diff --git a/.gitignore b/.gitignore
index 100a3a7..576caad 100644
--- a/.gitignore
+++ b/.gitignore
@@ -140,3 +140,4 @@ venv/
 *.pyc
 .vscode/
 __pyc
+Images/
\ No newline at end of file
diff --git a/Model-Training/Evaluate_model_1.ipynb b/Model-Training/Evaluate_model_1.ipynb
new file mode 100644
index 0000000..f3e43b7
--- /dev/null
+++ b/Model-Training/Evaluate_model_1.ipynb
@@ -0,0 +1,3917 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "view-in-github"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Devasy23/FaceRec/blob/feat%2FModel-evaluation/Model-Training/Evaluate_model_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7dpZF1CUCJjJ"
+      },
+      "source": [
+        "# Pre-setup\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ca5SxWfjCGjM",
+        "outputId": "7907eaeb-6cfb-49ae-a26e-775e0c83c2e6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting deepface\n",
+            "  Downloading deepface-0.0.92-py3-none-any.whl (105 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.5/105.5 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: requests>=2.27.1 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.31.0)\n",
+            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (1.25.2)\n",
+            "Requirement already satisfied: pandas>=0.23.4 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.0.3)\n",
+            "Requirement already satisfied: gdown>=3.10.1 in /usr/local/lib/python3.10/dist-packages (from deepface) (5.1.0)\n",
+            "Requirement already satisfied: tqdm>=4.30.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (4.66.4)\n",
+            "Requirement already satisfied: Pillow>=5.2.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (9.4.0)\n",
+            "Requirement already satisfied: opencv-python>=4.5.5.64 in /usr/local/lib/python3.10/dist-packages (from deepface) (4.8.0.76)\n",
+            "Requirement already satisfied: tensorflow>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.15.0)\n",
+            "Requirement already satisfied: keras>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.15.0)\n",
+            "Requirement already satisfied: Flask>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.2.5)\n",
+            "Collecting mtcnn>=0.1.0 (from deepface)\n",
+            "  Downloading mtcnn-0.1.1-py3-none-any.whl (2.3 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting retina-face>=0.0.1 (from deepface)\n",
+            "  Downloading retina_face-0.0.17-py3-none-any.whl (25 kB)\n",
+            "Collecting fire>=0.4.0 (from deepface)\n",
+            "  Downloading fire-0.6.0.tar.gz (88 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.4/88.4 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting gunicorn>=20.1.0 (from deepface)\n",
+            "  Downloading gunicorn-22.0.0-py3-none-any.whl (84 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.4/84.4 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from fire>=0.4.0->deepface) (1.16.0)\n",
+            "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from fire>=0.4.0->deepface) (2.4.0)\n",
+            "Requirement already satisfied: Werkzeug>=2.2.2 in /usr/local/lib/python3.10/dist-packages (from Flask>=1.1.2->deepface) (3.0.3)\n",
+            "Requirement already satisfied: Jinja2>=3.0 in /usr/local/lib/python3.10/dist-packages (from Flask>=1.1.2->deepface) (3.1.4)\n",
+            "Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.10/dist-packages (from Flask>=1.1.2->deepface) (2.2.0)\n",
+            "Requirement already satisfied: click>=8.0 in /usr/local/lib/python3.10/dist-packages (from Flask>=1.1.2->deepface) (8.1.7)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown>=3.10.1->deepface) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown>=3.10.1->deepface) (3.15.4)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from gunicorn>=20.1.0->deepface) (24.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23.4->deepface) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23.4->deepface) (2023.4)\n",
+            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23.4->deepface) (2024.1)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (3.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (2024.7.4)\n",
+            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (1.4.0)\n",
+            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (1.6.3)\n",
+            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (24.3.25)\n",
+            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (0.6.0)\n",
+            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (0.2.0)\n",
+            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (3.9.0)\n",
+            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (18.1.1)\n",
+            "Requirement already satisfied: ml-dtypes~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (0.2.0)\n",
+            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (3.3.0)\n",
+            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (3.20.3)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (67.7.2)\n",
+            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (4.12.2)\n",
+            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (1.14.1)\n",
+            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (0.37.1)\n",
+            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (1.64.1)\n",
+            "Requirement already satisfied: tensorboard<2.16,>=2.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (2.15.2)\n",
+            "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (2.15.0)\n",
+            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow>=1.9.0->deepface) (0.43.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=3.0->Flask>=1.1.2->deepface) (2.1.5)\n",
+            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (2.27.0)\n",
+            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (1.2.1)\n",
+            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (3.6)\n",
+            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (0.7.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown>=3.10.1->deepface) (2.5)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (1.7.1)\n",
+            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (5.3.3)\n",
+            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (0.4.0)\n",
+            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (4.9)\n",
+            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (1.3.1)\n",
+            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (0.6.0)\n",
+            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (3.2.2)\n",
+            "Building wheels for collected packages: fire\n",
+            "  Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for fire: filename=fire-0.6.0-py2.py3-none-any.whl size=117029 sha256=261de37dafc0ba41a167bbcd369b0c8653e13b40919b353f0c24285bcdf0a8e4\n",
+            "  Stored in directory: /root/.cache/pip/wheels/d6/6d/5d/5b73fa0f46d01a793713f8859201361e9e581ced8c75e5c6a3\n",
+            "Successfully built fire\n",
+            "Installing collected packages: gunicorn, fire, mtcnn, retina-face, deepface\n",
+            "Successfully installed deepface-0.0.92 fire-0.6.0 gunicorn-22.0.0 mtcnn-0.1.1 retina-face-0.0.17\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install deepface"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KxpbRTJDCGjU"
+      },
+      "source": [
+        "## Downloading Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NI6P1qt_h_hm",
+        "outputId": "d9ca9d2d-61f6-421a-fcba-51d5439c666e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "--2024-07-16 14:54:42--  http://vis-www.cs.umass.edu/lfw/lfw.tgz\n",
+            "Resolving vis-www.cs.umass.edu (vis-www.cs.umass.edu)... 128.119.244.95\n",
+            "Connecting to vis-www.cs.umass.edu (vis-www.cs.umass.edu)|128.119.244.95|:80... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 180566744 (172M) [application/x-gzip]\n",
+            "Saving to: ‘lfw.tgz’\n",
+            "\n",
+            "lfw.tgz             100%[===================>] 172.20M  1.74MB/s    in 88s     \n",
+            "\n",
+            "2024-07-16 14:56:11 (1.95 MB/s) - ‘lfw.tgz’ saved [180566744/180566744]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "!wget http://vis-www.cs.umass.edu/lfw/lfw.tgz"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JgJ3e_jmh_X3",
+        "outputId": "6ad93f39-276b-4d15-f41d-eb39ed5416a3"
+      },
+      "outputs": [],
+      "source": [
+        "!tar -xzvf lfw.tgz"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1X2EFr3WiOKo",
+        "outputId": "595f7fa6-9a69-424f-b910-e4e8063ae804"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Removed 411 folders\n",
+            "Remaining folders: 12\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "import shutil\n",
+        "count=0\n",
+        "for folder in os.listdir('lfw'):\n",
+        "    if len(os.listdir(f'lfw/{folder}')) < 50:\n",
+        "        shutil.rmtree(f'lfw/{folder}')\n",
+        "        count+=1\n",
+        "print(f\"Removed {count} folders\")\n",
+        "print(f\"Remaining folders: {len(os.listdir('lfw'))}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pz1vtNniCGjf"
+      },
+      "source": [
+        "## Downloading Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 384,
+          "referenced_widgets": [
+            "05b31ee914704cf6aa23b016e9605fa6",
+            "626e68b358004c4e92eef018313f703f",
+            "0a2e149c631d4c0c9723e3dc20cef824",
+            "3f149795afdc4f72bfd4819c2dd24e7e",
+            "5acf74fda9cd44399872a32f7e797d53",
+            "b77e47d27983498a9d5cc1b8d7a6f0a6",
+            "b37c1463ede74729998741f9ca74b9a0",
+            "09f275e257dc4f35887c189de0d0eeae",
+            "a83437e262784123ad7ab7356de9f4ad",
+            "b831e7f5cb914676b1579fc31e0466f5",
+            "b58b054a34ac4cfdb06561b956329fdb",
+            "91d1a5c7baca43f09729be8b10c0fac9",
+            "a7c63853647c43c9a98fb2c34229c25b",
+            "f32b27d0a22f442484533f98a7de6f42",
+            "e6e30d397e8d462cbfb01d22878b212e",
+            "55654d4205474d72a91581407194d839",
+            "d2ee1ee53b10436e99cd3cc7c6c23c1e",
+            "56026f29dfe44623b29bea01eae5a9f3",
+            "62e3046887594c5a8492daf015dfa1ea",
+            "1941abf3e1414fb0b7651cadee353a7c",
+            "3810d05f97ab46d2a7febde0ff055e2f",
+            "2185e42006c94abaa2d33786851bfa4d",
+            "d61f94c1b2e24bfb8bd491a7ad9869f1",
+            "71679224e9d841d1938df73a32764c47",
+            "476c1471d9fc4c7dad180d3ce7d5a081",
+            "522947b5b08940b789cf05f354272787",
+            "3831c6a3d72c4084ab38e755137d94c9",
+            "38049d813cfc4ef8af57584efa87578b",
+            "60df876c4d8742ff959ea20b8b509cdb",
+            "359b1bc569834267a81037494578c1bd",
+            "7430967a5b0f47e28715bc6d720f946d",
+            "b21c359b208448a298daf97c9371d6ab",
+            "0ceee2cb7916489ea96520ce488598a1",
+            "5e1636ca1090486cad4cf157c618e986",
+            "532a0bbeace044a792951905c6848e75",
+            "0d15f4bdda034d8ab4349fc3ec35eb55",
+            "bc3ef7720305454f9908fc32e48b529c",
+            "b7f0ca5b9f9b42ddb387aeb9271d80b2",
+            "21224c28636a40518f0f4e182080ae2b",
+            "8c5ddf1e3ec0415cb8b699fe967b347a",
+            "05236173ce514490b2f887ff6cd6724b",
+            "a901f57a165744c3a264f06b8dee6bde",
+            "1191b2aed06f43dba32cd82c5f40d76c",
+            "dbce9fbdde3143a188adcbfc086f163c",
+            "1e74eb5ac4c144b9996faecafdb8c77d",
+            "ec174d63fcaf47bea231121f9d7c835b",
+            "ce02aaaf5cee446590ce193045d1a350",
+            "10e73eb764bf4389ae2b1dc2efba61cb",
+            "dbd8dc288a144745bf13d9a2806198da",
+            "292bad649ae14135918d80162ba48527",
+            "6b2779101e0e484bba7da98bb7b605e1",
+            "97e4dcd5682445199f7fe384c87d295c",
+            "fe301e37724b4ffb9de9706695a2bd3a",
+            "0bf1466e9ffd426d827c78b8a3c00f86",
+            "e7abb91f1de0476d8a84c211ce8453a7",
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+            "3553e7cf1c5f4f35aa5f78c752c8e975",
+            "5d75e652f0904922b26d50383ad1df35",
+            "e4c6d269317d437486eaa9f3928a0d2d",
+            "c7ded8aba8dc4edcafb906cd0bdc5ed8",
+            "f0f1003bf31446e6a8f3eb71cb31c3ee",
+            "2ca928edee654a38a36b15bb478af58e",
+            "96e54e6938a04e2fa17675f3c008febc",
+            "47f7bf82c3f84b2e9f65b8445bb27c8f",
+            "74656211c6e44f19bae09abf7ed343ba",
+            "13e112a64677495a8764ee5a77d71d82",
+            "1037e6dd39494d96842994d401595ae7",
+            "f619b0b1edd949b9a685e7d67f3c3f55",
+            "39f619ac7c6347429328df70128b02f9",
+            "a7aae10f6e9347449a85b6513d396b34",
+            "d954233ab30c4e978f253bb560df52a5",
+            "3d41dd07c7b142608e44c4dadf87932c",
+            "8c9ec1f6cb3c4e17bc1df1101b1bc8f7",
+            "be69d2dd1b634a4cb2c4515f293d6687",
+            "fd64018e8af14baeb5d85f9c531a1a51",
+            "be766277e81847318e883b2b90ef4fd1",
+            "75b02f006ff749d98787f5a4465fdfb4"
+          ]
+        },
+        "id": "BzMfQwalCGji",
+        "outputId": "73e0b9b9-4580-4ee7-dc47-d3e4c052001a"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "05b31ee914704cf6aa23b016e9605fa6",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Fetching 6 files:   0%|          | 0/6 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "91d1a5c7baca43f09729be8b10c0fac9",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "variables/variables.index:   0%|          | 0.00/61.2k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d61f94c1b2e24bfb8bd491a7ad9869f1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              ".gitattributes:   0%|          | 0.00/1.59k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5e1636ca1090486cad4cf157c618e986",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "saved_model.pb:   0%|          | 0.00/18.1M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1e74eb5ac4c144b9996faecafdb8c77d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "keras_metadata.pb:   0%|          | 0.00/2.52M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d9999a65dfac41d1b9fc79fdcfd53bde",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "fingerprint.pb:   0%|          | 0.00/56.0 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1037e6dd39494d96842994d401595ae7",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "variables.data-00000-of-00001:   0%|          | 0.00/282M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'/content/model'"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "finetuned_model_dir = \"model\"\n",
