td>0.000</td>
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<td>0.000</td>
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<td>12</td>
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</tr>
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<tr>
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<td><strong>1</strong></td>
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<td>0.696</td>
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<td>0.780</td>
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<td>0.736</td>
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<td>41</td>
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</tr>
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<tr>
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<td><strong>2</strong></td>
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<td>0.000</td>
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<td>0.000</td>
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<td>0.000</td>
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<td>9</td>
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</tr>
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<tr>
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<td><strong>3</strong></td>
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<td>0.622</td>
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<td>0.676</td>
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<td>0.648</td>
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<td>34</td>
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</tr>
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<tr>
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<td><strong>4</strong></td>
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<td>0.625</td>
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<td>0.769</td>
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<td>0.690</td>
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<td>26</td>
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</tr>
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<tr>
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<td><strong>5</strong></td>
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<td>0.929</td>
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<td>1.000</td>
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<td>0.963</td>
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<td>26</td>
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</tr>
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<tr>
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<td><strong>6</strong></td>
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<td>0.956</td>
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<td>0.977</td>
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<td>0.966</td>
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<td>44</td>
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</tr>
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<tr>
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<td><strong>7</strong></td>
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<td>0.467</td>
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<td>0.538</td>
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<td>0.500</td>
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<td>26</td>
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</tr>
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<tr style="background-color: #f0f0f0; font-weight: bold;">
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<td>macro avg</td>
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<td>0.537</td>
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<td>0.593</td>
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<td>0.563</td>
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<td>218</td>
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</tr>
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<tr style="background-color: #f0f0f0; font-weight: bold;">
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<td>weighted avg</td>
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<td>0.662</td>
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<td>0.725</td>
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<td>0.691</td>
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<td>218</td>
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</tr>
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</tbody>
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</table>
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</div>
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<!-- Metrics Chart -->
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<div class="section"><h2>📊 Biểu Đồ Metrics</h2><div class="chart-container"><img src="data:image/png;base64,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alt="Metrics Chart"></div></div>
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<div class="section"><h2>📊 Phân Bố Số Mẫu</h2><div class="chart-container"><img 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" alt="Class Distribution"></div></div>
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<!-- Confusion Matrix -->
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<div class="section">
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<h2>🔢 Confusion Matrix</h2>
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<div class="chart-container"><img 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" alt="Confusion Matrix"></div>
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</div>
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<!-- Classes List -->
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<div class="section">
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<h2>🏷️ Danh Sách Các Loại Đất</h2>
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<div class="info-box">
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<ul style="list-style: none; display: flex; flex-wrap: wrap; gap: 10px;">
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<li style="background: #667eea; color: white; padding: 8px 15px; border-radius: 20px;">0</li><li style="background: #667eea; color: white; padding: 8px 15px; border-radius: 20px;">1</li><li style="background: #667eea; color: white; padding: 8px 15px; border-radius: 20px;">2</li><li style="background: #667eea; color: white; padding: 8px 15px; border-radius: 20px;">3</li><li style="background: #667eea; color: white; padding: 8px 15px; border-radius: 20px;">4</li><li style="background: #667eea; color: white; padding: 8px 15px; border-radius: 20px;">5</li><li style="background: #667eea; color: white; padding: 8px 15px; border-radius: 20px;">6</li><li style="background: #667eea; color: white; padding: 8px 15px; border-radius: 20px;">7</li>
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</ul>
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</div>
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</div>
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</div>
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<div class="footer">
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<p>🌍 Land Classification Training System | Generated: 05/01/2026 11:13:48</p>
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<p>Data Source: Microsoft Planetary Computer (Sentinel-2 L2A, Sentinel-1 RTC)</p>
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</div>
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</div>
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</body>
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</html>
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