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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model/cnn_model.keras filter=lfs diff=lfs merge=lfs -text
+model/mobnet_model.keras filter=lfs diff=lfs merge=lfs -text

03_Quantization_model.ipynb

@@ -0,0 +1,160 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import pathlib\n",
+    "import os"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quantize Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def quantize_model(model_path, output_path):\n",
+    "    \"\"\"\n",
+    "    Load Keras model and convert it to TFLite with quantization\n",
+    "    \n",
+    "    Args:\n",
+    "        model_path: Path to the .keras model file\n",
+    "        output_path: Path to save the quantized TFLite model\n",
+    "    \"\"\"\n",
+    "    # Step 1: Load the Keras model\n",
+    "    print(\"Loading model...\")\n",
+    "    model = tf.keras.models.load_model(model_path)\n",
+    "    \n",
+    "    # Step 2: Convert the model to TFLite format\n",
+    "    print(\"Converting to TFLite...\")\n",
+    "    converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
+    "    \n",
+    "    # Step 3: Enable quantization\n",
+    "    # Using dynamic range quantization (post-training quantization)\n",
+    "    converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
+    "    \n",
+    "    # Additional options for quantization\n",
+    "    converter.target_spec.supported_types = [tf.float16]\n",
+    "    \n",
+    "    # Step 4: Convert the model\n",
+    "    print(\"Applying quantization...\")\n",
+    "    tflite_model = converter.convert()\n",
+    "    \n",
+    "    # Step 5: Save the quantized model\n",
+    "    print(f\"Saving quantized model to {output_path}\")\n",
+    "    with open(output_path, 'wb') as f:\n",
+    "        f.write(tflite_model)\n",
+    "    \n",
+    "    # Calculate and print model size reduction\n",
+    "    original_size = os.path.getsize(model_path) / (1024 * 1024)  # Size in MB\n",
+    "    quantized_size = os.path.getsize(output_path) / (1024 * 1024)  # Size in MB\n",
+    "    \n",
+    "    print(f\"\\nModel size comparison:\")\n",
+    "    print(f\"Original model size: {original_size:.2f} MB\")\n",
+    "    print(f\"Quantized model size: {quantized_size:.2f} MB\")\n",
+    "    print(f\"Size reduction: {((original_size - quantized_size) / original_size * 100):.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define paths\n",
+    "model_path = \"model/mobnet_model.keras\"\n",
+    "output_path = \"model/mobnet_model_quantized.tflite\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model...\n",
+      "WARNING:tensorflow:From c:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\src\\backend.py:1398: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
+      "\n",
+      "WARNING:tensorflow:From c:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\src\\layers\\normalization\\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
+      "\n",
+      "Converting to TFLite...\n",
+      "Applying quantization...\n",
+      "INFO:tensorflow:Assets written to: C:\\Users\\ASUS\\AppData\\Local\\Temp\\tmpq8y46ide\\assets\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: C:\\Users\\ASUS\\AppData\\Local\\Temp\\tmpq8y46ide\\assets\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving quantized model to model/mobnet_model_quantized.tflite\n",
+      "\n",
+      "Model size comparison:\n",
+      "Original model size: 23.44 MB\n",
+      "Quantized model size: 4.27 MB\n",
+      "Size reduction: 81.79%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create output directory if it doesn't exist\n",
+    "pathlib.Path(output_path).parent.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# Run quantization\n",
+    "quantize_model(model_path, output_path)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

model/cnn_model.keras

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+size 8488520

model/mobnet_model.keras

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+oid sha256:09aa4a182f3d6a868b18ec980b794044ffd2ba7b5c1755d157891857376d2215
+size 24577292

model/mobnet_model_quantized.tflite

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+oid sha256:411f792d9283c27975e30d264717f1c900e073087429ba631571af6e2c561369
+size 4474992

requirements.txt

@@ -0,0 +1,13 @@
+tqdm==4.66.5
+imutils==0.5.4
+numpy==1.26.4
+pandas==2.0.3
+pillow==10.4.0
+matplotlib==3.7.3
+seaborn==0.11.0
+albumentations==1.4.1
+opencv-python==4.10.0.84
+tensorflow==2.15.1
+keras==2.15.1
+scikit-learn==1.2.2
+wandb==0.19.1