+        "\n",
+        "from huggingface_hub import snapshot_download\n",
+        "snapshot_download(repo_id='DnD11/FaceNet_Finetuned', repo_type=\"model\", local_dir=finetuned_model_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mlEhvK1rh_IX"
+      },
+      "source": [
+        "# Evaluation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 396,
+          "referenced_widgets": [
+            "b68785aeca464305860cafdc3655e145",
+            "c7ca6164c583495f94d50e14282d158a",
+            "2d562486f2f74fa486d6b586eae2a4d6",
+            "5dcd74f46b4a4ecdb476861f4e3e3135",
+            "01661e7fe0ca424a9769a2d26d44f6d3",
+            "1241ed7c396d41b695861a59dfa9d084",
+            "4174bd3bf85a40189640b840e54b67e7",
+            "9611c4fbb68144049f0e7fb69dbc182f",
+            "1166c616bbec4bf7a41273dbdfbd3c07",
+            "199608c95b13456997b7825a63e2befc",
+            "4b57a5366893453faad18d505bea3b2c",
+            "722763cb7d6d4f2ea2498132b5467d75",
+            "ca816622d3af499887e448686db2c529",
+            "1060c944805f48059e483f5fae479cc9",
+            "082f83c1baf244dba00ccf41544a55ca",
+            "683af30254c54861849c960497caeb96",
+            "d4758b6f3eaa4783b623cea9d0b121a3",
+            "b9c7ec701603422d99d0b31f8b5a2e66",
+            "53a61d664c41431faafcff1fcbc4d5d0",
+            "a3b95b241fec40309afdc8e127321dc9",
+            "92be0e62d22947a391b291c36ccfc9d6",
+            "a1afc23544124040bd2fce77b3103850"
+          ]
+        },
+        "id": "v5_9g5wwh0oi",
+        "outputId": "cce3ae28-8eb6-4ff7-82b4-5117fb586285"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b68785aeca464305860cafdc3655e145",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/12 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "722763cb7d6d4f2ea2498132b5467d75",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/12 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "ename": "AttributeError",
+          "evalue": "'_UserObject' object has no attribute 'predict'",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-12-c0c32cf843e7>\u001b[0m in \u001b[0;36m<cell line: 67>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;31m# Load the fine-tuned model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0mfinetuned_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaved_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexport_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfinetuned_model_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m \u001b[0membeddings_finetuned\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mgenerate_embeddings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinetuned_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;31m# Calculate intra-cluster distances for both models\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-12-c0c32cf843e7>\u001b[0m in \u001b[0;36mgenerate_embeddings\u001b[0;34m(model, dataset_path, deepface)\u001b[0m\n\u001b[1;32m     32\u001b[0m                 \u001b[0membedding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDeepFace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepresent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimg_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Facenet512\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menforce_detection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'embedding'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m                 \u001b[0membedding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m             \u001b[0membeddings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclass_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0membeddings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclass_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclass_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mAttributeError\u001b[0m: '_UserObject' object has no attribute 'predict'"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "import numpy as np\n",
+        "from tqdm import tqdm_notebook\n",
+        "from keras.models import load_model\n",
+        "from keras.preprocessing import image\n",
+        "from sklearn.metrics.pairwise import euclidean_distances\n",
+        "from deepface import DeepFace\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "# Function to load and preprocess images\n",
+        "def load_and_preprocess_image(img_path, target_size=(160, 160)):\n",
+        "    img = image.load_img(img_path, target_size=target_size)\n",
+        "    img_array = image.img_to_array(img)\n",
+        "    img_array = np.expand_dims(img_array, axis=0)\n",
+        "    img_array /= 255.0\n",
+        "    return img_array\n",
+        "\n",
+        "# Function to generate embeddings\n",
+        "def generate_embeddings(model, dataset_path, deepface = False):\n",
+        "    embeddings = {}\n",
+        "\n",
+        "    for class_name in tqdm_notebook(os.listdir(dataset_path)):\n",
+        "        class_path = os.path.join(dataset_path, class_name)\n",
+        "        if not os.path.isdir(class_path):\n",
+        "            continue\n",
+        "        embeddings[class_name] = []\n",
+        "\n",
+        "        for img_name in os.listdir(class_path):\n",
+        "            img_path = os.path.join(class_path, img_name)\n",
+        "            img_array = load_and_preprocess_image(img_path)\n",
+        "            if deepface:\n",
+        "                embedding = DeepFace.represent(img_path=img_path, model_name = \"Facenet512\", enforce_detection=False)[0]['embedding']\n",
+        "            else:\n",
+        "                embedding = model.predict(img_array)\n",
+        "            embeddings[class_name].append(embedding)\n",
+        "        embeddings[class_name] = np.array(embeddings[class_name])\n",
+        "\n",
+        "\n",
+        "\n",
+        "    return embeddings\n",
+        "\n",
+        "# Function to calculate intra-cluster distances\n",
+        "def calculate_intra_cluster_metrics(embeddings):\n",
+        "    metrics = {}\n",
+        "\n",
+        "    for label, cluster_embeddings in embeddings.items():\n",
+        "        \n",
+        "        avg_embedding = np.mean(cluster_embeddings, axis=0)\n",
+        "        distances = euclidean_distances(cluster_embeddings, [avg_embedding])\n",
+        "        max_distance = np.max(distances)\n",
+        "        min_distance = np.min(distances)\n",
+        "        variance = np.var(distances)\n",
+        "        standard_deviation = np.std(distances)\n",
+        "        percentile_25 = np.percentile(distances, 25)\n",
+        "        percentile_50 = np.percentile(distances, 50)\n",
+        "        percentile_75 = np.percentile(distances, 75)\n",
+        "        \n",
+        "        metrics[label] = {\"center\": avg_embedding, \"max_radius\": max_distance, \"min_radius\":min_distance, \"num_samples\": len(cluster_embeddings), \"variance\": variance, \"standard_deviation\": standard_deviation, \"percentile_25\": percentile_25, \"percentile_50\": percentile_50, \"percentile_75\": percentile_75}\n",
+        "\n",
+        "    return metrics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Path to the dataset\n",
+        "dataset_path = 'lfw'\n",
+        "model=1\n",
+        "# Generate embeddings for the original model\n",
+        "embeddings_original = generate_embeddings(model, dataset_path, True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Load the fine-tuned model\n",
+        "finetuned_model = tf.keras.models.load_model('/content/model/embedding_trial3.h5')\n",
+        "embeddings_finetuned = generate_embeddings(finetuned_model, dataset_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Calculate Metrics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Calculate intra-cluster distances for both models\n",
+        "metrics_original = calculate_intra_cluster_metrics(embeddings_original)\n",
+        "metrics_finetuned = calculate_intra_cluster_metrics(embeddings_finetuned)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def plot_histogram(metrics, key, title, xlabel):\n",
+        "    values = [metrics[label][key] for label in metrics]\n",
+        "    plt.figure()\n",
+        "    plt.hist(values, bins=20, alpha=0.7)\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('Frequency')\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_histogram(metrics_original, 'max_radius', 'Histogram of Max Radius (Original Model)', 'Max Radius')\n",
+        "plot_histogram(metrics_finetuned, 'max_radius', 'Histogram of Max Radius (Fine-tuned Model)', 'Max Radius')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def plot_boxplot(metrics, key, title, ylabel):\n",
+        "    values = [metrics[label][key] for label in metrics]\n",
+        "    plt.figure()\n",
+        "    plt.boxplot(values)\n",
+        "    plt.title(title)\n",
+        "    plt.ylabel(ylabel)\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_boxplot(metrics_original, 'percentile_50', 'Boxplot of Median Distances (Original Model)', 'Median Distance')\n",
+        "plot_boxplot(metrics_finetuned, 'percentile_50', 'Boxplot of Median Distances (Fine-tuned Model)', 'Median Distance')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def compare_metrics(metrics_original, metrics_finetuned, key):\n",
+        "    changes = {}\n",
+        "    for label in metrics_original:\n",
+        "        if label in metrics_finetuned:\n",
+        "            change = metrics_finetuned[label][key] - metrics_original[label][key]\n",
+        "            changes[label] = change\n",
+        "    return changes\n",
+        "\n",
+        "max_radius_change = compare_metrics(metrics_original, metrics_finetuned, 'max_radius')\n",
+        "plot_histogram(max_radius_change, 'max_radius', 'Histogram of Max Radius Change', 'Max Radius Change')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def calculate_inter_cluster_metrics(embeddings):\n",
+        "    centers = {label: metrics['center'] for label, metrics in embeddings.items()}\n",
+        "    labels = list(centers.keys())\n",
+        "    inter_cluster_distances = euclidean_distances(list(centers.values()))\n",
+        "    \n",
+        "    metrics = {}\n",
+        "    for i, label1 in enumerate(labels):\n",
+        "        for j, label2 in enumerate(labels):\n",
+        "            if i >= j:\n",
+        "                continue\n",
+        "            distance = inter_cluster_distances[i, j]\n",
+        "            pair = tuple(sorted((label1, label2)))\n",
+        "            metrics[pair] = distance\n",
+        "\n",
+        "    return metrics\n",
+        "\n",
+        "inter_cluster_metrics_original = calculate_inter_cluster_metrics(metrics_original)\n",
+        "inter_cluster_metrics_finetuned = calculate_inter_cluster_metrics(metrics_finetuned)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def compare_inter_cluster_metrics(metrics_original, metrics_finetuned):\n",
+        "    changes = {}\n",
+        "    for pair in metrics_original:\n",
+        "        if pair in metrics_finetuned:\n",
+        "            change = metrics_finetuned[pair] - metrics_original[pair]\n",
+        "            changes[pair] = change\n",
+        "    return changes\n",
+        "\n",
+        "inter_cluster_distance_change = compare_inter_cluster_metrics(inter_cluster_metrics_original, inter_cluster_metrics_finetuned)\n",
+        "\n",
+        "def plot_inter_cluster_changes(inter_cluster_distance_change, title, xlabel):\n",
+        "    values = list(inter_cluster_distance_change.values())\n",
+        "    plt.figure()\n",
+        "    plt.hist(values, bins=20, alpha=0.7)\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('Frequency')\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_inter_cluster_changes(inter_cluster_distance_change, 'Histogram of Inter-Cluster Distance Changes', 'Inter-Cluster Distance Change')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Impact on faces"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def classify_impact(intra_metrics_original, intra_metrics_finetuned, inter_metrics_original, inter_metrics_finetuned):\n",
+        "    impact = {\n",
+        "        'positively_impacted': [],\n",
+        "        'negatively_impacted': [],\n",
+        "        'averagely_impacted': []\n",
+        "    }\n",
+        "\n",
+        "    # Define thresholds for considering a change as significant\n",
+        "    threshold = 0.01  # You can adjust this value based on your needs\n",
+        "\n",
+        "    # Classify intra-cluster metric changes\n",
+        "    for label in intra_metrics_original:\n",
+        "        if label in intra_metrics_finetuned:\n",
+        "            max_radius_change = intra_metrics_finetuned[label]['max_radius'] - intra_metrics_original[label]['max_radius']\n",
+        "            if max_radius_change < -threshold:\n",
+        "                impact['positively_impacted'].append((label, 'max_radius'))\n",
+        "            elif max_radius_change > threshold:\n",
+        "                impact['negatively_impacted'].append((label, 'max_radius'))\n",
+        "            else:\n",
+        "                impact['averagely_impacted'].append((label, 'max_radius'))\n",
+        "            \n",
+        "            # Add other metrics here if needed\n",
+        "            # Example: variance, standard_deviation, etc.\n",
+        "            variance_change = intra_metrics_finetuned[label]['variance'] - intra_metrics_original[label]['variance']\n",
+        "            if variance_change < -threshold:\n",
+        "                impact['positively_impacted'].append((label, 'variance'))\n",
+        "            elif variance_change > threshold:\n",
+        "                impact['negatively_impacted'].append((label, 'variance'))\n",
+        "            else:\n",
+        "                impact['averagely_impacted'].append((label, 'variance'))\n",
+        "\n",
