{
  "id": 699592,
  "title": "CNN with Data Augmentation & Batch Normalization - Tips for 99%+ Accuracy",
  "url": "/competitions/digit-recognizer/discussion/699592",
  "author_name": "HAMIDA",
  "post_date": "2026-05-14T11:16:00.514000",
  "votes": 0,
  "comment_count": 0,
  "views": null,
  "content": "<p>Hi fellow Kagglers!</p>\n<p>I wanted to share some techniques I found effective for pushing MNIST accuracy above 99%:</p>\n<p><strong>Architecture:</strong></p>\n<ul>\n<li>Multiple Conv2D blocks (32 -&gt; 64 -&gt; 128 filters) with BatchNormalization after each layer</li>\n<li>MaxPooling2D between blocks + Dropout (0.25) for regularization</li>\n<li>Dense(256) + Dropout(0.5) before the final softmax layer</li>\n</ul>\n<p><strong>Data Augmentation (key for generalization):</strong></p>\n<ul>\n<li>Rotation range: 10 degrees</li>\n<li>Zoom range: 0.1</li>\n<li>Width/height shift: 0.1</li>\n<li>These prevent overfitting on the 42K training samples</li>\n</ul>\n<p><strong>Training tricks:</strong></p>\n<ul>\n<li>ReduceROnPlateau callback (factor=0.5, patience=3)</li>\n<li>EarlyStopping (patience=10, restorebest_weights=True)</li>\n<li>Batch size 128 with GPU T4</li>\n</ul>\n<p>With this setup, I consistently get 99.2%+ validation accuracy.</p>\n<p>What techniques have you found most effective Has anyone tried ensemble methods or test-time augmentation (TTA) to push even further?</p>\n<p>Happy to discuss and share notebooks!</p>",
  "messages": [
    {
      "id": 3457808,
      "postDate": "2026-05-14T11:16:00.513Z",
      "content": "<p>Hi fellow Kagglers!</p>\n<p>I wanted to share some techniques I found effective for pushing MNIST accuracy above 99%:</p>\n<p><strong>Architecture:</strong></p>\n<ul>\n<li>Multiple Conv2D blocks (32 -&gt; 64 -&gt; 128 filters) with BatchNormalization after each layer</li>\n<li>MaxPooling2D between blocks + Dropout (0.25) for regularization</li>\n<li>Dense(256) + Dropout(0.5) before the final softmax layer</li>\n</ul>\n<p><strong>Data Augmentation (key for generalization):</strong></p>\n<ul>\n<li>Rotation range: 10 degrees</li>\n<li>Zoom range: 0.1</li>\n<li>Width/height shift: 0.1</li>\n<li>These prevent overfitting on the 42K training samples</li>\n</ul>\n<p><strong>Training tricks:</strong></p>\n<ul>\n<li>ReduceROnPlateau callback (factor=0.5, patience=3)</li>\n<li>EarlyStopping (patience=10, restorebest_weights=True)</li>\n<li>Batch size 128 with GPU T4</li>\n</ul>\n<p>With this setup, I consistently get 99.2%+ validation accuracy.</p>\n<p>What techniques have you found most effective Has anyone tried ensemble methods or test-time augmentation (TTA) to push even further?</p>\n<p>Happy to discuss and share notebooks!</p>",
      "rawMarkdown": "Hi fellow Kagglers!\n\nI wanted to share some techniques I found effective for pushing MNIST accuracy above 99%:\n\n**Architecture:**\n- Multiple Conv2D blocks (32 -> 64 -> 128 filters) with BatchNormalization after each layer\n- MaxPooling2D between blocks + Dropout (0.25) for regularization\n- Dense(256) + Dropout(0.5) before the final softmax layer\n\n**Data Augmentation (key for generalization):**\n- Rotation range: 10 degrees\n- Zoom range: 0.1\n- Width/height shift: 0.1\n- These prevent overfitting on the 42K training samples\n\n**Training tricks:**\n- ReduceROnPlateau callback (factor=0.5, patience=3)\n- EarlyStopping (patience=10, restorebest_weights=True)\n- Batch size 128 with GPU T4\n\nWith this setup, I consistently get 99.2%+ validation accuracy.\n\nWhat techniques have you found most effective Has anyone tried ensemble methods or test-time augmentation (TTA) to push even further?\n\nHappy to discuss and share notebooks!"
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "3457808": "Hi fellow Kagglers!\n\nI wanted to share some techniques I found effective for pushing MNIST accuracy above 99%:\n\n**Architecture:**\n- Multiple Conv2D blocks (32 -> 64 -> 128 filters) with BatchNormalization after each layer\n- MaxPooling2D between blocks + Dropout (0.25) for regularization\n- Dense(256) + Dropout(0.5) before the final softmax layer\n\n**Data Augmentation (key for generalization):**\n- Rotation range: 10 degrees\n- Zoom range: 0.1\n- Width/height shift: 0.1\n- These prevent overfitting on the 42K training samples\n\n**Training tricks:**\n- ReduceROnPlateau callback (factor=0.5, patience=3)\n- EarlyStopping (patience=10, restorebest_weights=True)\n- Batch size 128 with GPU T4\n\nWith this setup, I consistently get 99.2%+ validation accuracy.\n\nWhat techniques have you found most effective Has anyone tried ensemble methods or test-time augmentation (TTA) to push even further?\n\nHappy to discuss and share notebooks!"
  }
}