diff --git a/tutorials/notebooks/imx500_notebooks/keras/keras_yolov8n_for_imx500.ipynb b/tutorials/notebooks/imx500_notebooks/keras/keras_yolov8n_for_imx500.ipynb
index 96a517a8b..4150d2153 100644
--- a/tutorials/notebooks/imx500_notebooks/keras/keras_yolov8n_for_imx500.ipynb
+++ b/tutorials/notebooks/imx500_notebooks/keras/keras_yolov8n_for_imx500.ipynb
@@ -28,18 +28,22 @@
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## Setup\n",
-    "### Install the relevant packages"
-   ],
+   "id": "d74f9c855ec54081",
    "metadata": {
     "collapsed": false
    },
-   "id": "d74f9c855ec54081"
+   "source": [
+    "## Setup\n",
+    "### Install the relevant packages"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "7c7fa04c9903736f",
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "TF_VER = '2.14.0'\n",
@@ -47,25 +51,25 @@
     "!pip install -q tensorflow=={TF_VER}\n",
     "!pip install -q pycocotools\n",
     "!pip install 'huggingface-hub<=0.21.4'"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "7c7fa04c9903736f"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "Install MCT (if it’s not already installed). Additionally, in order to use all the necessary utility functions for this tutorial, we also copy [MCT tutorials folder](https://github.com/sony/model_optimization/tree/main/tutorials) and add it to the system path."
-   ],
+   "id": "57717bc8f59a0d85",
    "metadata": {
     "collapsed": false
    },
-   "id": "57717bc8f59a0d85"
+   "source": [
+    "Install MCT (if it’s not already installed). Additionally, in order to use all the necessary utility functions for this tutorial, we also copy [MCT tutorials folder](https://github.com/sony/model_optimization/tree/main/tutorials) and add it to the system path."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "9728247bc20d0600",
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import sys\n",
@@ -76,25 +80,25 @@
     "    !pip install model_compression_toolkit\n",
     "!git clone https://github.com/sony/model_optimization.git temp_mct && mv temp_mct/tutorials . && \\rm -rf temp_mct\n",
     "sys.path.insert(0,\"tutorials\")"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "9728247bc20d0600"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "### Download COCO evaluation set"
-   ],
+   "id": "7a1038b9fd98bba2",
    "metadata": {
     "collapsed": false
    },
-   "id": "7a1038b9fd98bba2"
+   "source": [
+    "### Download COCO evaluation set"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "8bea492d71b4060f",
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "if not os.path.isdir('coco'):\n",
@@ -104,11 +108,7 @@
     "    !wget -nc http://images.cocodataset.org/zips/val2017.zip\n",
     "    !unzip -q -o val2017.zip -d ./coco\n",
     "    !echo Done loading val2017 images"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "8bea492d71b4060f"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -149,6 +149,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "56393342-cecf-4f64-b9ca-2f515c765942",
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import model_compression_toolkit as mct\n",
@@ -213,38 +217,38 @@
     "                                                          core_config=config,\n",
     "                                                          target_platform_capabilities=tpc)\n",
     "print('Quantized model is ready')"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "56393342-cecf-4f64-b9ca-2f515c765942"
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "3be2016acdc9da60",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "### Model Export\n",
     "\n",
     "Now, we can export the quantized model, ready for deployment, into a `.keras` format file. Please ensure that the `save_model_path` has been set correctly. "
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "3be2016acdc9da60"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "outputs": [],
-   "source": [
-    "mct.exporter.keras_export_model(model=quant_model, save_model_path='./qmodel.keras')"
-   ],
+   "id": "72dd885c7b92fa93",
    "metadata": {
     "collapsed": false
    },
-   "id": "72dd885c7b92fa93"
+   "outputs": [],
+   "source": [
+    "mct.exporter.keras_export_model(model=quant_model, save_model_path='./qmodel.keras')"
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "ba1ade49894e4e22",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "\n",
     "### Gradient-Based Post Training Quantization using Model Compression Toolkit\n",
@@ -252,20 +256,24 @@
     "**Please note that this section is computationally heavy, and it's recommended to run it on a GPU. For fast deployment, you may choose to skip this step.** \n",
     "\n",
     "We will start by loading the COCO training set, and re-define the representative dataset accordingly. "
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "ba1ade49894e4e22"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "5276ec7291d28603",
+   "metadata": {
+    "collapsed": false,
+    "tags": [
+     "long_run"
+    ]
+   },
    "outputs": [],
    "source": [
-    "!wget -nc http://images.cocodataset.org/zips/train2017.zip\n",
-    "!unzip -q -o train2017.zip -d ./coco\n",
-    "!echo Done loading train2017 images\n",
+    "if not os.path.isdir('coco/train2017'):\n",
+    "    !wget -nc http://images.cocodataset.org/zips/train2017.zip\n",
+    "    !unzip -q -o train2017.zip -d ./coco\n",
+    "    !echo Done loading train2017 images\n",
     "\n",
     "REPRESENTATIVE_DATASET_FOLDER = './coco/train2017/'\n",
     "REPRESENTATIVE_DATASET_ANNOTATION_FILE = './coco/annotations/instances_train2017.json'\n",
@@ -281,28 +289,28 @@
     "\n",
     "# Get representative dataset generator\n",
     "representative_dataset_gen = get_representative_dataset(n_iters, rep_data_loader)"
-   ],
-   "metadata": {
-    "tags": [
-     "long_run"
-    ],
-    "collapsed": false
-   },
-   "id": "5276ec7291d28603"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "Next, we'll set up the Gradient-Based PTQ configuration and execute the necessary MCT command. Keep in mind that this step can be time-consuming, depending on your runtime."
-   ],
+   "id": "fce524abd2f1e750",
    "metadata": {
     "collapsed": false
    },
-   "id": "fce524abd2f1e750"
+   "source": [
+    "Next, we'll set up the Gradient-Based PTQ configuration and execute the necessary MCT command. Keep in mind that this step can be time-consuming, depending on your runtime."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "30f0a0c1c497ba2",
+   "metadata": {
+    "collapsed": false,
+    "tags": [
+     "long_run"
+    ]
+   },
    "outputs": [],
    "source": [
     "# Specify the necessary configuration for Gradient-Based PTQ.\n",
@@ -319,14 +327,7 @@
     "    target_platform_capabilities=tpc)\n",
     "\n",
     "print('Quantized model is ready')"
-   ],
-   "metadata": {
-    "tags": [
-     "long_run"
-    ],
-    "collapsed": false
-   },
-   "id": "30f0a0c1c497ba2"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -399,6 +400,10 @@
   },
   {
    "cell_type": "markdown",
+   "id": "6d93352843a27433",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "\\\n",
     "Copyright 2024 Sony Semiconductor Israel, Inc. All rights reserved.\n",
@@ -414,14 +419,13 @@
     "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
     "See the License for the specific language governing permissions and\n",
     "limitations under the License."
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "6d93352843a27433"
+   ]
   }
  ],
  "metadata": {
+  "colab": {
+   "provenance": []
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -438,9 +442,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.7"
-  },
-  "colab": {
-   "provenance": []
   }
  },
  "nbformat": 4,