+        "    # Classify inter-cluster metric changes\n",
+        "    for pair in inter_metrics_original:\n",
+        "        if pair in inter_metrics_finetuned:\n",
+        "            distance_change = inter_metrics_finetuned[pair] - inter_metrics_original[pair]\n",
+        "            if distance_change < -threshold:\n",
+        "                impact['positively_impacted'].append((pair, 'inter_cluster_distance'))\n",
+        "            elif distance_change > threshold:\n",
+        "                impact['negatively_impacted'].append((pair, 'inter_cluster_distance'))\n",
+        "            else:\n",
+        "                impact['averagely_impacted'].append((pair, 'inter_cluster_distance'))\n",
+        "\n",
+        "    return impact\n",
+        "\n",
+        "# Assuming metrics_original and metrics_finetuned are dictionaries containing the calculated metrics\n",
+        "impact = classify_impact(metrics_original, metrics_finetuned, inter_cluster_metrics_original, inter_cluster_metrics_finetuned)\n",
+        "\n",
+        "# Print the impact classification\n",
+        "print(f\"Positively Impacted Faces: {len(impact['positively_impacted'])}\")\n",
+        "print(f\"Negatively Impacted Faces: {len(impact['negatively_impacted'])}\")\n",
+        "print(f\"Averagely Impacted Faces: {len(impact['averagely_impacted'])}\")\n",
+        "\n",
+        "# Optional: Print detailed impact for each face\n",
+        "for category, changes in impact.items():\n",
+        "    print(f\"\\n{category.capitalize()} Faces:\")\n",
+        "    for change in changes:\n",
+        "        print(change)\n"
+      ]
+    }
+  ],
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+}
diff --git a/Model-Training/Evaluate_model_II.ipynb b/Model-Training/Evaluate_model_II.ipynb
new file mode 100644
index 0000000..162b2f5
--- /dev/null
+++ b/Model-Training/Evaluate_model_II.ipynb
@@ -0,0 +1,14147 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Devasy23/FaceRec/blob/feat%2FModel-evaluation/Model-Training/Evaluate_model_II.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7dpZF1CUCJjJ"
+      },
+      "source": [
+        "# Pre-setup\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ca5SxWfjCGjM",
+        "outputId": "0a8afad8-5fc1-4abf-870e-87fbf648f7ce"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting deepface\n",
+            "  Downloading deepface-0.0.92-py3-none-any.whl (105 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.5/105.5 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: requests>=2.27.1 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.31.0)\n",
+            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (1.25.2)\n",
+            "Requirement already satisfied: pandas>=0.23.4 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.0.3)\n",
+            "Requirement already satisfied: gdown>=3.10.1 in /usr/local/lib/python3.10/dist-packages (from deepface) (5.1.0)\n",
+            "Requirement already satisfied: tqdm>=4.30.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (4.66.4)\n",
+            "Requirement already satisfied: Pillow>=5.2.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (9.4.0)\n",
+            "Requirement already satisfied: opencv-python>=4.5.5.64 in /usr/local/lib/python3.10/dist-packages (from deepface) (4.8.0.76)\n",
+            "Requirement already satisfied: tensorflow>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.15.0)\n",
+            "Requirement already satisfied: keras>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.15.0)\n",
+            "Requirement already satisfied: Flask>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.2.5)\n",
+            "Collecting mtcnn>=0.1.0 (from deepface)\n",
+            "  Downloading mtcnn-0.1.1-py3-none-any.whl (2.3 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting retina-face>=0.0.1 (from deepface)\n",
+            "  Downloading retina_face-0.0.17-py3-none-any.whl (25 kB)\n",
+            "Collecting fire>=0.4.0 (from deepface)\n",
+            "  Downloading fire-0.6.0.tar.gz (88 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.4/88.4 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting gunicorn>=20.1.0 (from deepface)\n",
+            "  Downloading gunicorn-22.0.0-py3-none-any.whl (84 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.4/84.4 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from fire>=0.4.0->deepface) (1.16.0)\n",
+            "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from fire>=0.4.0->deepface) (2.4.0)\n",
+            "Requirement already satisfied: Werkzeug>=2.2.2 in /usr/local/lib/python3.10/dist-packages (from Flask>=1.1.2->deepface) (3.0.3)\n",
+            "Requirement already satisfied: Jinja2>=3.0 in /usr/local/lib/python3.10/dist-packages (from Flask>=1.1.2->deepface) (3.1.4)\n",
+            "Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.10/dist-packages (from Flask>=1.1.2->deepface) (2.2.0)\n",
+            "Requirement already satisfied: click>=8.0 in /usr/local/lib/python3.10/dist-packages (from Flask>=1.1.2->deepface) (8.1.7)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown>=3.10.1->deepface) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown>=3.10.1->deepface) (3.15.4)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from gunicorn>=20.1.0->deepface) (24.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23.4->deepface) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23.4->deepface) (2023.4)\n",
+            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23.4->deepface) (2024.1)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (3.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (2024.7.4)\n",
+            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (1.4.0)\n",
+            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (1.6.3)\n",
+            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (24.3.25)\n",
+            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (0.6.0)\n",
+            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (0.2.0)\n",
+            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (3.9.0)\n",
+            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (18.1.1)\n",
+            "Requirement already satisfied: ml-dtypes~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (0.2.0)\n",
+            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (3.3.0)\n",
+            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (3.20.3)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (67.7.2)\n",
+            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (4.12.2)\n",
+            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (1.14.1)\n",
+            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (0.37.1)\n",
+            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (1.64.1)\n",
+            "Requirement already satisfied: tensorboard<2.16,>=2.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (2.15.2)\n",
+            "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (2.15.0)\n",
+            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow>=1.9.0->deepface) (0.43.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=3.0->Flask>=1.1.2->deepface) (2.1.5)\n",
+            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (2.27.0)\n",
+            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (1.2.1)\n",
+            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (3.6)\n",
+            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (0.7.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown>=3.10.1->deepface) (2.5)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (1.7.1)\n",
+            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (5.4.0)\n",
+            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (0.4.0)\n",
+            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (4.9)\n",
+            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (1.3.1)\n",
+            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (0.6.0)\n",
+            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (3.2.2)\n",
+            "Building wheels for collected packages: fire\n",
+            "  Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for fire: filename=fire-0.6.0-py2.py3-none-any.whl size=117029 sha256=67bc03a9e6bdcc6f65bb0b6581d1d41d76ee9413e12d009a71a4696a6b32e126\n",
+            "  Stored in directory: /root/.cache/pip/wheels/d6/6d/5d/5b73fa0f46d01a793713f8859201361e9e581ced8c75e5c6a3\n",
+            "Successfully built fire\n",
+            "Installing collected packages: gunicorn, fire, mtcnn, retina-face, deepface\n",
+            "Successfully installed deepface-0.0.92 fire-0.6.0 gunicorn-22.0.0 mtcnn-0.1.1 retina-face-0.0.17\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install deepface"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KxpbRTJDCGjU"
+      },
+      "source": [
+        "## Downloading Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NI6P1qt_h_hm",
+        "outputId": "dec9f11e-da1e-4019-b085-d7fb0ad763ad"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2024-07-20 05:17:12--  http://vis-www.cs.umass.edu/lfw/lfw.tgz\n",
+            "Resolving vis-www.cs.umass.edu (vis-www.cs.umass.edu)... 128.119.244.95\n",
+            "Connecting to vis-www.cs.umass.edu (vis-www.cs.umass.edu)|128.119.244.95|:80... failed: Connection timed out.\n",
+            "Retrying.\n",
+            "\n",
+            "--2024-07-20 05:19:24--  (try: 2)  http://vis-www.cs.umass.edu/lfw/lfw.tgz\n",
+            "Connecting to vis-www.cs.umass.edu (vis-www.cs.umass.edu)|128.119.244.95|:80... failed: Connection timed out.\n",
+            "Retrying.\n",
+            "\n",
+            "--2024-07-20 05:21:36--  (try: 3)  http://vis-www.cs.umass.edu/lfw/lfw.tgz\n",
+            "Connecting to vis-www.cs.umass.edu (vis-www.cs.umass.edu)|128.119.244.95|:80... failed: Connection timed out.\n",
+            "Retrying.\n",
+            "\n",
+            "--2024-07-20 05:23:48--  (try: 4)  http://vis-www.cs.umass.edu/lfw/lfw.tgz\n",
+            "Connecting to vis-www.cs.umass.edu (vis-www.cs.umass.edu)|128.119.244.95|:80... failed: Connection timed out.\n",
+            "Retrying.\n",
+            "\n",
+            "--2024-07-20 05:26:04--  (try: 5)  http://vis-www.cs.umass.edu/lfw/lfw.tgz\n",
+            "Connecting to vis-www.cs.umass.edu (vis-www.cs.umass.edu)|128.119.244.95|:80... ^C\n"
+          ]
+        }
+      ],
+      "source": [
+        "!wget http://vis-www.cs.umass.edu/lfw/lfw.tgz"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JgJ3e_jmh_X3",
+        "outputId": "d73c3143-1b81-409b-cfe8-57118ccee89a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
+            "lfw/Luis_Sanchez/\n",
+            "lfw/Luis_Sanchez/Luis_Sanchez_0001.jpg\n",
+            "lfw/Juliette_Binoche/\n",
+            "lfw/Juliette_Binoche/Juliette_Binoche_0001.jpg\n",
+            "lfw/Joel_Todd/\n",
+            "lfw/Joel_Todd/Joel_Todd_0001.jpg\n",
+            "lfw/Allan_Wagner/\n",
+            "lfw/Allan_Wagner/Allan_Wagner_0001.jpg\n",
+            "lfw/Alejandro_Fernandez/\n",
+            "lfw/Alejandro_Fernandez/Alejandro_Fernandez_0001.jpg\n",
+            "lfw/Guillaume_Depardieu/\n",
+            "lfw/Guillaume_Depardieu/Guillaume_Depardieu_0001.jpg\n",
+            "lfw/Delphine_Chuillot/\n",
+            "lfw/Delphine_Chuillot/Delphine_Chuillot_0001.jpg\n",
+            "lfw/Paul_LeClerc/\n",
+            "lfw/Paul_LeClerc/Paul_LeClerc_0001.jpg\n",
+            "lfw/Eddie_Jordan/\n",
+            "lfw/Eddie_Jordan/Eddie_Jordan_0001.jpg\n",
+            "lfw/Lionel_Richie/\n",
+            "lfw/Lionel_Richie/Lionel_Richie_0001.jpg\n",
+            "lfw/Lionel_Richie/Lionel_Richie_0002.jpg\n",
+            "lfw/Jose_Viegas_Filho/\n",
+            "lfw/Jose_Viegas_Filho/Jose_Viegas_Filho_0001.jpg\n",
+            "lfw/Jose_Viegas_Filho/Jose_Viegas_Filho_0002.jpg\n",
+            "lfw/Roger_Mahony/\n",
+            "lfw/Roger_Mahony/Roger_Mahony_0001.jpg\n",
+            "lfw/Clay_Aiken/\n",
+            "lfw/Clay_Aiken/Clay_Aiken_0001.jpg\n",
+            "lfw/Clay_Aiken/Clay_Aiken_0002.jpg\n",
+            "lfw/Clay_Aiken/Clay_Aiken_0003.jpg\n",
+            "lfw/Clay_Aiken/Clay_Aiken_0004.jpg\n",
+            "lfw/Clay_Aiken/Clay_Aiken_0005.jpg\n",
+            "lfw/Clay_Aiken/Clay_Aiken_0006.jpg\n",
+            "lfw/Edward_Egan/\n",
+            "lfw/Edward_Egan/Edward_Egan_0001.jpg\n",
+            "lfw/Pat_DAmuro/\n",
+            "lfw/Pat_DAmuro/Pat_DAmuro_0001.jpg\n",
+            "lfw/Demi_Moore/\n",
+            "lfw/Demi_Moore/Demi_Moore_0001.jpg\n",
+            "lfw/Demi_Moore/Demi_Moore_0002.jpg\n",
+            "lfw/Demi_Moore/Demi_Moore_0003.jpg\n",
+            "lfw/Demi_Moore/Demi_Moore_0004.jpg\n",
+            "lfw/Jamie_King/\n",
+            "lfw/Jamie_King/Jamie_King_0001.jpg\n",
+            "lfw/Joseph_Galante/\n",
+            "lfw/Joseph_Galante/Joseph_Galante_0001.jpg\n",
+            "lfw/Kenneth_Brill/\n",
+            "lfw/Kenneth_Brill/Kenneth_Brill_0001.jpg\n",
+            "lfw/Noel_Forgeard/\n",
+            "lfw/Noel_Forgeard/Noel_Forgeard_0001.jpg\n",
+            "lfw/Akbar_Al_Baker/\n",
+            "lfw/Akbar_Al_Baker/Akbar_Al_Baker_0001.jpg\n",
+            "lfw/Gregg_Berhalter/\n",
+            "lfw/Gregg_Berhalter/Gregg_Berhalter_0001.jpg\n",
+            "lfw/Tuncay_Sanli/\n",
+            "lfw/Tuncay_Sanli/Tuncay_Sanli_0001.jpg\n",
+            "lfw/Lou_Reed/\n",
+            "lfw/Lou_Reed/Lou_Reed_0001.jpg\n",
+            "lfw/Tabare_Vazquez/\n",
+            "lfw/Tabare_Vazquez/Tabare_Vazquez_0001.jpg\n",
+            "lfw/Mary_Tyler_Moore/\n",
+            "lfw/Mary_Tyler_Moore/Mary_Tyler_Moore_0001.jpg\n",
+            "lfw/Mary_Tyler_Moore/Mary_Tyler_Moore_0002.jpg\n",
+            "lfw/Akiko_Morigami/\n",
+            "lfw/Akiko_Morigami/Akiko_Morigami_0001.jpg\n",
+            "lfw/William_Nessen/\n",
+            "lfw/William_Nessen/William_Nessen_0001.jpg\n",
+            "lfw/Paul_Greengrass/\n",
+            "lfw/Paul_Greengrass/Paul_Greengrass_0001.jpg\n",
+            "lfw/Jerelle_Kraus/\n",
+            "lfw/Jerelle_Kraus/Jerelle_Kraus_0001.jpg\n",
+            "lfw/Surya_Bahadur_Thapa/\n",
+            "lfw/Surya_Bahadur_Thapa/Surya_Bahadur_Thapa_0001.jpg\n",
