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Merge pull request Gagandeep-2003#64 from KaiAllAlone/main Add ResNet50-based drowsiness detection training notebook
- Added Colab notebook for training a ResNet50-based eye drowsiness detection model. - Includes dataset download steps, model architecture, training logs, and save/load examples. - Added README in models/ folder describing setup and usage. - No changes to existing application code.
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‎models/ML_Models/ResNET+CNN (Tranfer Learning)/Drowsiness.ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"gpuType": "T4"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "165vZIxs7OhN",
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"outputId": "c1b4a906-8735-43f8-a1c1-1b2cb0e1ba3f"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Downloading from https://www.kaggle.com/api/v1/datasets/download/akashshingha850/mrl-eye-dataset?dataset_version_number=4...\n"
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]
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},
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"100%|██████████| 329M/329M [00:02<00:00, 160MB/s]"
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]
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},
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Extracting files...\n"
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]
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},
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"\n"
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]
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},
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Path to dataset files: /root/.cache/kagglehub/datasets/akashshingha850/mrl-eye-dataset/versions/4\n"
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]
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}
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],
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"source": [
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"import kagglehub\n",
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"\n",
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"# Download latest version\n",
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"path = kagglehub.dataset_download(\"akashshingha850/mrl-eye-dataset\")\n",
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"\n",
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"print(\"Path to dataset files:\", path)"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import os\n",
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"os.listdir(\"/root/.cache/kagglehub/datasets/akashshingha850/mrl-eye-dataset/versions/4/data/val\")"
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],
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"metadata": {
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"id": "ENSZig9m8QdU",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "9a77fdf9-0bb1-4fc8-f3d7-2248fd4ec97b"
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},
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"['sleepy', 'awake']"
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]
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},
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"metadata": {},
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"execution_count": 2
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"train_folder_path='/root/.cache/kagglehub/datasets/akashshingha850/mrl-eye-dataset/versions/4/data/train'\n",
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"test_folder_path='/root/.cache/kagglehub/datasets/akashshingha850/mrl-eye-dataset/versions/4/data/test'\n",
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"val_folder_path=\"/root/.cache/kagglehub/datasets/akashshingha850/mrl-eye-dataset/versions/4/data/val\"\n",
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"# Device setup"
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],
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"metadata": {
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"id": "dCvWRivkHYp_"
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},
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"execution_count": 3,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"import tensorflow as tf\n",
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"import matplotlib.pyplot as plt\n",
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"from tensorflow.keras import layers, models\n",
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"print(f\"TensorFlow version: {tf.__version__}\")\n",
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"\n",
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"# GPU Configuration for Google Colab\n",
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"def configure_gpu():\n",
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" gpus = tf.config.experimental.list_physical_devices('GPU')\n",
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" if gpus:\n",
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" try:\n",
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" for gpu in gpus:\n",
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" tf.config.experimental.set_memory_growth(gpu, True)\n",
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" print(f\"✅ GPU configured successfully! Available GPUs: {len(gpus)}\")\n",
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" print(f\"GPU details: {gpus[0]}\")\n",
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" return True\n",
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" except RuntimeError as e:\n",
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" print(f\"❌ GPU configuration failed: {e}\")\n",
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" return False\n",
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" else:\n",
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" print(\"ℹ️ No GPU available, using CPU\")\n",
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" return False\n",
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"gpu_available = configure_gpu()"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "bWsXYeSNWYs_",
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"outputId": "eef97b24-ae93-44ec-8fd9-a93328222fbe"
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},
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"TensorFlow version: 2.18.0\n",
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"✅ GPU configured successfully! Available GPUs: 1\n",
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"GPU details: PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"\n",
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"IMG_SIZE = (224, 224)\n",
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"BATCH_SIZE = 16\n",
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"EPOCHS = 5\n",
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"\n",
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"train_ds = tf.keras.utils.image_dataset_from_directory(\n",
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" train_folder_path,\n",
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" image_size=IMG_SIZE,\n",
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" batch_size=BATCH_SIZE,\n",
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" validation_split=0.2,\n",
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" subset='training',\n",
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" label_mode='categorical',\n",
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" seed=42\n",
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")\n",
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"\n",
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"val_ds = tf.keras.utils.image_dataset_from_directory(\n",
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" val_folder_path,\n",
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" image_size=IMG_SIZE,\n",
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" batch_size=BATCH_SIZE,\n",
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" validation_split=0.2,\n",
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" subset='validation',\n",
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" label_mode='categorical',\n",
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" seed=42\n",
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")\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "241PAJT1MZ0v",
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"outputId": "bcc33ec3-db75-4049-e135-2a302d6bf87f"
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},
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"execution_count": 11,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Found 50937 files belonging to 2 classes.\n",
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"Using 40750 files for training.\n",
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"Found 16980 files belonging to 2 classes.\n",
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"Using 3396 files for validation.\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"base_model = tf.keras.applications.ResNet50(\n",
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" input_shape=IMG_SIZE + (3,),\n",
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" include_top=False,\n",
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" weights='imagenet'\n",
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")\n",
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"base_model.trainable = False #freeze base\n",
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"\n",
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"model = models.Sequential([\n",
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" base_model,\n",
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" layers.GlobalAveragePooling2D(),\n",
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" layers.Dense(128, activation='relu'),\n",
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" layers.Dropout(0.3),\n",
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" layers.Dense(2, activation='softmax') # classification\n",
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"])\n",
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"\n",
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"model.compile(optimizer='adam',\n",
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" loss='binary_crossentropy',\n",
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" metrics=['accuracy'])"
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],
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"metadata": {
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"id": "tg3s90D8OIkV"
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},
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"execution_count": 15,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"history = model.fit(\n",
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" train_ds,\n",
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" validation_data=val_ds,\n",
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" epochs=EPOCHS,\n",
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" verbose=1\n",
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")"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "o5cxEgMDVBHE",
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"outputId": "7fed8812-8d9d-414a-b19c-378569cecaab"
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},
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"execution_count": 16,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Epoch 1/5\n",
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"\u001b[1m2547/2547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 50ms/step - accuracy: 0.9445 - loss: 0.1492 - val_accuracy: 0.9753 - val_loss: 0.0677\n",
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"Epoch 2/5\n",
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"\u001b[1m2547/2547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m118s\u001b[0m 46ms/step - accuracy: 0.9669 - loss: 0.0893 - val_accuracy: 0.9756 - val_loss: 0.0598\n",
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"Epoch 3/5\n",
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"\u001b[1m2547/2547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 47ms/step - accuracy: 0.9712 - loss: 0.0774 - val_accuracy: 0.9761 - val_loss: 0.0590\n",
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"Epoch 4/5\n",
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"\u001b[1m2547/2547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 47ms/step - accuracy: 0.9744 - loss: 0.0686 - val_accuracy: 0.9806 - val_loss: 0.0500\n",
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"Epoch 5/5\n",
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"\u001b[1m2547/2547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 46ms/step - accuracy: 0.9776 - loss: 0.0600 - val_accuracy: 0.9850 - val_loss: 0.0415\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"os.mkdir('/content/saved_model')"
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],
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"metadata": {
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"id": "YTuUHm9DbGq1"
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},
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"execution_count": 17,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"model.save('/content/saved_model/resnet.keras')"
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],
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"metadata": {
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"id": "MiDvSrwcXDvX"
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},
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"execution_count": 19,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "Q_IZYp52bQOC"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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}

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