+            "lfw/King_Gyanendra/\n",
+            "lfw/King_Gyanendra/King_Gyanendra_0001.jpg\n",
+            "lfw/Nadia_Forte/\n",
+            "lfw/Nadia_Forte/Nadia_Forte_0001.jpg\n",
+            "lfw/Adrian_McPherson/\n",
+            "lfw/Adrian_McPherson/Adrian_McPherson_0001.jpg\n",
+            "lfw/Adrian_McPherson/Adrian_McPherson_0002.jpg\n",
+            "lfw/Ernesto_Zedillo/\n",
+            "lfw/Ernesto_Zedillo/Ernesto_Zedillo_0001.jpg\n",
+            "lfw/Ernesto_Zedillo/Ernesto_Zedillo_0002.jpg\n",
+            "lfw/Stephen_Crampton/\n",
+            "lfw/Stephen_Crampton/Stephen_Crampton_0001.jpg\n",
+            "lfw/Joe_Glover/\n",
+            "lfw/Joe_Glover/Joe_Glover_0001.jpg\n",
+            "lfw/Fernando_Valenzuela/\n",
+            "lfw/Fernando_Valenzuela/Fernando_Valenzuela_0001.jpg\n",
+            "lfw/Jim_Anderson/\n",
+            "lfw/Jim_Anderson/Jim_Anderson_0001.jpg\n",
+            "lfw/Alberta_Lee/\n",
+            "lfw/Alberta_Lee/Alberta_Lee_0001.jpg\n",
+            "lfw/Wen_Ho_Lee/\n",
+            "lfw/Wen_Ho_Lee/Wen_Ho_Lee_0001.jpg\n",
+            "lfw/Adam_Herbert/\n",
+            "lfw/Adam_Herbert/Adam_Herbert_0001.jpg\n",
+            "lfw/Cha_Yung-gu/\n",
+            "lfw/Cha_Yung-gu/Cha_Yung-gu_0001.jpg\n",
+            "lfw/Juan_Roman_Carrasco/\n",
+            "lfw/Juan_Roman_Carrasco/Juan_Roman_Carrasco_0001.jpg\n",
+            "lfw/Albaro_Recoba/\n",
+            "lfw/Albaro_Recoba/Albaro_Recoba_0001.jpg\n",
+            "lfw/Gabriel_Valdes/\n",
+            "lfw/Gabriel_Valdes/Gabriel_Valdes_0001.jpg\n",
+            "lfw/Gabriel_Valdes/Gabriel_Valdes_0002.jpg\n",
+            "lfw/Yang_Jianli/\n",
+            "lfw/Yang_Jianli/Yang_Jianli_0001.jpg\n",
+            "lfw/Pham_Sy_Chien/\n",
+            "lfw/Pham_Sy_Chien/Pham_Sy_Chien_0001.jpg\n",
+            "lfw/Henk_Bekedam/\n",
+            "lfw/Henk_Bekedam/Henk_Bekedam_0001.jpg\n",
+            "lfw/Jose_Woldenberg/\n",
+            "lfw/Jose_Woldenberg/Jose_Woldenberg_0001.jpg\n",
+            "lfw/Eric_Staal/\n",
+            "lfw/Eric_Staal/Eric_Staal_0001.jpg\n",
+            "lfw/Paul_Gascoigne/\n",
+            "lfw/Paul_Gascoigne/Paul_Gascoigne_0001.jpg\n",
+            "lfw/Paul_Gascoigne/Paul_Gascoigne_0002.jpg\n",
+            "lfw/Paul_Gascoigne/Paul_Gascoigne_0003.jpg\n",
+            "lfw/Mahima_Chaudhari/\n",
+            "lfw/Mahima_Chaudhari/Mahima_Chaudhari_0001.jpg\n",
+            "lfw/Carl_Reiner/\n",
+            "lfw/Carl_Reiner/Carl_Reiner_0001.jpg\n",
+            "lfw/Carl_Reiner/Carl_Reiner_0002.jpg\n",
+            "lfw/William_Pryor_Jr/\n",
+            "lfw/William_Pryor_Jr/William_Pryor_Jr_0001.jpg\n",
+            "lfw/Craig_MacTavish/\n",
+            "lfw/Craig_MacTavish/Craig_MacTavish_0001.jpg\n",
+            "lfw/Klaus_Schwab/\n",
+            "lfw/Klaus_Schwab/Klaus_Schwab_0001.jpg\n",
+            "lfw/Rick_Rickert/\n",
+            "lfw/Rick_Rickert/Rick_Rickert_0001.jpg\n",
+            "lfw/Thomas_Kelly/\n",
+            "lfw/Thomas_Kelly/Thomas_Kelly_0001.jpg\n",
+            "lfw/Lena_Olin/\n",
+            "lfw/Lena_Olin/Lena_Olin_0001.jpg\n",
+            "lfw/Carlos_Manuel_Pruneda/\n",
+            "lfw/Carlos_Manuel_Pruneda/Carlos_Manuel_Pruneda_0001.jpg\n",
+            "lfw/Carlos_Manuel_Pruneda/Carlos_Manuel_Pruneda_0002.jpg\n",
+            "lfw/Carlos_Manuel_Pruneda/Carlos_Manuel_Pruneda_0003.jpg\n",
+            "lfw/Maritza_Macias_Furano/\n",
+            "lfw/Maritza_Macias_Furano/Maritza_Macias_Furano_0001.jpg\n",
+            "lfw/Martha_Burk/\n",
+            "lfw/Martha_Burk/Martha_Burk_0001.jpg\n",
+            "lfw/Martha_Burk/Martha_Burk_0002.jpg\n",
+            "lfw/Martha_Burk/Martha_Burk_0003.jpg\n",
+            "lfw/Martha_Burk/Martha_Burk_0004.jpg\n",
+            "lfw/Zach_Parise/\n",
+            "lfw/Zach_Parise/Zach_Parise_0001.jpg\n",
+            "lfw/Shane_Hmiel/\n",
+            "lfw/Shane_Hmiel/Shane_Hmiel_0001.jpg\n",
+            "lfw/Mary_Lou_Markakis/\n",
+            "lfw/Mary_Lou_Markakis/Mary_Lou_Markakis_0001.jpg\n",
+            "lfw/Nick_Markakis/\n",
+            "lfw/Nick_Markakis/Nick_Markakis_0001.jpg\n",
+            "lfw/Fabian_Vargas/\n",
+            "lfw/Fabian_Vargas/Fabian_Vargas_0001.jpg\n",
+            "lfw/Razali_Ismail/\n",
+            "lfw/Razali_Ismail/Razali_Ismail_0001.jpg\n",
+            "lfw/Hugh_Hefner/\n",
+            "lfw/Hugh_Hefner/Hugh_Hefner_0001.jpg\n",
+            "lfw/Frederique_van_der_Wal/\n",
+            "lfw/Frederique_van_der_Wal/Frederique_van_der_Wal_0001.jpg\n",
+            "lfw/Amy_Pascal/\n",
+            "lfw/Amy_Pascal/Amy_Pascal_0001.jpg\n",
+            "lfw/Howard_Stringer/\n",
+            "lfw/Howard_Stringer/Howard_Stringer_0001.jpg\n",
+            "lfw/Mario_Lobo_Zagallo/\n",
+            "lfw/Mario_Lobo_Zagallo/Mario_Lobo_Zagallo_0001.jpg\n",
+            "lfw/Jason_Mewes/\n",
+            "lfw/Jason_Mewes/Jason_Mewes_0001.jpg\n",
+            "lfw/Giselle_Estefania_Tavarelli/\n",
+            "lfw/Giselle_Estefania_Tavarelli/Giselle_Estefania_Tavarelli_0001.jpg\n",
+            "lfw/Park_Na-kyong/\n",
+            "lfw/Park_Na-kyong/Park_Na-kyong_0001.jpg\n",
+            "lfw/Manuel_Llorente/\n",
+            "lfw/Manuel_Llorente/Manuel_Llorente_0001.jpg\n",
+            "lfw/Jaime_Orti/\n",
+            "lfw/Jaime_Orti/Jaime_Orti_0001.jpg\n",
+            "lfw/Jaime_Pressly/\n",
+            "lfw/Jaime_Pressly/Jaime_Pressly_0001.jpg\n",
+            "lfw/Saadi_Gadhafi/\n",
+            "lfw/Saadi_Gadhafi/Saadi_Gadhafi_0001.jpg\n",
+            "lfw/Zhong_Nanshan/\n",
+            "lfw/Zhong_Nanshan/Zhong_Nanshan_0001.jpg\n",
+            "lfw/Jacques_Kallis/\n",
+            "lfw/Jacques_Kallis/Jacques_Kallis_0001.jpg\n",
+            "lfw/Jennifer_Love_Hewitt/\n",
+            "lfw/Jennifer_Love_Hewitt/Jennifer_Love_Hewitt_0001.jpg\n",
+            "lfw/Chante_Jawan_Mallard/\n",
+            "lfw/Chante_Jawan_Mallard/Chante_Jawan_Mallard_0001.jpg\n",
+            "lfw/Paul_McNulty/\n",
+            "lfw/Paul_McNulty/Paul_McNulty_0001.jpg\n",
+            "lfw/Paul_McNulty/Paul_McNulty_0002.jpg\n",
+            "lfw/Ralph_Firman/\n",
+            "lfw/Ralph_Firman/Ralph_Firman_0001.jpg\n",
+            "lfw/Ralph_Firman/Ralph_Firman_0002.jpg\n",
+            "lfw/Alice_Fisher/\n",
+            "lfw/Alice_Fisher/Alice_Fisher_0001.jpg\n",
+            "lfw/Alice_Fisher/Alice_Fisher_0002.jpg\n",
+            "lfw/Josh_Evans/\n",
+            "lfw/Josh_Evans/Josh_Evans_0001.jpg\n",
+            "lfw/Keith_Fotta/\n",
+            "lfw/Keith_Fotta/Keith_Fotta_0001.jpg\n",
+            "lfw/Steven_Seagal/\n",
+            "lfw/Steven_Seagal/Steven_Seagal_0001.jpg\n",
+            "lfw/Steven_Seagal/Steven_Seagal_0002.jpg\n",
+            "lfw/Michael_Rolinee/\n",
+            "lfw/Michael_Rolinee/Michael_Rolinee_0001.jpg\n",
+            "lfw/Courtney_Love/\n",
+            "lfw/Courtney_Love/Courtney_Love_0001.jpg\n",
+            "lfw/Courtney_Love/Courtney_Love_0002.jpg\n",
+            "lfw/Jim_Hendry/\n",
+            "lfw/Jim_Hendry/Jim_Hendry_0001.jpg\n",
+            "lfw/Corinna_Harfouch/\n",
+            "lfw/Corinna_Harfouch/Corinna_Harfouch_0001.jpg\n",
+            "lfw/Adrien_Brody/\n",
+            "lfw/Adrien_Brody/Adrien_Brody_0001.jpg\n",
+            "lfw/Adrien_Brody/Adrien_Brody_0002.jpg\n",
+            "lfw/Adrien_Brody/Adrien_Brody_0003.jpg\n",
+            "lfw/Adrien_Brody/Adrien_Brody_0004.jpg\n",
+            "lfw/Adrien_Brody/Adrien_Brody_0005.jpg\n",
+            "lfw/Adrien_Brody/Adrien_Brody_0006.jpg\n",
+            "lfw/Adrien_Brody/Adrien_Brody_0007.jpg\n",
+            "lfw/Adrien_Brody/Adrien_Brody_0008.jpg\n",
+            "lfw/Adrien_Brody/Adrien_Brody_0009.jpg\n",
+            "lfw/Adrien_Brody/Adrien_Brody_0010.jpg\n",
+            "lfw/Adrien_Brody/Adrien_Brody_0011.jpg\n",
+            "lfw/Adrien_Brody/Adrien_Brody_0012.jpg\n",
+            "lfw/Kevin_Gil/\n",
+            "lfw/Kevin_Gil/Kevin_Gil_0001.jpg\n",
+            "lfw/Tom_Coughlin/\n",
+            "lfw/Tom_Coughlin/Tom_Coughlin_0001.jpg\n",
+            "lfw/Janet_Thorpe/\n",
+            "lfw/Janet_Thorpe/Janet_Thorpe_0001.jpg\n",
+            "lfw/Janet_Thorpe/Janet_Thorpe_0002.jpg\n",
+            "lfw/Daniel_Bruehl/\n",
+            "lfw/Daniel_Bruehl/Daniel_Bruehl_0001.jpg\n",
+            "lfw/Wolfgang_Becker/\n",
+            "lfw/Wolfgang_Becker/Wolfgang_Becker_0001.jpg\n",
+            "lfw/Sandra_Bullock/\n",
+            "lfw/Sandra_Bullock/Sandra_Bullock_0001.jpg\n",
+            "lfw/Sandra_Bullock/Sandra_Bullock_0002.jpg\n",
+            "lfw/Sandra_Bullock/Sandra_Bullock_0003.jpg\n",
+            "lfw/Sandra_Bullock/Sandra_Bullock_0004.jpg\n",
+            "lfw/Kareena_Kapoor/\n",
+            "lfw/Kareena_Kapoor/Kareena_Kapoor_0001.jpg\n",
+            "lfw/Ruben_Sierra/\n",
+            "lfw/Ruben_Sierra/Ruben_Sierra_0001.jpg\n",
+            "lfw/Bill_Hughes/\n",
+            "lfw/Bill_Hughes/Bill_Hughes_0001.jpg\n",
+            "lfw/Jose_Alencar/\n",
+            "lfw/Jose_Alencar/Jose_Alencar_0001.jpg\n",
+            "lfw/Rick_Bragg/\n",
+            "lfw/Rick_Bragg/Rick_Bragg_0001.jpg\n",
+            "lfw/Farouk_al-Sharaa/\n",
+            "lfw/Farouk_al-Sharaa/Farouk_al-Sharaa_0001.jpg\n",
+            "lfw/Farouk_al-Sharaa/Farouk_al-Sharaa_0002.jpg\n",
+            "lfw/Farouk_al-Sharaa/Farouk_al-Sharaa_0003.jpg\n",
+            "lfw/Fernando_Hierro/\n",
+            "lfw/Fernando_Hierro/Fernando_Hierro_0001.jpg\n",
+            "lfw/Aleksander_Kwasniewski/\n",
+            "lfw/Aleksander_Kwasniewski/Aleksander_Kwasniewski_0001.jpg\n",
+            "lfw/Aleksander_Kwasniewski/Aleksander_Kwasniewski_0002.jpg\n",
+            "lfw/Aleksander_Kwasniewski/Aleksander_Kwasniewski_0003.jpg\n",
+            "lfw/Aleksander_Kwasniewski/Aleksander_Kwasniewski_0004.jpg\n",
+            "lfw/George_Gregan/\n",
+            "lfw/George_Gregan/George_Gregan_0001.jpg\n",
+            "lfw/Mike_Bryan/\n",
+            "lfw/Mike_Bryan/Mike_Bryan_0001.jpg\n",
+            "lfw/Beyonce_Knowles/\n",
+            "lfw/Beyonce_Knowles/Beyonce_Knowles_0001.jpg\n",
+            "lfw/Humberto_Coelho/\n",
+            "lfw/Humberto_Coelho/Humberto_Coelho_0001.jpg\n",
+            "lfw/Jon_Constance/\n",
+            "lfw/Jon_Constance/Jon_Constance_0001.jpg\n",
+            "lfw/Toutai_Kefu/\n",
+            "lfw/Toutai_Kefu/Toutai_Kefu_0001.jpg\n",
+            "lfw/Fujio_Mitarai/\n",
+            "lfw/Fujio_Mitarai/Fujio_Mitarai_0001.jpg\n",
+            "lfw/Barbara_Boxer/\n",
+            "lfw/Barbara_Boxer/Barbara_Boxer_0001.jpg\n",
+            "lfw/Tom_Kelly/\n",
+            "lfw/Tom_Kelly/Tom_Kelly_0001.jpg\n",
+            "lfw/Ian_Gillan/\n",
+            "lfw/Ian_Gillan/Ian_Gillan_0001.jpg\n",
+            "lfw/Mike_Alden/\n",
+            "lfw/Mike_Alden/Mike_Alden_0001.jpg\n",
+            "lfw/Herb_Dhaliwal/\n",
+            "lfw/Herb_Dhaliwal/Herb_Dhaliwal_0001.jpg\n",
+            "lfw/Michel_Minard/\n",
+            "lfw/Michel_Minard/Michel_Minard_0001.jpg\n",
+            "lfw/Mohammed_Dahlan/\n",
+            "lfw/Mohammed_Dahlan/Mohammed_Dahlan_0001.jpg\n",
+            "lfw/Matthew_During/\n",
+            "lfw/Matthew_During/Matthew_During_0001.jpg\n",
+            "lfw/Anibal_Ibarra/\n",
+            "lfw/Anibal_Ibarra/Anibal_Ibarra_0001.jpg\n",
+            "lfw/Anibal_Ibarra/Anibal_Ibarra_0002.jpg\n",
+            "lfw/Anibal_Ibarra/Anibal_Ibarra_0003.jpg\n",
+            "lfw/Phil_Morris/\n",
+            "lfw/Phil_Morris/Phil_Morris_0001.jpg\n",
+            "lfw/Ramona_Rispton/\n",
+            "lfw/Ramona_Rispton/Ramona_Rispton_0001.jpg\n",
+            "lfw/Brandon_Jones/\n",
+            "lfw/Brandon_Jones/Brandon_Jones_0001.jpg\n",
+            "lfw/Peter_Ueberroth/\n",
+            "lfw/Peter_Ueberroth/Peter_Ueberroth_0001.jpg\n",
+            "lfw/Charles_Cope/\n",
+            "lfw/Charles_Cope/Charles_Cope_0001.jpg\n",
+            "lfw/Roy_Moore/\n",
+            "lfw/Roy_Moore/Roy_Moore_0001.jpg\n",
+            "lfw/Roy_Moore/Roy_Moore_0002.jpg\n",
+            "lfw/Roy_Moore/Roy_Moore_0003.jpg\n",
+            "lfw/Roy_Moore/Roy_Moore_0004.jpg\n",
+            "lfw/Roy_Moore/Roy_Moore_0005.jpg\n",
+            "lfw/Roy_Moore/Roy_Moore_0006.jpg\n",
+            "lfw/Alex_Holmes/\n",
+            "lfw/Alex_Holmes/Alex_Holmes_0001.jpg\n",
+            "lfw/Juan_Roman_Riquelme/\n",
+            "lfw/Juan_Roman_Riquelme/Juan_Roman_Riquelme_0001.jpg\n",
+            "lfw/Giuseppe_Gibilisco/\n",
+            "lfw/Giuseppe_Gibilisco/Giuseppe_Gibilisco_0001.jpg\n",
+            "lfw/Giuseppe_Gibilisco/Giuseppe_Gibilisco_0002.jpg\n",
+            "lfw/Giuseppe_Gibilisco/Giuseppe_Gibilisco_0003.jpg\n",
+            "lfw/Giuseppe_Gibilisco/Giuseppe_Gibilisco_0004.jpg\n",
+            "lfw/Platon_Lebedev/\n",
+            "lfw/Platon_Lebedev/Platon_Lebedev_0001.jpg\n",
+            "lfw/Pharrell_Williams/\n",
+            "lfw/Pharrell_Williams/Pharrell_Williams_0001.jpg\n",
+            "lfw/Vadim_Strogalev/\n",
+            "lfw/Vadim_Strogalev/Vadim_Strogalev_0001.jpg\n",
+            "lfw/Claudia_Coslovich/\n",
+            "lfw/Claudia_Coslovich/Claudia_Coslovich_0001.jpg\n",
+            "lfw/Jessica_Alba/\n",
+            "lfw/Jessica_Alba/Jessica_Alba_0001.jpg\n",
+            "lfw/Jessica_Alba/Jessica_Alba_0002.jpg\n",
+            "lfw/Hana_Makhmalbaf/\n",
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+            "lfw/Rosemarie_Stack/Rosemarie_Stack_0001.jpg\n",
+            "lfw/Rosemarie_Stack/Rosemarie_Stack_0002.jpg\n",
+            "lfw/Joe_Dumars/\n",
+            "lfw/Joe_Dumars/Joe_Dumars_0001.jpg\n",
+            "lfw/Joe_Dumars/Joe_Dumars_0002.jpg\n",
+            "lfw/Marion_Fahnestock/\n",
+            "lfw/Marion_Fahnestock/Marion_Fahnestock_0001.jpg\n",
+            "lfw/Jean-Luc_Bideau/\n",
+            "lfw/Jean-Luc_Bideau/Jean-Luc_Bideau_0001.jpg\n",
+            "lfw/Kim_Cattrall/\n",
+            "lfw/Kim_Cattrall/Kim_Cattrall_0001.jpg\n",
+            "lfw/Angela_Mascia-Frye/\n",
+            "lfw/Angela_Mascia-Frye/Angela_Mascia-Frye_0001.jpg\n",
+            "lfw/Michael_Hagee/\n",
+            "lfw/Michael_Hagee/Michael_Hagee_0001.jpg\n",
+            "lfw/Paul_Desmarais/\n",
+            "lfw/Paul_Desmarais/Paul_Desmarais_0001.jpg\n",
+            "lfw/John_Mabry/\n",
+            "lfw/John_Mabry/John_Mabry_0001.jpg\n",
+            "lfw/Taku_Yamasaki/\n",
+            "lfw/Taku_Yamasaki/Taku_Yamasaki_0001.jpg\n",
+            "lfw/Nicola_Bono/\n",
+            "lfw/Nicola_Bono/Nicola_Bono_0001.jpg\n",
+            "lfw/Chen_Kaige/\n",
+            "lfw/Chen_Kaige/Chen_Kaige_0001.jpg\n",
+            "lfw/Chuanyun_Li/\n",
+            "lfw/Chuanyun_Li/Chuanyun_Li_0001.jpg\n",
+            "lfw/Seth_Gorney/\n",
+            "lfw/Seth_Gorney/Seth_Gorney_0001.jpg\n",
+            "lfw/Paul_Reiser/\n",
+            "lfw/Paul_Reiser/Paul_Reiser_0001.jpg\n",
+            "lfw/Ken_Kutaragi/\n",
+            "lfw/Ken_Kutaragi/Ken_Kutaragi_0001.jpg\n",
+            "lfw/Imam_Samudra/\n",
+            "lfw/Imam_Samudra/Imam_Samudra_0001.jpg\n",
+            "lfw/Stephanie_Cohen_Aloro/\n",
+            "lfw/Stephanie_Cohen_Aloro/Stephanie_Cohen_Aloro_0001.jpg\n",
+            "lfw/Hideki_Sato/\n",
+            "lfw/Hideki_Sato/Hideki_Sato_0001.jpg\n",
+            "lfw/JT_Snow/\n",
+            "lfw/JT_Snow/JT_Snow_0001.jpg\n",
+            "lfw/Ray_Sherman/\n",
+            "lfw/Ray_Sherman/Ray_Sherman_0001.jpg\n",
+            "lfw/Arye_Mekel/\n",
+            "lfw/Arye_Mekel/Arye_Mekel_0001.jpg\n",
+            "lfw/Arye_Mekel/Arye_Mekel_0002.jpg\n",
+            "lfw/Florecita_Cobian/\n",
+            "lfw/Florecita_Cobian/Florecita_Cobian_0001.jpg\n",
+            "lfw/Lesley_Flood/\n",
+            "lfw/Lesley_Flood/Lesley_Flood_0001.jpg\n",
+            "lfw/Hector_Babenco/\n",
+            "lfw/Hector_Babenco/Hector_Babenco_0001.jpg\n",
+            "lfw/Hector_Babenco/Hector_Babenco_0002.jpg\n",
+            "lfw/Hector_Babenco/Hector_Babenco_0003.jpg\n",
+            "lfw/Maria_Luisa_Mendonca/\n",
+            "lfw/Maria_Luisa_Mendonca/Maria_Luisa_Mendonca_0001.jpg\n",
+            "lfw/Maria_Luisa_Mendonca/Maria_Luisa_Mendonca_0002.jpg\n",
+            "lfw/Caio_Blat/\n",
+            "lfw/Caio_Blat/Caio_Blat_0001.jpg\n",
+            "lfw/Jodie_Kidd/\n",
+            "lfw/Jodie_Kidd/Jodie_Kidd_0001.jpg\n",
+            "lfw/Andy_Rooney/\n",
+            "lfw/Andy_Rooney/Andy_Rooney_0001.jpg\n",
+            "lfw/Pascal_Rheaume/\n",
+            "lfw/Pascal_Rheaume/Pascal_Rheaume_0001.jpg\n",
+            "lfw/Amber_Frey/\n",
+            "lfw/Amber_Frey/Amber_Frey_0001.jpg\n",
+            "lfw/Stellan_Skarsgard/\n",
+            "lfw/Stellan_Skarsgard/Stellan_Skarsgard_0001.jpg\n",
+            "lfw/Stellan_Skarsgard/Stellan_Skarsgard_0002.jpg\n",
+            "lfw/Faye_Alibocus/\n",
+            "lfw/Faye_Alibocus/Faye_Alibocus_0001.jpg\n",
+            "lfw/Joshua_Davey/\n",
+            "lfw/Joshua_Davey/Joshua_Davey_0001.jpg\n",
+            "lfw/Barbara_Bach/\n",
+            "lfw/Barbara_Bach/Barbara_Bach_0001.jpg\n",
+            "lfw/Ringo_Starr/\n",
+            "lfw/Ringo_Starr/Ringo_Starr_0001.jpg\n",
+            "lfw/Bernice_Wong/\n",
+            "lfw/Bernice_Wong/Bernice_Wong_0001.jpg\n",
+            "lfw/Roger_Lyons/\n",
+            "lfw/Roger_Lyons/Roger_Lyons_0001.jpg\n",
+            "lfw/Na_Na_Keum/\n",
+            "lfw/Na_Na_Keum/Na_Na_Keum_0001.jpg\n",
+            "lfw/Alexandra_Vodjanikova/\n",
+            "lfw/Alexandra_Vodjanikova/Alexandra_Vodjanikova_0001.jpg\n",
+            "lfw/Alexandra_Vodjanikova/Alexandra_Vodjanikova_0002.jpg\n",
+            "lfw/Yannos_Papantoniou/\n",
+            "lfw/Yannos_Papantoniou/Yannos_Papantoniou_0001.jpg\n",
+            "lfw/Sanja_Papic/\n",
+            "lfw/Sanja_Papic/Sanja_Papic_0001.jpg\n",
+            "lfw/Paul_Walker/\n",
+            "lfw/Paul_Walker/Paul_Walker_0001.jpg\n",
+            "lfw/Jason_Sehorn/\n",
+            "lfw/Jason_Sehorn/Jason_Sehorn_0001.jpg\n",
+            "lfw/Darko_Milicic/\n",
+            "lfw/Darko_Milicic/Darko_Milicic_0001.jpg\n",
+            "lfw/Katerina_Smrzova/\n",
+            "lfw/Katerina_Smrzova/Katerina_Smrzova_0001.jpg\n",
+            "lfw/David_Carradine/\n",
+            "lfw/David_Carradine/David_Carradine_0001.jpg\n",
+            "lfw/Coco_dEste/\n",
+            "lfw/Coco_dEste/Coco_dEste_0001.jpg\n",
+            "lfw/Rene_Antonio_Leon_Rodriguez/\n",
+            "lfw/Rene_Antonio_Leon_Rodriguez/Rene_Antonio_Leon_Rodriguez_0001.jpg\n",
+            "lfw/Carlos_Quintanilla_Schmidt/\n",
+            "lfw/Carlos_Quintanilla_Schmidt/Carlos_Quintanilla_Schmidt_0001.jpg\n",
+            "lfw/Carlos_Quintanilla_Schmidt/Carlos_Quintanilla_Schmidt_0002.jpg\n",
+            "lfw/Bill_Parsons/\n",
+            "lfw/Bill_Parsons/Bill_Parsons_0001.jpg\n",
+            "lfw/Danny_Ainge/\n",
+            "lfw/Danny_Ainge/Danny_Ainge_0001.jpg\n",
+            "lfw/Brad_Alexander_Smith/\n",
+            "lfw/Brad_Alexander_Smith/Brad_Alexander_Smith_0001.jpg\n",
+            "lfw/Alex_Zanardi/\n",
+            "lfw/Alex_Zanardi/Alex_Zanardi_0001.jpg\n",
+            "lfw/Steve_Avery/\n",
+            "lfw/Steve_Avery/Steve_Avery_0001.jpg\n",
+            "lfw/Matt_Anderson/\n",
+            "lfw/Matt_Anderson/Matt_Anderson_0001.jpg\n",
+            "lfw/Richard_Jefferson/\n",
+            "lfw/Richard_Jefferson/Richard_Jefferson_0001.jpg\n",
+            "lfw/Leon_Barmore/\n",
+            "lfw/Leon_Barmore/Leon_Barmore_0001.jpg\n",
+            "lfw/Jose_Luis_Santiago_Vasconcelos/\n",
+            "lfw/Jose_Luis_Santiago_Vasconcelos/Jose_Luis_Santiago_Vasconcelos_0001.jpg\n",
+            "lfw/Mary_Matalin/\n",
+            "lfw/Mary_Matalin/Mary_Matalin_0001.jpg\n",
+            "lfw/David_Ho/\n",
+            "lfw/David_Ho/David_Ho_0001.jpg\n",
+            "lfw/Thomas_Ulrich/\n",
+            "lfw/Thomas_Ulrich/Thomas_Ulrich_0001.jpg\n",
+            "lfw/Graciano_Rocchigiani/\n",
+            "lfw/Graciano_Rocchigiani/Graciano_Rocchigiani_0001.jpg\n",
+            "lfw/John_Moe/\n",
+            "lfw/John_Moe/John_Moe_0001.jpg\n",
+            "lfw/Tora_Takagi/\n",
+            "lfw/Tora_Takagi/Tora_Takagi_0001.jpg\n",
+            "lfw/Chris_Neil/\n",
+            "lfw/Chris_Neil/Chris_Neil_0001.jpg\n",
+            "lfw/Barbara_Bodine/\n",
+            "lfw/Barbara_Bodine/Barbara_Bodine_0001.jpg\n",
+            "lfw/Rolf_Eckrodt/\n",
+            "lfw/Rolf_Eckrodt/Rolf_Eckrodt_0001.jpg\n",
+            "lfw/Rolf_Eckrodt/Rolf_Eckrodt_0002.jpg\n",
+            "lfw/Carol_Niedermayer/\n",
+            "lfw/Carol_Niedermayer/Carol_Niedermayer_0001.jpg\n",
+            "lfw/Martha_Sahagun_de_Fox/\n",
+            "lfw/Martha_Sahagun_de_Fox/Martha_Sahagun_de_Fox_0001.jpg\n",
+            "lfw/Armando_Calderon_Sol/\n",
+            "lfw/Armando_Calderon_Sol/Armando_Calderon_Sol_0001.jpg\n",
+            "lfw/Terunobu_Maeda/\n",
+            "lfw/Terunobu_Maeda/Terunobu_Maeda_0001.jpg\n",
+            "lfw/Alex_Corretja/\n",
+            "lfw/Alex_Corretja/Alex_Corretja_0001.jpg\n",
+            "lfw/Elena_Tihomirova/\n",
+            "lfw/Elena_Tihomirova/Elena_Tihomirova_0001.jpg\n",
+            "lfw/Emmy_Rossum/\n",
+            "lfw/Emmy_Rossum/Emmy_Rossum_0001.jpg\n",
+            "lfw/Andrea_Yates/\n",
+            "lfw/Andrea_Yates/Andrea_Yates_0001.jpg\n",
+            "lfw/Laura_Romero/\n",
+            "lfw/Laura_Romero/Laura_Romero_0001.jpg\n",
+            "lfw/Szu_Yu_Chen/\n",
+            "lfw/Szu_Yu_Chen/Szu_Yu_Chen_0001.jpg\n",
+            "lfw/Rudy_Tomjanovich/\n",
+            "lfw/Rudy_Tomjanovich/Rudy_Tomjanovich_0001.jpg\n",
+            "lfw/Norman_Mineta/\n",
+            "lfw/Norman_Mineta/Norman_Mineta_0001.jpg\n",
+            "lfw/Michael_Killeen/\n",
+            "lfw/Michael_Killeen/Michael_Killeen_0001.jpg\n",
+            "lfw/Kristen_Rivera/\n",
+            "lfw/Kristen_Rivera/Kristen_Rivera_0001.jpg\n",
+            "lfw/Alicia_Hollowell/\n",
+            "lfw/Alicia_Hollowell/Alicia_Hollowell_0001.jpg\n",
+            "lfw/Peter_Hollingworth/\n",
+            "lfw/Peter_Hollingworth/Peter_Hollingworth_0001.jpg\n",
+            "lfw/Ashlea_Talbot/\n",
+            "lfw/Ashlea_Talbot/Ashlea_Talbot_0001.jpg\n",
+            "lfw/Christine_Rau/\n",
+            "lfw/Christine_Rau/Christine_Rau_0001.jpg\n",
+            "lfw/Wei_Wu/\n",
+            "lfw/Wei_Wu/Wei_Wu_0001.jpg\n",
+            "lfw/Eugene_Melnyk/\n",
+            "lfw/Eugene_Melnyk/Eugene_Melnyk_0001.jpg\n",
+            "lfw/Jim_Paxson/\n",
+            "lfw/Jim_Paxson/Jim_Paxson_0001.jpg\n",
+            "lfw/Annie_Chaplin/\n",
+            "lfw/Annie_Chaplin/Annie_Chaplin_0001.jpg\n",
+            "lfw/Jeanne_Moreau/\n",
+            "lfw/Jeanne_Moreau/Jeanne_Moreau_0001.jpg\n",
+            "lfw/Jeanne_Moreau/Jeanne_Moreau_0002.jpg\n",
+            "lfw/Ornella_Muti/\n",
+            "lfw/Ornella_Muti/Ornella_Muti_0001.jpg\n",
+            "lfw/Yuri_Luzhkov/\n",
+            "lfw/Yuri_Luzhkov/Yuri_Luzhkov_0001.jpg\n",
+            "lfw/Mark_Butcher/\n",
+            "lfw/Mark_Butcher/Mark_Butcher_0001.jpg\n",
+            "lfw/Michelle_Lecky/\n",
+            "lfw/Michelle_Lecky/Michelle_Lecky_0001.jpg\n",
+            "lfw/Tina_Pisnik/\n",
+            "lfw/Tina_Pisnik/Tina_Pisnik_0001.jpg\n",
+            "lfw/Jeffrey_Katzenberg/\n",
+            "lfw/Jeffrey_Katzenberg/Jeffrey_Katzenberg_0001.jpg\n",
+            "lfw/Andrew_Bunner/\n",
+            "lfw/Andrew_Bunner/Andrew_Bunner_0001.jpg\n",
+            "lfw/Andrew_Bunner/Andrew_Bunner_0002.jpg\n",
+            "lfw/David_Welch/\n",
+            "lfw/David_Welch/David_Welch_0001.jpg\n",
+            "lfw/Jane_Rooney/\n",
+            "lfw/Jane_Rooney/Jane_Rooney_0001.jpg\n",
+            "lfw/Mickey_Rooney/\n",
+            "lfw/Mickey_Rooney/Mickey_Rooney_0001.jpg\n",
+            "lfw/Peter_Hunt/\n",
+            "lfw/Peter_Hunt/Peter_Hunt_0001.jpg\n",
+            "lfw/Francis_Crick/\n",
+            "lfw/Francis_Crick/Francis_Crick_0001.jpg\n",
+            "lfw/Kay_Behrensmeyer/\n",
+            "lfw/Kay_Behrensmeyer/Kay_Behrensmeyer_0001.jpg\n",
+            "lfw/Gala_Leon_Garcia/\n",
+            "lfw/Gala_Leon_Garcia/Gala_Leon_Garcia_0001.jpg\n",
+            "lfw/Wanda_de_la_Jesus/\n",
+            "lfw/Wanda_de_la_Jesus/Wanda_de_la_Jesus_0001.jpg\n",
+            "lfw/Jimmy_Smits/\n",
+            "lfw/Jimmy_Smits/Jimmy_Smits_0001.jpg\n",
+            "lfw/Jane_Leeves/\n",
+            "lfw/Jane_Leeves/Jane_Leeves_0001.jpg\n",
+            "lfw/Kelsey_Grammer/\n",
+            "lfw/Kelsey_Grammer/Kelsey_Grammer_0001.jpg\n",
+            "lfw/Donna_Ralston/\n",
+            "lfw/Donna_Ralston/Donna_Ralston_0001.jpg\n",
+            "lfw/Larry_Ralston/\n",
+            "lfw/Larry_Ralston/Larry_Ralston_0001.jpg\n",
+            "lfw/Barry_Diller/\n",
+            "lfw/Barry_Diller/Barry_Diller_0001.jpg\n",
+            "lfw/Dominique_Perben/\n",
+            "lfw/Dominique_Perben/Dominique_Perben_0001.jpg\n",
+            "lfw/Peri_Gilpin/\n",
+            "lfw/Peri_Gilpin/Peri_Gilpin_0001.jpg\n",
+            "lfw/David_Bowie/\n",
+            "lfw/David_Bowie/David_Bowie_0001.jpg\n",
+            "lfw/Elizabeth_Regan/\n",
+            "lfw/Elizabeth_Regan/Elizabeth_Regan_0001.jpg\n",
+            "lfw/Whoopi_Goldberg/\n",
+            "lfw/Whoopi_Goldberg/Whoopi_Goldberg_0001.jpg\n",
+            "lfw/Dany_Heatley/\n",
+            "lfw/Dany_Heatley/Dany_Heatley_0001.jpg\n",
+            "lfw/Chris_Kolanas/\n",
+            "lfw/Chris_Kolanas/Chris_Kolanas_0001.jpg\n",
+            "lfw/Chuck_Hagel/\n",
+            "lfw/Chuck_Hagel/Chuck_Hagel_0001.jpg\n",
+            "lfw/Jamir_Miller/\n",
+            "lfw/Jamir_Miller/Jamir_Miller_0001.jpg\n",
+            "lfw/Tatiana_Kennedy_Schlossberg/\n",
+            "lfw/Tatiana_Kennedy_Schlossberg/Tatiana_Kennedy_Schlossberg_0001.jpg\n",
+            "lfw/James_Smith/\n",
+            "lfw/James_Smith/James_Smith_0001.jpg\n",
+            "lfw/James_Smith/James_Smith_0002.jpg\n",
+            "lfw/Doug_Christie/\n",
+            "lfw/Doug_Christie/Doug_Christie_0001.jpg\n",
+            "lfw/Desiree_McKenzie/\n",
+            "lfw/Desiree_McKenzie/Desiree_McKenzie_0001.jpg\n",
+            "lfw/Dan_Prinster/\n",
+            "lfw/Dan_Prinster/Dan_Prinster_0001.jpg\n",
+            "lfw/Henning_Scherf/\n",
+            "lfw/Henning_Scherf/Henning_Scherf_0001.jpg\n",
+            "lfw/Kang_Gum-sil/\n",
+            "lfw/Kang_Gum-sil/Kang_Gum-sil_0001.jpg\n",
+            "lfw/Denys_Arcand/\n",
+            "lfw/Denys_Arcand/Denys_Arcand_0001.jpg\n",
+            "lfw/Dwayne_Williams/\n",
+            "lfw/Dwayne_Williams/Dwayne_Williams_0001.jpg\n",
+            "lfw/Se_Hyuk_Joo/\n",
+            "lfw/Se_Hyuk_Joo/Se_Hyuk_Joo_0001.jpg\n",
+            "lfw/Daniel_Scioli/\n",
+            "lfw/Daniel_Scioli/Daniel_Scioli_0001.jpg\n",
+            "lfw/Marie-Josee_Croze/\n",
+            "lfw/Marie-Josee_Croze/Marie-Josee_Croze_0001.jpg\n",
+            "lfw/Rachel_Kempson/\n",
+            "lfw/Rachel_Kempson/Rachel_Kempson_0001.jpg\n",
+            "lfw/Tony_Elias/\n",
+            "lfw/Tony_Elias/Tony_Elias_0001.jpg\n",
+            "lfw/Wang_Nan/\n",
+            "lfw/Wang_Nan/Wang_Nan_0001.jpg\n",
+            "lfw/Gus_Van_Sant/\n",
+            "lfw/Gus_Van_Sant/Gus_Van_Sant_0001.jpg\n",
+            "lfw/Gus_Van_Sant/Gus_Van_Sant_0002.jpg\n",
+            "lfw/Gus_Van_Sant/Gus_Van_Sant_0003.jpg\n",
+            "lfw/Werner_Schlager/\n",
+            "lfw/Werner_Schlager/Werner_Schlager_0001.jpg\n",
+            "lfw/Nan_Wang/\n",
+            "lfw/Nan_Wang/Nan_Wang_0001.jpg\n",
+            "lfw/Nan_Wang/Nan_Wang_0002.jpg\n",
+            "lfw/Nan_Wang/Nan_Wang_0003.jpg\n",
+            "lfw/Nan_Wang/Nan_Wang_0004.jpg\n",
+            "lfw/Helio_Castroneves/\n",
+            "lfw/Helio_Castroneves/Helio_Castroneves_0001.jpg\n",
+            "lfw/Peter_Greenaway/\n",
+            "lfw/Peter_Greenaway/Peter_Greenaway_0001.jpg\n",
+            "lfw/Peter_Greenaway/Peter_Greenaway_0002.jpg\n",
+            "lfw/Eva_Amurri/\n",
+            "lfw/Eva_Amurri/Eva_Amurri_0001.jpg\n",
+            "lfw/Hugh_Carey/\n",
+            "lfw/Hugh_Carey/Hugh_Carey_0001.jpg\n",
+            "lfw/Hal_Sellers/\n",
+            "lfw/Hal_Sellers/Hal_Sellers_0001.jpg\n",
+            "lfw/Valentina_Cervi/\n",
+            "lfw/Valentina_Cervi/Valentina_Cervi_0001.jpg\n",
+            "lfw/Diana_Taylor/\n",
+            "lfw/Diana_Taylor/Diana_Taylor_0001.jpg\n",
+            "lfw/Hermogenes_Ebdane_Jr/\n",
+            "lfw/Hermogenes_Ebdane_Jr/Hermogenes_Ebdane_Jr_0001.jpg\n",
+            "lfw/Steven_Tyler/\n",
+            "lfw/Steven_Tyler/Steven_Tyler_0001.jpg\n",
+            "lfw/Patricia_Medina/\n",
+            "lfw/Patricia_Medina/Patricia_Medina_0001.jpg\n",
+            "lfw/Jean-Marc_Olive/\n",
+            "lfw/Jean-Marc_Olive/Jean-Marc_Olive_0001.jpg\n",
+            "lfw/Antonio_Cassano/\n",
+            "lfw/Antonio_Cassano/Antonio_Cassano_0001.jpg\n",
+            "lfw/Jose_Santos/\n",
+            "lfw/Jose_Santos/Jose_Santos_0001.jpg\n",
+            "lfw/Dianne_Reeves/\n",
+            "lfw/Dianne_Reeves/Dianne_Reeves_0001.jpg\n",
+            "lfw/Prince_Felipe/\n",
+            "lfw/Prince_Felipe/Prince_Felipe_0001.jpg\n",
+            "lfw/Infanta_Cristina/\n",
+            "lfw/Infanta_Cristina/Infanta_Cristina_0001.jpg\n",
+            "lfw/Cuba_Gooding_Jr/\n",
+            "lfw/Cuba_Gooding_Jr/Cuba_Gooding_Jr_0001.jpg\n",
+            "lfw/Glenn_Rivers/\n",
+            "lfw/Glenn_Rivers/Glenn_Rivers_0001.jpg\n",
+            "lfw/Dino_de_Laurentis/\n",
+            "lfw/Dino_de_Laurentis/Dino_de_Laurentis_0001.jpg\n",
+            "lfw/Dino_de_Laurentis/Dino_de_Laurentis_0002.jpg\n",
+            "lfw/Francis_Ricciardone/\n",
+            "lfw/Francis_Ricciardone/Francis_Ricciardone_0001.jpg\n",
+            "lfw/Leticia_Dolera/\n",
+            "lfw/Leticia_Dolera/Leticia_Dolera_0001.jpg\n",
+            "lfw/Joseph_Fiennes/\n",
+            "lfw/Joseph_Fiennes/Joseph_Fiennes_0001.jpg\n",
+            "lfw/Colin_Prescot/\n",
+            "lfw/Colin_Prescot/Colin_Prescot_0001.jpg\n"
+          ]
+        }
+      ],
+      "source": [
+        "!tar -xzvf /content/drive/MyDrive/lfw.tgz"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1X2EFr3WiOKo",
+        "outputId": "f332fad9-1f65-4e52-dfc6-49368acd687e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Removed 5326 folders\n",
+            "Remaining folders: 423\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "import shutil\n",
+        "count=0\n",
+        "for folder in os.listdir('lfw'):\n",
+        "    if len(os.listdir(f'lfw/{folder}')) < 5:\n",
+        "        shutil.rmtree(f'lfw/{folder}')\n",
+        "        count+=1\n",
+        "print(f\"Removed {count} folders\")\n",
+        "print(f\"Remaining folders: {len(os.listdir('lfw'))}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pz1vtNniCGjf"
+      },
+      "source": [
+        "## Downloading Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 384,
+          "referenced_widgets": [
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+            "8c9ec1f6cb3c4e17bc1df1101b1bc8f7",
+            "be69d2dd1b634a4cb2c4515f293d6687",
+            "fd64018e8af14baeb5d85f9c531a1a51",
+            "be766277e81847318e883b2b90ef4fd1",
+            "75b02f006ff749d98787f5a4465fdfb4"
+          ]
+        },
+        "id": "BzMfQwalCGji",
+        "outputId": "73e0b9b9-4580-4ee7-dc47-d3e4c052001a"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "05b31ee914704cf6aa23b016e9605fa6",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Fetching 6 files:   0%|          | 0/6 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "91d1a5c7baca43f09729be8b10c0fac9",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "variables/variables.index:   0%|          | 0.00/61.2k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d61f94c1b2e24bfb8bd491a7ad9869f1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              ".gitattributes:   0%|          | 0.00/1.59k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5e1636ca1090486cad4cf157c618e986",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "saved_model.pb:   0%|          | 0.00/18.1M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1e74eb5ac4c144b9996faecafdb8c77d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "keras_metadata.pb:   0%|          | 0.00/2.52M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d9999a65dfac41d1b9fc79fdcfd53bde",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "fingerprint.pb:   0%|          | 0.00/56.0 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1037e6dd39494d96842994d401595ae7",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "variables.data-00000-of-00001:   0%|          | 0.00/282M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'/content/model'"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "finetuned_model_dir = \"model\"\n",
+        "\n",
+        "from huggingface_hub import snapshot_download\n",
+        "snapshot_download(repo_id='DnD11/FaceNet_Finetuned', repo_type=\"model\", local_dir=finetuned_model_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mlEhvK1rh_IX"
+      },
+      "source": [
+        "# Evaluation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "v5_9g5wwh0oi"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import numpy as np\n",
+        "from tqdm import tqdm_notebook\n",
+        "from keras.models import load_model\n",
+        "from keras.preprocessing import image\n",
+        "from sklearn.metrics.pairwise import euclidean_distances\n",
+        "from deepface import DeepFace\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "# Function to load and preprocess images\n",
+        "def load_and_preprocess_image(img_path, target_size=(160, 160)):\n",
+        "    img = image.load_img(img_path, target_size=target_size)\n",
+        "    img_array = image.img_to_array(img)\n",
+        "    img_array = np.expand_dims(img_array, axis=0)\n",
+        "    img_array /= 255.0\n",
+        "    return img_array\n",
+        "\n",
+        "# Function to generate embeddings\n",
+        "def generate_embeddings(model, dataset_path, deepface = False):\n",
+        "    embeddings = {}\n",
+        "\n",
+        "    for class_name in tqdm_notebook(os.listdir(dataset_path)):\n",
+        "        class_path = os.path.join(dataset_path, class_name)\n",
+        "        if not os.path.isdir(class_path):\n",
+        "            continue\n",
+        "        embeddings[class_name] = []\n",
+        "\n",
+        "        for img_name in os.listdir(class_path):\n",
+        "            img_path = os.path.join(class_path, img_name)\n",
+        "            img_array = load_and_preprocess_image(img_path)\n",
+        "            if deepface:\n",
+        "                embedding = DeepFace.represent(img_path=img_path, model_name = \"Facenet512\", enforce_detection=False, detector_backend=\"skip\")[0]['embedding']\n",
+        "            else:\n",
+        "                embedding = model.predict(img_array)\n",
+        "            embeddings[class_name].append(embedding)\n",
+        "        embeddings[class_name] = np.array(embeddings[class_name])\n",
+        "\n",
+        "\n",
+        "\n",
+        "    return embeddings\n",
+        "\n",
+        "# Function to calculate intra-cluster distances\n",
+        "def calculate_intra_cluster_metrics(embeddings):\n",
+        "    metrics = {}\n",
+        "\n",
+        "    for label, cluster_embeddings in embeddings.items():\n",
+        "        # cluster_embeddings = np.squeeze(cluster_embeddings, axis=1)\n",
+        "\n",
+        "        avg_embedding = np.mean(cluster_embeddings, axis=0)\n",
+        "        distances = euclidean_distances(cluster_embeddings, [avg_embedding])\n",
+        "        max_distance = np.max(distances)\n",
+        "        min_distance = np.min(distances)\n",
+        "        variance = np.var(distances)\n",
+        "        standard_deviation = np.std(distances)\n",
+        "        percentile_25 = np.percentile(distances, 25)\n",
+        "        percentile_50 = np.percentile(distances, 50)\n",
+        "        percentile_75 = np.percentile(distances, 75)\n",
+        "\n",
+        "        metrics[label] = {\"center\": avg_embedding, \"max_radius\": max_distance, \"min_radius\":min_distance, \"num_samples\": len(cluster_embeddings), \"variance\": variance, \"standard_deviation\": standard_deviation, \"percentile_25\": percentile_25, \"percentile_50\": percentile_50, \"percentile_75\": percentile_75}\n",
+        "\n",
+        "    return metrics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!wget https://huggingface.co/DnD11/FaceNet_Finetuned/resolve/main/embedding_trial2.h5"
+      ],
+      "metadata": {
+        "id": "jazKxH07kWza",
+        "outputId": "543b7abc-0874-436a-ca49-c44085d3c620",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2024-07-20 05:16:03--  https://huggingface.co/DnD11/FaceNet_Finetuned/resolve/main/embedding_trial2.h5\n",
+            "Resolving huggingface.co (huggingface.co)... 18.239.69.83, 18.239.69.71, 18.239.69.50, ...\n",
+            "Connecting to huggingface.co (huggingface.co)|18.239.69.83|:443... connected.\n",
+            "HTTP request sent, awaiting response... 302 Found\n",
+            "Location: https://cdn-lfs-us-1.huggingface.co/repos/03/4f/034f80180b5ba08a0d9fe97aa18e17eb02604e9ba5c3e985eaee5a613e4810f6/901871f1a8502c1e168ce190908e8c5658f350ab7f0626932ab6cea57f67c9be?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27embedding_trial2.h5%3B+filename%3D%22embedding_trial2.h5%22%3B&Expires=1721711763&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyMTcxMTc2M319LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzAzLzRmLzAzNGY4MDE4MGI1YmEwOGEwZDlmZTk3YWExOGUxN2ViMDI2MDRlOWJhNWMzZTk4NWVhZWU1YTYxM2U0ODEwZjYvOTAxODcxZjFhODUwMmMxZTE2OGNlMTkwOTA4ZThjNTY1OGYzNTBhYjdmMDYyNjkzMmFiNmNlYTU3ZjY3YzliZT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=E2AvUZAywXEADz2LG0l40XcFqTC3baxpdK4JQcx1Vb1WsSXwoN0MSB3D-JKqB33w6aFZ03D26OFj%7E7joERMk6QPzHklqFv7qp%7E8KiHV5jobp2rAj6U4LQ6rOoxfLBDcBZHlkX8VsYRG-8b7nzbqyN%7EizxjVNPW2ERGi1U54BtpWoRr9CgKhqblgcMT%7E-qhg0rkGcSJFARlp9fu6widS8N2uSYPQ33q%7EYMtwm3lyInFzrjuGXjxjmh254KjJp9a2MWgqFzbXuFRpdCetfoaKOnyXYvd6S90HAzPUsRsGRpdtEzFlUFr%7EqE-NJoUFqoH1bLUXVRt0tnBDArGl-KU94yw__&Key-Pair-Id=K24J24Z295AEI9 [following]\n",
+            "--2024-07-20 05:16:03--  https://cdn-lfs-us-1.huggingface.co/repos/03/4f/034f80180b5ba08a0d9fe97aa18e17eb02604e9ba5c3e985eaee5a613e4810f6/901871f1a8502c1e168ce190908e8c5658f350ab7f0626932ab6cea57f67c9be?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27embedding_trial2.h5%3B+filename%3D%22embedding_trial2.h5%22%3B&Expires=1721711763&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyMTcxMTc2M319LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzAzLzRmLzAzNGY4MDE4MGI1YmEwOGEwZDlmZTk3YWExOGUxN2ViMDI2MDRlOWJhNWMzZTk4NWVhZWU1YTYxM2U0ODEwZjYvOTAxODcxZjFhODUwMmMxZTE2OGNlMTkwOTA4ZThjNTY1OGYzNTBhYjdmMDYyNjkzMmFiNmNlYTU3ZjY3YzliZT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=E2AvUZAywXEADz2LG0l40XcFqTC3baxpdK4JQcx1Vb1WsSXwoN0MSB3D-JKqB33w6aFZ03D26OFj%7E7joERMk6QPzHklqFv7qp%7E8KiHV5jobp2rAj6U4LQ6rOoxfLBDcBZHlkX8VsYRG-8b7nzbqyN%7EizxjVNPW2ERGi1U54BtpWoRr9CgKhqblgcMT%7E-qhg0rkGcSJFARlp9fu6widS8N2uSYPQ33q%7EYMtwm3lyInFzrjuGXjxjmh254KjJp9a2MWgqFzbXuFRpdCetfoaKOnyXYvd6S90HAzPUsRsGRpdtEzFlUFr%7EqE-NJoUFqoH1bLUXVRt0tnBDArGl-KU94yw__&Key-Pair-Id=K24J24Z295AEI9\n",
+            "Resolving cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)... 18.239.94.6, 18.239.94.3, 18.239.94.84, ...\n",
+            "Connecting to cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)|18.239.94.6|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 94732496 (90M) [binary/octet-stream]\n",
+            "Saving to: ‘embedding_trial2.h5’\n",
+            "\n",
+            "embedding_trial2.h5 100%[===================>]  90.34M  38.2MB/s    in 2.4s    \n",
+            "\n",
+            "2024-07-20 05:16:06 (38.2 MB/s) - ‘embedding_trial2.h5’ saved [94732496/94732496]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "XCtTmUv0iJy1",
+        "outputId": "4f313e98-1443-4f8f-821d-9f06b7322e98",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "ca422fda8afc4f0797d65b1a8a808cdc",
+            "5707fef239e2414787163e923358a937",
+            "4815dd96a58145538c30d925350658f5",
+            "11cc823478b1451b82490b4fd969e27b",
+            "c5f86e350f9f4267baf2f2aea4cba7d5",
+            "8c089b27dc2d45b9bfd5826825bf85a6",
+            "83cbc864040d45e1b31df5e0a65969a6",
+            "89567efa35a44cfaabee6d5b3009d66c",
+            "a2ebedd55767443fa2924df342ff1b4c",
+            "8926d506fce8406a990cf53890cac867",
+            "cd465523f78a43bdb8124b88a1fb7024"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/423 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ca422fda8afc4f0797d65b1a8a808cdc"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Path to the dataset\n",
+        "dataset_path = 'lfw'\n",
+        "model=1\n",
+        "# Generate embeddings for the original model\n",
+        "embeddings_original = generate_embeddings(model, dataset_path, True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "fLYr8we0iJy1",
+        "outputId": "f72bce45-8b70-417d-987c-c34e3779f2fe",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "00a64b4ea5314bccb09759474d50b522",
+            "ab160914cad6459da642e8edeb9c7147",
+            "ad8933fcf2484969a181c1b5e1f33fe4",
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+            "3d9bac658ba9442888e9464b57fa551f",
+            "3361f1363a274662bb683dad5c04484a"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/423 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "00a64b4ea5314bccb09759474d50b522"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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+            "1/1 [==============================] - 0s 31ms/step\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
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+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 31ms/step\n",
+            "1/1 [==============================] - 0s 30ms/step\n",
+            "1/1 [==============================] - 0s 29ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 30ms/step\n",
+            "1/1 [==============================] - 0s 49ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 29ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 28ms/step\n",
+            "1/1 [==============================] - 0s 30ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 29ms/step\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "1/1 [==============================] - 0s 30ms/step\n",
+            "1/1 [==============================] - 0s 30ms/step\n",
+            "1/1 [==============================] - 0s 28ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load the fine-tuned model\n",
+        "import tensorflow as tf\n",
+        "finetuned_model = tf.keras.models.load_model('/content/embedding_trial2.h5')\n",
+        "embeddings_finetuned = generate_embeddings(finetuned_model, dataset_path)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pickle\n",
+        "with open('embeddings_original.pkl', 'wb') as f:\n",
+        "    pickle.dump(embeddings_original, f)\n",
+        "with open('embeddings_finetuned.pkl', 'wb') as f:\n",
+        "    pickle.dump(embeddings_finetuned, f)"
+      ],
+      "metadata": {
+        "id": "SVsPGCy1qnT6"
+      },
+      "execution_count": 33,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mlpjNEeRiJy2"
+      },
+      "source": [
+        "## Calculate Metrics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "embeddings_finetuned[\"Wen_Jiabao\"].shape"
+      ],
+      "metadata": {
+        "id": "rQXD7DSwsuSl",
+        "outputId": "d874e86b-76e7-4634-c226-773a2f02171b",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(13, 1, 512)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "OuTDI5iqiJy2"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate intra-cluster distances for both models\n",
+        "metrics_original = calculate_intra_cluster_metrics(embeddings_original)\n",
+        "metrics_finetuned = calculate_intra_cluster_metrics(embeddings_finetuned)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "Xn4zoiytiJy3",
+        "outputId": "3d2eaec1-17ae-44cb-c5bd-42c6d9a11c74",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 927
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def plot_histogram(metrics, key, title, xlabel):\n",
+        "    values = [metrics[label][key] for label in metrics]\n",
+        "    plt.figure()\n",
+        "    plt.hist(values, bins=20, alpha=0.7)\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('Frequency')\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_histogram(metrics_original, 'max_radius', 'Histogram of Max Radius (Original Model)', 'Max Radius')\n",
+        "plot_histogram(metrics_finetuned, 'max_radius', 'Histogram of Max Radius (Fine-tuned Model)', 'Max Radius')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "PeTLOsthiJy3",
+        "outputId": "bdc1c1ca-7cbb-4654-8a0d-2c4158501a59",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 887
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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dtWjRwvZxlSpVdOutt+qPP/6wtS1cuFANGjQo9NVx/szZZ599ptq1a6tWrVp238c777xTkmzfx/zZvM8//9yh0y3Hjh2TYRiXnNFZuHCh7ed7xYoVl71U3tfX127218PDQ40bN7Y79uIe17XQu3dvu+PP/77l13vs2DF988036tWrl06cOGGr9ejRo2rfvr3S0tJ04MABSX/9nmvSpIkaN25sG69KlSp64IEHHK6rb9++2rlzp3766Sfbv0Wd/vrqq68UGBho9/ugbNmyevbZZ3Xy5El9++23tn5lypSxzSJLf83oPfPMM3bjOXLMl1KpUiW707lmwikwk2jcuLHdIug+ffro9ttv16BBg3TPPffIw8NDe/bsUXBwsMqXL2+3b/4ppT179tjaPDw89OGHH6pRo0by8vLSzJkzC5xKkSQ3Nze7ECPJdvqjqEvXDx8+rNOnT+vWW28tsK127dqyWq3at2+f7TRFceXXX9S4y5Yt06lTp+Tj4+PQuBcrW7asevbsqblz56px48bat29fkb8Q09LSZBiGatasWeRYF9f9z35ly5Yt8LUtzLFjxzRmzBh9+umntsXd+f4ZCiTp5ptvtvs4/w/Pn3/+aXcFl6NOnjwpSQWeXxd76aWXtHLlSjVu3Fg1atRQu3bt1LdvXzVr1qxYn+P777/XqFGjlJqaWmDdUnZ2tl1g/OdxSn8d68UL1Xft2qX77rvvkp8zLS1NW7dutZ2G+6f8r3nv3r31wQcf6NFHH9Xw4cPVtm1bde/eXT169JCb2+VfixqXWDfXsmXLIhdBF+amm24q8PNaqVIlu9O0xT2uY8eO6dy5c7Z2b2/vYgVzR1zqOSn9dTrTMAyNHDlSI0eOLLLekJAQ7dmzRzExMQW2F/Z74XJuv/121apVS3PnzlXFihUVGBhoC4j/tGfPHtWsWbPA9/qfv1/37NmjoKCgAouS/1mfI8d8KYZhFPq72wwIQCbl5uamNm3a6N///rfS0tIcDhOStGzZMkl/zSqkpaUpPDzc2WXekPr27avExESNHj1aDRo0UJ06dQrtZ7VaZbFY9PXXXxe6QNpZV2X06tVLP/zwg1588UVFRkbK19dXVqtVHTp0KHQmoqjF2pf6A1wcv/32mySpRo0aRfapXbu2tm/frqVLlyopKUkLFy7Uu+++q/j4eNsl1UXZtWuX2rZtq1q1amny5MkKDQ2Vh4eHvvrqK02ZMqXAsTrrOK1Wq+rVq2dbD/dP+Wu0vL299d1332nVqlX68ssvlZSUpPnz5+vOO+/U8uXLi6yncuXKslgsTr2CsDjHXtzj6t69u232QpLi4uIue7M+i8VS6Nc5/wIKR+vN/94OHTpU7du3L7TvpZ53V6Nv376aPn26ypcvr969excrzDqDs475zz//LPJFWGlHADKxCxcuSPr7lXm1atW0cuVKnThxwu5Vev5VDhffCHDTpk0aO3asBgwYoI0bN+rRRx/V5s2bC7zys1qt+uOPP+wWveZfBVTUfXuqVKmicuXKafv27QW2bdu2TW5ubrZfvo68csmvv6hx/f39r2r2J1/z5s118803KyUl5ZILh6tXry7DMBQeHl7kouCL605LS7N7dXn+/Hnt3r1bDRo0KHLfP//8U8nJyRozZozi4+Nt7WlpaY4cklPMnj1bFotFd9111yX7+fj4qHfv3urdu7fOnTun7t2769VXX9WIESPk5eVV5Pf8iy++UG5urpYsWWI3Y3A1p2qqV69uC26X6vPrr7+qbdu2l30+urm5qW3btmrbtq0mT56s1157Ta+88opWrVql2NjYQvcpU6aMqlevrt27d1/xcVyJ4h7Xm2++aRfOgoODJV36Z7NSpUp2p9vyXTzL7Ij8mdCyZcsW+XXMV61atUKf/4X9XiiOvn37Kj4+XocOHdLs2bMv+Xk3bdokq9VqF5L++fu1WrVqSk5O1smTJ+1eBP2zPkeOuSgXLlzQvn371KVLlyva/0bHGiCTOn/+vJYvXy4PDw/bFGynTp2Ul5end955x67vlClTZLFYbFeHnD9/Xv3791dwcLD+/e9/a9asWcrMzNQLL7xQ6Oe6eDzDMPTOO++obNmyatu2baH93d3d1a5dO33++ed2p8kyMzM1d+5cNW/e3HYqJj+wFOeuwEFBQYqMjNRHH31k1/+3337T8uXL1alTp8uOURwWi0VvvfWWRo0apYceeqjIft27d5e7u7vGjBlT4NWwYRi2y9Sjo6NVpUoVJSYm2p1qmDVr1mWPO/+V8z/Hnzp1qgNHdPVef/11LV++XL17977kq81/Xprv4eGhOnXqyDAMnT9/XlLR3/PCjjU7O1szZ8684rrvu+8+/frrr/rf//5XYFv+5+nVq5cOHDigGTNmFOhz5swZnTp1SlLBK7UkKTIyUpIue1l506ZN9fPPPzta/lUp7nFFRUUpNjbW9sif8SxXrpykwn82q1evrm3btunw4cO2tl9//bVYV8QVpmrVqmrdurXee+89HTp0qMD2iz9Pp06dtHbtWv3444922x15i5GLVa9eXVOnTlVCQoLduqJ/6tSpkzIyMuzug3XhwgW9/fbb8vX1td16o1OnTrpw4YLd7Rvy8vL09ttv243nyDEXZcuWLTp79qzuuOOOy/YtjZgBMomvv/7a9kojKytLc+fOVVpamoYPH24LE507d1abNm30yiuvKD09XQ0aNNDy5cv1+eef6/nnn7fdg2T8+PHauHGjkpOTVb58edWvX1/x8fH6v//7P/Xo0cMuSHh5eSkpKUlxcXGKiYnR119/rS+//FIvv/xykWsL8j9H/j1TnnrqKZUpU0bvvfeecnNz7e5XEhkZKXd3d02YMEHZ2dny9PS03QemMG+88YY6duyopk2b6pFHHrFdBu/n5+fU9yLr2rWrunbtesk+1atX1/jx4zVixAilp6erW7duKl++vHbv3q3//e9/evzxxzV06FCVLVtW48eP1xNPPKE777xTvXv31u7duzVz5szLrgGqUKGCWrZsqYkTJ+r8+fMKCQnR8uXLS2w24cKFC7a3Azl79qz27NmjJUuWaNOmTWrTpo3ef//9S+7frl07BQYGqlmzZgoICNDWrVv1zjvv6O6777bNSkZFRUmSXnnlFd1///0qW7asOnfurHbt2snDw0OdO3fWE088oZMnT2rGjBmqWrVqoX8giuPFF1/UggUL1LNnTz388MOKiorSsWPHtGTJEiUmJqpBgwZ66KGH9N///ldPPvmkVq1apWbNmikvL0/btm3Tf//7Xy1btkzR0dEaO3asvvvuO919992qVq2asrKy9O677+qmm25S8+bNL1lH165dNXv2bO3YseOSs4XOVNzjKoq3t7fq1Kmj+fPn65ZbblHlypV122236bbbbtPDDz+syZMnq3379nrkkUeUlZWlxMRE1a1b94oX20+bNk3NmzdXvXr19NhjjykiIkKZmZlKTU3V/v37bW/5MGzYMM2ePVsdOnTQc889Z7sMPn+G5krkX8J+KY8//rjee+899e/fX+vXr1dYWJgWLFig77//XlOnTrU9vzt37qxmzZpp+PDhSk9PV506dbRo0aJC1+sV95iLsmLFCpUrV+6ys7Kl1jW95gzXXGGXwXt5eRmRkZHG9OnTbZfy5jtx4oTxwgsvGMHBwUbZsmWNmjVrGm+88Yat3/r1640yZcrYXdpuGH9dptmoUSMjODjY+PPPPw3D+Puy6F27dhnt2rUzypUrZwQEBBijRo0y8vLy7PZXIZfLbtiwwWjfvr3h6+trlCtXzmjTpo3xww8/FDjGGTNmGBEREYa7u3uxLolfuXKl0axZM8Pb29uoUKGC0blzZ2PLli12fa70MvhL+edl8PkWLlxoNG/e3PDx8TF8fHyMWrVqGU8//bSxfft2u37vvvuuER4ebnh6ehrR0dHGd999V+By4sIug9+/f79x7733GhUrVjT8/PyMnj17GgcPHizwNb/48t6L5T+HLnd5cP5tD/If5cqVM8LCwoz77rvPWLBgQYHvef7X5OL633vvPaNly5bGv/71L8PT09OoXr268eKLLxrZ2dl2+40bN84ICQkx3Nzc7GpbsmSJUb9+fcPLy8sICwszJkyYYHz44YcF6q9WrZpx9913X7YewzCMo0ePGoMGDTJCQkIMDw8P46abbjLi4uKMI0eO2PqcO3fOmDBhglG3bl3D09PTqFSpkhEVFWWMGTPGVntycrLRtWtXIzg42PDw8DCCg4ONPn36GDt27Ljk19Uw/rpM3d/f3xg3bpxde1Hfs3xFXQZf2PPwn5egF/e4LuWHH34woqKiDA8PjwLPtzlz5hgRERGGh4eHERkZaSxbtqzIy+DfeOONAmMX9jtj165dRr9+/YzAwECjbNmyRkhIiHHPPfcYCxYssOu3adMmo1WrVoaXl5cREhJijBs3zvjPf/7j8GXwl6J/XAZvGIaRmZlpDBgwwPD39zc8PDyMevXq2f2s5jt69Kjx0EMPGRUqVDD8/PyMhx56yHYbiX/2L84xF3UZfExMjPHggw9e8jhKM4thXOXKRqAI/fv314IFC2xrjABcuXHjxmnmzJlKS0srclEwUFwbN25Uw4YNtWHDBtupWLNhDRAA3ABeeOEFnTx5Up9++qmrS0Ep8Prrr6tHjx6mDT8Sa4AA4Ibg6+tb4D5OwJUiSDMDBAAATIg1QAAAwHSYAQIAAKZDAAIAAKbDIuhCWK1WHTx4UOXLlzftm8QBAHCjMQxDJ06cUHBw8GXfl40AVIiDBw/a3msKAADcWPbt26ebbrrpkn0IQIXIvyX5vn37bG8TAQAArm85OTkKDQ21e0PvohCACpF/2qtChQoEIAAAbjDFWb7CImgAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA63AgRgGnk5eVp9erVOnTokIKCgtSiRQu5u7u7uiwALsAMEABTWLRokapXr642bdqob9++atOmjapXr65Fixa5ujQALkAAAlDqLVq0SPfdd5+ysrLs2rOysnTfffcRggATIgABKNXy8vL05JNPXrLPwIEDlZeXd40qAnA9IAABKNVSUlJ0+PBhSVLbtm2VmpqqEydOKDU1VW3btpX010xQSkqKC6sEcK0RgACUat98840kqUmTJvr888/VpEkT+fr62n18cT8A5kAAAlCq7du3T5L0wAMPyM3N/leem5ub+vTpY9cPgDkQgACUaqGhoZKkTz75RFar1W6b1WrVvHnz7PoBMAcCEIBS7c4775QkrV27Vl27drVbA9S1a1etXbvWrh8Ac7AYhmG4uojrTU5Ojvz8/JSdna0KFSq4uhwAVyEvL09BQUE6fPiwvL29debMGdu2cuXK6fTp06pataoOHjzITRGBG5wjf7+ZAQJQqrm7uysxMfGSfaZPn074AUyGAASg1OvevbsWLlyoqlWr2rVXrVpVCxcuVPfu3V1UGQBXIQABMC1WAADmxZuhAij1Fi1apB49eujuu+/WsGHDbGuBvv76a/Xo0UMLFixgFggwGRZBF4JF0EDpkZeXpxo1asjf31+HDx/Wnj17bNuqVaumKlWq6OjRo0pLS2MdEHCDYxE0APx/q1evVnp6un7++WfVr1/f7jL4+vXr6+eff9bu3bu1evVqV5cK4BoiAAEo1Q4cOCBJ6tixoxYvXmz3VhiLFy9Wx44d7foBMAcCEIBSLf+NULt3717oW2F069bNrh8AcyAAASjVqlSpIumvhdCFvRXG4sWL7foBMAcCEIBSLSQkRJKUlJSkbt262a0B6tatm5KSkuz6ATAHrgIrBFeBAaXHxVeBHTlyROnp6bZt4eHh+te//sVVYEAp4cjfb+4DBKBUc3d315tvvmm7D9DQoUNt9wFKSkrSl19+qQULFhB+AJMhAAEo9bp3764FCxZoyJAhWrp0qa09PDycmyACJsUpsEJwCgwonfLy8rR69WodOnRIQUFBatGiBTM/QCnCKTAAKIS7u7tat27t6jIAXAe4CgwAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJjOdRGApk2bprCwMHl5eSkmJkY//vhjsfb79NNPZbFYbO/mnM8wDMXHxysoKEje3t6KjY1VWlpaCVQOAABuRC4PQPPnz9fgwYM1atQobdiwQQ0aNFD79u2VlZV1yf3S09M1dOhQtWjRosC2iRMn6q233lJiYqLWrVsnHx8ftW/fXmfPni2pwwAAADcQl98JOiYmRo0aNdI777wjSbJarQoNDdUzzzyj4cOHF7pPXl6eWrZsqYcfflirV6/W8ePHtXjxYkl/zf4EBwdryJAhGjp0qCQpOztbAQEBmjVrlu6///7L1sSdoIHSiTtBA6WbI3+/XToDdO7cOa1fv16xsbG2Njc3N8XGxio1NbXI/caOHauqVavqkUceKbBt9+7dysjIsBvTz89PMTExRY6Zm5urnJwcuweA0mXRokWqUaOG2rRpo759+6pNmzaqUaOGFi1a5OrSALiASwPQkSNHlJeXp4CAALv2gIAAZWRkFLrPmjVr9J///EczZswodHv+fo6MmZCQID8/P9sjNDTU0UMBcB1btGiRevTooczMTLv2zMxM9ejRgxAEmJDL1wA54sSJE3rooYc0Y8YM+fv7O23cESNGKDs72/bYt2+f08YG4Fp5eXkaOHCgDMNQ27ZtlZqaqhMnTig1NVVt27aVYRgaOHCg8vLyXF0qgGvIpW+G6u/vL3d390JflQUGBhbov2vXLqWnp6tz5862NqvVKkkqU6aMtm/fbtsvMzNTQUFBdmNGRkYWWoenp6c8PT2v9nAAXIdSUlKUlZWl5s2b6/PPP5eb21+v+5o0aaLPP/9cLVu21Pfff6+UlBS1bdvWxdUCuFZcOgPk4eGhqKgoJScn29qsVquSk5PVtGnTAv1r1aqlzZs3a+PGjbZHly5d1KZNG23cuFGhoaEKDw9XYGCg3Zg5OTlat25doWMCKN1SUlIkSWPGjLGFn3xubm4aPXq0XT8A5uDSGSBJGjx4sOLi4hQdHa3GjRtr6tSpOnXqlAYMGCBJ6tevn0JCQpSQkCAvLy/ddtttdvtXrFhRkuzan3/+eY0fP141a9ZUeHi4Ro4cqeDg4AL3CwJgLoVdBQbAnFwegHr37q3Dhw8rPj5eGRkZioyMVFJSkm0R8969ewu8arucYcOG6dSpU3r88cd1/PhxNW/eXElJSfLy8iqJQwBwHWvdurXGjx+vp59+WmfPnlV6erptW/4NWPP7ATAPl98H6HrEfYCA0iMvL0+VK1dWTk6OAgICNG7cON1zzz1aunSpRo4cqczMTFWoUEHHjh3jnkDADc6Rv98unwECgJLm5eWlnJwcZWdn6/HHH7e1e3t727YDMJcb6jJ4AHDU6tWrlZWVpYSEhELvD/baa68pKytLq1evdlGFAFyBAASgVDt06JAkadCgQdq1a5dWrVqluXPnatWqVdq5c6cGDRpk1w+AOXAKDECpln8/sN9++01NmjQpsNj5t99+s+sHwByYAQJQqrVo0UJhYWF67bXXbDdOzWe1WpWQkKDw8HAuiQdMhgAEoFRzd3fXm2++qaVLl6pbt252b4XRrVs3LV26VJMmTeIKMMBkOAUGoNTr3r27FixYoCFDhuiOO+6wtYeHh2vBggXq3r27C6sD4ArcB6gQ3AcIKJ0KuxM0Mz9A6cF9gACgEO7u7tzxGYAk1gABAAATIgABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADT4UaIAK57p0+f1rZt25wy1pkzZ5Senq6wsDB5e3s7ZcxatWqpXLlyThkLwLVBAAJw3du2bZuioqJcXUaR1q9fr4YNG7q6DAAOIAABuO7VqlVL69evd8pYW7du1YMPPqg5c+aodu3aThmzVq1aThkHwLVDAAJw3StXrpzTZ1hq167NrA1gYiyCBgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApnNdBKBp06YpLCxMXl5eiomJ0Y8//lhk30WLFik6OloVK1aUj4+PIiMjNXv2bLs+/fv3l8VisXt06NChpA8DAADcIMq4uoD58+dr8ODBSkxMVExMjKZOnar27dtr+/btqlq1aoH+lStX1iuvvKJatWrJw8NDS5cu1YABA1S1alW1b9/e1q9Dhw6aOXOm7WNPT89rcjwAAOD65/IZoMmTJ+uxxx7TgAEDVKdOHSUmJqpcuXL68MMPC+3funVr3Xvvvapdu7aqV6+u5557TvXr19eaNWvs+nl6eiowMND2qFSp0rU4HAAAcANwaQA6d+6c1q9fr9jYWFubm5ubYmNjlZqaetn9DcNQcnKytm/frpYtW9ptS0lJUdWqVXXrrbdq4MCBOnr0aJHj5ObmKicnx+4BAABKL5eeAjty5Ijy8vIUEBBg1x4QEKBt27YVuV92drZCQkKUm5srd3d3vfvuu7rrrrts2zt06KDu3bsrPDxcu3bt0ssvv6yOHTsqNTVV7u7uBcZLSEjQmDFjnHdgAADguubyNUBXonz58tq4caNOnjyp5ORkDR48WBEREWrdurUk6f7777f1rVevnurXr6/q1asrJSVFbdu2LTDeiBEjNHjwYNvHOTk5Cg0NLfHjAAAAruHSAOTv7y93d3dlZmbatWdmZiowMLDI/dzc3FSjRg1JUmRkpLZu3aqEhARbAPqniIgI+fv7a+fOnYUGIE9PTxZJAwBgIi5dA+Th4aGoqCglJyfb2qxWq5KTk9W0adNij2O1WpWbm1vk9v379+vo0aMKCgq6qnoBAEDp4PJTYIMHD1ZcXJyio6PVuHFjTZ06VadOndKAAQMkSf369VNISIgSEhIk/bVeJzo6WtWrV1dubq6++uorzZ49W9OnT5cknTx5UmPGjNF9992nwMBA7dq1S8OGDVONGjXsLpMHAADm5fIA1Lt3bx0+fFjx8fHKyMhQZGSkkpKSbAuj9+7dKze3vyeqTp06paeeekr79++Xt7e3atWqpTlz5qh3796SJHd3d23atEkfffSRjh8/ruDgYLVr107jxo3jNBcAAJAkWQzDMFxdxPUmJydHfn5+ys7OVoUKFVxdDgAn2rBhg6KiorR+/Xo1bNjQ1eUAcCJH/n67/EaIAAAA1xoBCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmM5VBaCzZ886qw4AAIBrxuEAZLVaNW7cOIWEhMjX11d//PGHJGnkyJH6z3/+4/QCAQAAnM3hADR+/HjNmjVLEydOlIeHh639tttu0wcffODU4gAAAEqCwwHo448/1vvvv68HHnhA7u7utvYGDRpo27ZtTi0OAACgJDgcgA4cOKAaNWoUaLdarTp//rxTigIAAChJDgegOnXqaPXq1QXaFyxYoNtvv90pRQEAAJSkMo7uEB8fr7i4OB04cEBWq1WLFi3S9u3b9fHHH2vp0qUlUSMAAIBTOTwD1LVrV33xxRdauXKlfHx8FB8fr61bt+qLL77QXXfdVRI1AgAAOJXDM0CS1KJFC61YscLZtQAAAFwTDs8A/fTTT1q3bl2B9nXr1unnn392SlEAAAAlyeEA9PTTT2vfvn0F2g8cOKCnn37aKUUBAACUJIcD0JYtW9SwYcMC7bfffru2bNnilKIAAABKksMByNPTU5mZmQXaDx06pDJlrmhJEQAAwDXlcABq166dRowYoezsbFvb8ePH9fLLL3MVGAAAuCE4PGUzadIktWzZUtWqVbPd+HDjxo0KCAjQ7NmznV4gAACAszkcgEJCQrRp0yZ98skn+vXXX+Xt7a0BAwaoT58+Klu2bEnUCAAA4FRXtGjHx8dHjz/+uLNrAQAAuCauKAClpaVp1apVysrKktVqtdsWHx/vlMIAAABKisMBaMaMGRo4cKD8/f0VGBgoi8Vi22axWAhAAADguudwABo/frxeffVVvfTSSyVRDwAAQIlz+DL4P//8Uz179iyJWgAAAK4JhwNQz549tXz58pKoBQAA4Jpw+BRYjRo1NHLkSK1du1b16tUrcOn7s88+67TiAAAASoLDAej999+Xr6+vvv32W3377bd22ywWCwEIAABc9xwOQLt37y6JOgAAAK4Zh9cAAQAA3Oiu6EaI+/fv15IlS7R3716dO3fObtvkyZOdUhgAAEBJcTgAJScnq0uXLoqIiNC2bdt02223KT09XYZhqGHDhiVRIwAAgFM5fApsxIgRGjp0qDZv3iwvLy8tXLhQ+/btU6tWrbg/EAAAuCE4HIC2bt2qfv36SZLKlCmjM2fOyNfXV2PHjtWECROcXiAAAICzORyAfHx8bOt+goKCtGvXLtu2I0eOOK8yAACAEuLwGqAmTZpozZo1ql27tjp16qQhQ4Zo8+bNWrRokZo0aVISNQIAADiVwwFo8uTJOnnypCRpzJgxOnnypObPn6+aNWtyBRgAALghOByAIiIibP/38fFRYmKiUwsCAAAoaQ6vAYqIiNDRo0cLtB8/ftwuHAEAAFyvHA5A6enpysvLK9Cem5urAwcOOKUoAACAklTsU2BLliyx/X/ZsmXy8/OzfZyXl6fk5GSFhYU5tTgAAICSUOwA1K1bN0l/veN7XFyc3bayZcsqLCxMb775plOLAwAAKAnFPgVmtVpltVp18803Kysry/ax1WpVbm6utm/frnvuueeKipg2bZrCwsLk5eWlmJgY/fjjj0X2XbRokaKjo1WxYkX5+PgoMjJSs2fPtutjGIbi4+MVFBQkb29vxcbGKi0t7YpqAwAApY/Da4B2794tf39/u7bjx49fcQHz58/X4MGDNWrUKG3YsEENGjRQ+/btlZWVVWj/ypUr65VXXlFqaqo2bdqkAQMGaMCAAVq2bJmtz8SJE/XWW28pMTFR69atk4+Pj9q3b6+zZ89ecZ0AAKD0cDgATZgwQfPnz7d93LNnT1WuXFkhISH69ddfHS5g8uTJeuyxxzRgwADVqVNHiYmJKleunD788MNC+7du3Vr33nuvateurerVq+u5555T/fr1tWbNGkl/zf5MnTpV//d//6euXbuqfv36+vjjj3Xw4EEtXrzY4foAAEDp43AASkxMVGhoqCRpxYoVWrlypZKSktSxY0e9+OKLDo117tw5rV+/XrGxsX8X5Oam2NhYpaamXnZ/wzCUnJys7du3q2XLlpL+mqHKyMiwG9PPz08xMTFFjpmbm6ucnBy7BwAAKL0cvhFiRkaGLQAtXbpUvXr1Urt27RQWFqaYmBiHxjpy5Ijy8vIUEBBg1x4QEKBt27YVuV92drZCQkKUm5srd3d3vfvuu7rrrrts9eWP8c8x87f9U0JCgsaMGeNQ7QAA4Mbl8AxQpUqVtG/fPklSUlKSbabFMIxC7w9UEsqXL6+NGzfqp59+0quvvqrBgwcrJSXliscbMWKEsrOzbY/84wMAAKWTwzNA3bt3V9++fVWzZk0dPXpUHTt2lCT98ssvqlGjhkNj+fv7y93dXZmZmXbtmZmZCgwMLHI/Nzc32+eKjIzU1q1blZCQoNatW9v2y8zMVFBQkN2YkZGRhY7n6ekpT09Ph2oHAAA3LodngKZMmaJBgwapTp06WrFihXx9fSVJhw4d0lNPPeXQWB4eHoqKilJycrKtzWq1Kjk5WU2bNi32OPmX4ktSeHi4AgMD7cbMycnRunXrHBoTAACUXg7PAJUtW1ZDhw4t0P7CCy9cUQGDBw9WXFycoqOj1bhxY02dOlWnTp3SgAEDJEn9+vVTSEiIEhISJP21Xic6OlrVq1dXbm6uvvrqK82ePVvTp0+X9NeNGp9//nmNHz9eNWvWVHh4uEaOHKng4GDbzRwBXDtpaWk6ceKEq8uw2bp1q92/14vy5curZs2ari4DMI1iBaAlS5aoY8eOKlu2rN1bYhSmS5cuDhXQu3dvHT58WPHx8crIyFBkZKSSkpJsi5j37t0rN7e/J6pOnTqlp556Svv375e3t7dq1aqlOXPmqHfv3rY+w4YN06lTp/T444/r+PHjat68uZKSkuTl5eVQbQCuTlpamm655RZXl1GoBx980NUlFLBjxw5CEHCNWAzDMC7Xyc3NTRkZGapatapdGCkwmMVyzRZCl6ScnBz5+fkpOztbFSpUcHU5wA1rw4YNioqK0pw5c1S7dm1XlyNJOnPmjNLT0xUWFiZvb29XlyPpr9moBx98UOvXr1fDhg1dXQ5ww3Lk73exZoCsVmuh/weA4qhdu/Z19Ye9WbNmri4BgIs5vAgaAADgRufQImir1apZs2Zp0aJFSk9Pl8ViUXh4uHr06KGHHnpIFoulpOoEAABwmmLPABmGoS5duujRRx/VgQMHVK9ePdWtW1d79uxR//79de+995ZknQAAAE5T7BmgWbNm6bvvvlNycrLatGljt+2bb75Rt27d9PHHH6tfv35OLxIAAMCZij0DNG/ePL388ssFwo8k3XnnnRo+fLg++eQTpxYHAABQEoodgDZt2qQOHToUub1jx4769ddfnVIUAABASSp2ADp27FiBd1i/WEBAgP7880+nFAUAAFCSih2A8vLyVKZM0UuG3N3ddeHCBacUBQAAUJKKvQjaMAz179+/yHdNz38zUgAAgOtdsQNQXFzcZftwBRgAALgRFDsAzZw5syTrAAAAuGZ4KwwAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6xb4K7GJpaWlatWqVsrKyZLVa7bbFx8c7pTAAAICS4nAAmjFjhgYOHCh/f38FBgbKYrHYtlksFgIQAAC47jkcgMaPH69XX31VL730UknUAwAAUOIcXgP0559/qmfPniVRCwAAwDXhcADq2bOnli9fXhK1AAAAXBMOnwKrUaOGRo4cqbVr16pevXoqW7as3fZnn33WacUBAACUBIcD0Pvvvy9fX199++23+vbbb+22WSwWAhAAALjuORyAdu/eXRJ1AAAAXDPcCBEAAJjOFd0Icf/+/VqyZIn27t2rc+fO2W2bPHmyUwoDAAAoKQ4HoOTkZHXp0kURERHatm2bbrvtNqWnp8swDDVs2LAkagQAAHAqh0+BjRgxQkOHDtXmzZvl5eWlhQsXat++fWrVqhX3BwIAADcEhwPQ1q1b1a9fP0lSmTJldObMGfn6+mrs2LGaMGGC0wsEAABwNocDkI+Pj23dT1BQkHbt2mXbduTIEedVBgAAUEIcXgPUpEkTrVmzRrVr11anTp00ZMgQbd68WYsWLVKTJk1KokYAAACncjgATZ48WSdPnpQkjRkzRidPntT8+fNVs2ZNrgADAAA3BIcDUEREhO3/Pj4+SkxMdGpBAAAAJe2K7gMEAMVhuXBWtwe6yfv4Dukg910tivfxHbo90E2WC2ddXQpgGsUKQJUrV9aOHTvk7++vSpUqyWKxFNn32LFjTisOwI3N6+RebXjCV/ruCek7V1dz/aotacMTvtp6cq+kO1xdDmAKxQpAU6ZMUfny5SVJU6dOLcl6AJQiZ31vVsP3TuqTTz5R7Vq1XF3OdWvrtm164IEH9J9ON7u6FMA0ihWA4uLiCv0/AFyKUcZLv2RYdabiLVJwpKvLuW6dybDqlwyrjDJeri4FMI1iBaCcnJxiD1ihQoUrLgYAAOBaKFYAqlix4iXX/VwsLy/vqgoCAAAoacUKQKtWrbL9Pz09XcOHD1f//v3VtGlTSVJqaqo++ugjJSQklEyVAAAATlSsANSqVSvb/8eOHavJkyerT58+trYuXbqoXr16ev/991kjBAAArnsO35gjNTVV0dHRBdqjo6P1448/OqUoAACAkuRwAAoNDdWMGTMKtH/wwQcKDQ11SlEAAAAlyeE7QU+ZMkX33Xefvv76a8XExEiSfvzxR6WlpWnhwoVOLxAAAMDZHJ4B6tSpk3bs2KHOnTvr2LFjOnbsmDp37qwdO3aoU6dOJVEjAACAU13Re4GFhobqtddec3YtAAAA18QVvTvh6tWr9eCDD+qOO+7QgQMHJEmzZ8/WmjVrnFocAABASXA4AC1cuFDt27eXt7e3NmzYoNzcXElSdnY2s0IAAOCG4HAAGj9+vBITEzVjxgyVLVvW1t6sWTNt2LDBqcUBAACUBIcD0Pbt29WyZcsC7X5+fjp+/LgzagIAAChRDgegwMBA7dy5s0D7mjVrFBERcUVFTJs2TWFhYfLy8lJMTMwlb6g4Y8YMtWjRQpUqVVKlSpUUGxtboH///v1lsVjsHh06dLii2gAAQOnjcAB67LHH9Nxzz2ndunWyWCw6ePCgPvnkEw0dOlQDBw50uID58+dr8ODBGjVqlDZs2KAGDRqoffv2ysrKKrR/SkqK+vTpo1WrVik1NVWhoaFq166dbTF2vg4dOujQoUO2x7x58xyuDQAAlE4OXwY/fPhwWa1WtW3bVqdPn1bLli3l6empoUOH6plnnnG4gMmTJ+uxxx7TgAEDJEmJiYn68ssv9eGHH2r48OEF+n/yySd2H3/wwQdauHChkpOT1a9fP1u7p6enAgMDHa4HAACUfg7PAFksFr3yyis6duyYfvvtN61du1aHDx/WuHHjHP7k586d0/r16xUbG/t3QW5uio2NVWpqarHGOH36tM6fP6/KlSvbtaekpKhq1aq69dZbNXDgQB09erTIMXJzc5WTk2P3AAAApdcV3QhRkjw8PFSnTp2r+uRHjhxRXl6eAgIC7NoDAgK0bdu2Yo3x0ksvKTg42C5EdejQQd27d1d4eLh27dqll19+WR07dlRqaqrc3d0LjJGQkKAxY8Zc1bEAAIAbR7ED0MMPP1ysfh9++OEVF+Oo119/XZ9++qlSUlLk5eVla7///vtt/69Xr57q16+v6tWrKyUlRW3bti0wzogRIzR48GDbxzk5ObyxKwAApVixA9CsWbNUrVo13X777TIMwymf3N/fX+7u7srMzLRrz8zMvOz6nUmTJun111/XypUrVb9+/Uv2jYiIkL+/v3bu3FloAPL09JSnp6fjBwAAAG5IxQ5AAwcO1Lx587R7924NGDBADz74YIF1N47y8PBQVFSUkpOT1a1bN0mS1WpVcnKyBg0aVOR+EydO1Kuvvqply5YpOjr6sp9n//79Onr0qIKCgq6qXgAAUDoUexH0tGnTdOjQIQ0bNkxffPGFQkND1atXLy1btuyqZoQGDx6sGTNm6KOPPtLWrVs1cOBAnTp1ynZVWL9+/TRixAhb/wkTJmjkyJH68MMPFRYWpoyMDGVkZOjkyZOSpJMnT+rFF1/U2rVrlZ6eruTkZHXt2lU1atRQ+/btr7hOAABQejh0FZinp6f69OmjFStWaMuWLapbt66eeuophYWF2QKIo3r37q1JkyYpPj5ekZGR2rhxo5KSkmwLo/fu3atDhw7Z+k+fPl3nzp1Tjx49FBQUZHtMmjRJkuTu7q5NmzapS5cuuuWWW/TII48oKipKq1ev5jQXAACQdBVXgbm5ucliscgwDOXl5V1VEYMGDSrylFdKSordx+np6Zccy9vbW8uWLbuqegAAQOnm0AxQbm6u5s2bp7vuuku33HKLNm/erHfeeUd79+6Vr69vSdUIAADgVMWeAXrqqaf06aefKjQ0VA8//LDmzZsnf3//kqwNAACgRBQ7ACUmJurmm29WRESEvv32W3377beF9lu0aJHTigMAACgJxQ5A/fr1k8ViKclaAAAArgmHboQIAABQGjj8ZqgAAAA3OgIQAAAwnSu+DxAAXM7p06clSRs2bHBxJX87c+aM0tPTFRYWJm9vb1eXI0naunWrq0sATIcABKDEbNu2TZL02GOPubiSG0P58uVdXQJgGgQgACUm/02Oa9WqpXLlyrm2mP9v69atevDBBzVnzhzVrl3b1eXYlC9fXjVr1nR1GYBpEIAAlBh/f389+uijri6jULVr11bDhg1dXQYAF2ERNAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMJ3rIgBNmzZNYWFh8vLyUkxMjH788cci+86YMUMtWrRQpUqVVKlSJcXGxhbobxiG4uPjFRQUJG9vb8XGxiotLa2kDwMAANwgXB6A5s+fr8GDB2vUqFHasGGDGjRooPbt2ysrK6vQ/ikpKerTp49WrVql1NRUhYaGql27djpw4ICtz8SJE/XWW28pMTFR69atk4+Pj9q3b6+zZ89eq8MCAADXMYthGIYrC4iJiVGjRo30zjvvSJKsVqtCQ0P1zDPPaPjw4ZfdPy8vT5UqVdI777yjfv36yTAMBQcHa8iQIRo6dKgkKTs7WwEBAZo1a5buv//+y46Zk5MjPz8/ZWdnq0KFCld3gACuKxs2bFBUVJTWr1+vhg0burocAE7kyN9vl84AnTt3TuvXr1dsbKytzc3NTbGxsUpNTS3WGKdPn9b58+dVuXJlSdLu3buVkZFhN6afn59iYmKKHDM3N1c5OTl2DwAAUHq5NAAdOXJEeXl5CggIsGsPCAhQRkZGscZ46aWXFBwcbAs8+fs5MmZCQoL8/Pxsj9DQUEcPBQAA3EBcvgboarz++uv69NNP9b///U9eXl5XPM6IESOUnZ1te+zbt8+JVQIAgOtNGVd+cn9/f7m7uyszM9OuPTMzU4GBgZfcd9KkSXr99de1cuVK1a9f39aev19mZqaCgoLsxoyMjCx0LE9PT3l6el7hUQAAgBuNS2eAPDw8FBUVpeTkZFub1WpVcnKymjZtWuR+EydO1Lhx45SUlKTo6Gi7beHh4QoMDLQbMycnR+vWrbvkmAAAwDxcOgMkSYMHD1ZcXJyio6PVuHFjTZ06VadOndKAAQMkSf369VNISIgSEhIkSRMmTFB8fLzmzp2rsLAw27oeX19f+fr6ymKx6Pnnn9f48eNVs2ZNhYeHa+TIkQoODla3bt1cdZgAAOA64vIA1Lt3bx0+fFjx8fHKyMhQZGSkkpKSbIuY9+7dKze3vyeqpk+frnPnzqlHjx5244waNUqjR4+WJA0bNkynTp3S448/ruPHj6t58+ZKSkq6qnVCAACg9HD5fYCuR9wHCCi9uA8QUHrdMPcBAgAAcAUCEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMJ0yri4AAC7n9OnT2rZtm1PG2rp1q92/zlCrVi2VK1fOaeMBKHkuD0DTpk3TG2+8oYyMDDVo0EBvv/22GjduXGjf33//XfHx8Vq/fr327NmjKVOm6Pnnn7frM3r0aI0ZM8au7dZbb3XaL08A1962bdsUFRXl1DEffPBBp421fv16NWzY0GnjASh5Lg1A8+fP1+DBg5WYmKiYmBhNnTpV7du31/bt21W1atUC/U+fPq2IiAj17NlTL7zwQpHj1q1bVytXrrR9XKaMy3MegKtQq1YtrV+/3iljnTlzRunp6QoLC5O3t7dTxqxVq5ZTxgFw7bg0GUyePFmPPfaYBgwYIElKTEzUl19+qQ8//FDDhw8v0L9Ro0Zq1KiRJBW6PV+ZMmUUGBhY7Dpyc3OVm5tr+zgnJ6fY+wIoeeXKlXPqDEuzZs2cNhaAG5PLFkGfO3dO69evV2xs7N/FuLkpNjZWqampVzV2WlqagoODFRERoQceeEB79+69ZP+EhAT5+fnZHqGhoVf1+QEAwPXNZQHoyJEjysvLU0BAgF17QECAMjIyrnjcmJgYzZo1S0lJSZo+fbp2796tFi1a6MSJE0XuM2LECGVnZ9se+/btu+LPDwAArn+lbnFMx44dbf+vX7++YmJiVK1aNf33v//VI488Uug+np6e8vT0vFYlAgAAF3PZDJC/v7/c3d2VmZlp156ZmenQ+p3LqVixom655Rbt3LnTaWMCAIAbm8sCkIeHh6KiopScnGxrs1qtSk5OVtOmTZ32eU6ePKldu3YpKCjIaWMCAIAbm0tPgQ0ePFhxcXGKjo5W48aNNXXqVJ06dcp2VVi/fv0UEhKihIQESX8tnN6yZYvt/wcOHNDGjRvl6+urGjVqSJKGDh2qzp07q1q1ajp48KBGjRold3d39enTxzUHCQAArjsuDUC9e/fW4cOHFR8fr4yMDEVGRiopKcm2MHrv3r1yc/t7kurgwYO6/fbbbR9PmjRJkyZNUqtWrZSSkiJJ2r9/v/r06aOjR4+qSpUqat68udauXasqVapc02MDAADXL4thGIari7je5OTkyM/PT9nZ2apQoYKrywEAAMXgyN9v3gwVAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYTql7KwxnyL8wjneFBwDgxpH/d7s4F7gTgAqR/8apvCs8AAA3nhMnTsjPz++SfbgPUCGsVqsOHjyo8uXLy2KxuLocAE6Uk5Oj0NBQ7du3j/t8AaWMYRg6ceKEgoOD7W6kXBgCEABT4UanACQWQQMAABMiAAEAANMhAAEwFU9PT40aNUqenp6uLgWAC7EGCAAAmA4zQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQABM4bvvvlPnzp0VHBwsi8WixYsXu7okAC5EAAJgCqdOnVKDBg00bdo0V5cC4DrAu8EDMIWOHTuqY8eOri4DwHWCGSAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6XAUGwBROnjypnTt32j7evXu3Nm7cqMqVK+vmm292YWUAXMFiGIbh6iIAoKSlpKSoTZs2Bdrj4uI0a9asa18QAJciAAEAANNhDRAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADCd/wcMuYQccKTNswAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def plot_boxplot(metrics, key, title, ylabel):\n",
+        "    values = [metrics[label][key] for label in metrics]\n",
+        "    plt.figure()\n",
+        "    plt.boxplot(values)\n",
+        "    plt.title(title)\n",
+        "    plt.ylabel(ylabel)\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_boxplot(metrics_original, 'percentile_50', 'Boxplot of Median Distances (Original Model)', 'Median Distance')\n",
+        "plot_boxplot(metrics_finetuned, 'percentile_50', 'Boxplot of Median Distances (Fine-tuned Model)', 'Median Distance')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "id": "tx0_WPHWiJy3",
+        "outputId": "3ca088a3-2ed4-4106-8fd3-26c4a1e8328e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def plot_histogram(metrics, title, xlabel):\n",
+        "    values = list(metrics.values()) # Extract values from the dictionary\n",
+        "    plt.figure()\n",
+        "    plt.hist(values, bins=20, alpha=0.7)\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('Frequency')\n",
+        "    plt.show()\n",
+        "\n",
+        "def compare_metrics(metrics_original, metrics_finetuned, key):\n",
+        "    changes = {}\n",
+        "    for label in metrics_original:\n",
+        "        if label in metrics_finetuned:\n",
+        "            change = metrics_finetuned[label][key] - metrics_original[label][key]\n",
+        "            changes[label] = change\n",
+        "    return changes\n",
+        "\n",
+        "max_radius_change = compare_metrics(metrics_original, metrics_finetuned, 'max_radius')\n",
+        "plot_histogram(max_radius_change, 'Histogram of Max Radius Change', 'Max Radius Change')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "id": "UMkNB6xviJy3"
+      },
+      "outputs": [],
+      "source": [
+        "def calculate_inter_cluster_metrics(embeddings):\n",
+        "    centers = {label: metrics['center'] for label, metrics in embeddings.items()}\n",
+        "    labels = list(centers.keys())\n",
+        "    inter_cluster_distances = euclidean_distances(list(centers.values()))\n",
+        "\n",
+        "    metrics = {}\n",
+        "    for i, label1 in enumerate(labels):\n",
+        "        for j, label2 in enumerate(labels):\n",
+        "            if i >= j:\n",
+        "                continue\n",
+        "            distance = inter_cluster_distances[i, j]\n",
+        "            pair = tuple(sorted((label1, label2)))\n",
+        "            metrics[pair] = distance\n",
+        "\n",
+        "    return metrics\n",
+        "\n",
+        "inter_cluster_metrics_original = calculate_inter_cluster_metrics(metrics_original)\n",
+        "inter_cluster_metrics_finetuned = calculate_inter_cluster_metrics(metrics_finetuned)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "id": "K9Und3BkiJy4",
+        "outputId": "3cde8556-4f99-46d4-fc00-5e0e4723d333",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def compare_inter_cluster_metrics(metrics_original, metrics_finetuned):\n",
+        "    changes = {}\n",
+        "    for pair in metrics_original:\n",
+        "        if pair in metrics_finetuned:\n",
+        "            change = metrics_finetuned[pair] - metrics_original[pair]\n",
+        "            changes[pair] = change\n",
+        "    return changes\n",
+        "\n",
+        "inter_cluster_distance_change = compare_inter_cluster_metrics(inter_cluster_metrics_original, inter_cluster_metrics_finetuned)\n",
+        "\n",
+        "def plot_inter_cluster_changes(inter_cluster_distance_change, title, xlabel):\n",
+        "    values = list(inter_cluster_distance_change.values())\n",
+        "    plt.figure()\n",
+        "    plt.hist(values, bins=20, alpha=0.7)\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('Frequency')\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_inter_cluster_changes(inter_cluster_distance_change, 'Histogram of Inter-Cluster Distance Changes', 'Inter-Cluster Distance Change')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QforM1ZSiJy4"
+      },
+      "source": [
+        "## Impact on faces"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "id": "EmxEp0iDiJy4",
+        "outputId": "c02bf828-3387-4df6-a568-2dd52c44a742",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Positively Impacted Faces: 90099\n",
+            "Negatively Impacted Faces: 0\n",
+            "Averagely Impacted Faces: 0\n"
+          ]
+        }
+      ],
+      "source": [
+        "def classify_impact(intra_metrics_original, intra_metrics_finetuned, inter_metrics_original, inter_metrics_finetuned):\n",
+        "    impact = {\n",
+        "        'positively_impacted': [],\n",
+        "        'negatively_impacted': [],\n",
+        "        'averagely_impacted': []\n",
+        "    }\n",
+        "\n",
+        "    # Define thresholds for considering a change as significant\n",
+        "    threshold = 0.01  # You can adjust this value based on your needs\n",
+        "\n",
+        "    # Classify intra-cluster metric changes\n",
+        "    for label in intra_metrics_original:\n",
+        "        if label in intra_metrics_finetuned:\n",
+        "            max_radius_change = intra_metrics_finetuned[label]['max_radius'] - intra_metrics_original[label]['max_radius']\n",
+        "            if max_radius_change < -threshold:\n",
+        "                impact['positively_impacted'].append((label, 'max_radius'))\n",
+        "            elif max_radius_change > threshold:\n",
+        "                impact['negatively_impacted'].append((label, 'max_radius'))\n",
+        "            else:\n",
+        "                impact['averagely_impacted'].append((label, 'max_radius'))\n",
+        "\n",
+        "            # Add other metrics here if needed\n",
+        "            # Example: variance, standard_deviation, etc.\n",
+        "            variance_change = intra_metrics_finetuned[label]['variance'] - intra_metrics_original[label]['variance']\n",
+        "            if variance_change < -threshold:\n",
+        "                impact['positively_impacted'].append((label, 'variance'))\n",
+        "            elif variance_change > threshold:\n",
+        "                impact['negatively_impacted'].append((label, 'variance'))\n",
+        "            else:\n",
+        "                impact['averagely_impacted'].append((label, 'variance'))\n",
+        "\n",
+        "    # Classify inter-cluster metric changes\n",
+        "    for pair in inter_metrics_original:\n",
+        "        if pair in inter_metrics_finetuned:\n",
+        "            distance_change = inter_metrics_finetuned[pair] - inter_metrics_original[pair]\n",
+        "            if distance_change < -threshold:\n",
+        "                impact['positively_impacted'].append((pair, 'inter_cluster_distance'))\n",
+        "            elif distance_change > threshold:\n",
+        "                impact['negatively_impacted'].append((pair, 'inter_cluster_distance'))\n",
+        "            else:\n",
+        "                impact['averagely_impacted'].append((pair, 'inter_cluster_distance'))\n",
+        "\n",
+        "    return impact\n",
+        "\n",
+        "# Assuming metrics_original and metrics_finetuned are dictionaries containing the calculated metrics\n",
+        "impact = classify_impact(metrics_original, metrics_finetuned, inter_cluster_metrics_original, inter_cluster_metrics_finetuned)\n",
+        "\n",
+        "# Print the impact classification\n",
+        "print(f\"Positively Impacted Faces: {len(impact['positively_impacted'])}\")\n",
+        "print(f\"Negatively Impacted Faces: {len(impact['negatively_impacted'])}\")\n",
+        "print(f\"Averagely Impacted Faces: {len(impact['averagely_impacted'])}\")\n",
+        "\n",
+        "# Optional: Print detailed impact for each face\n",
+        "# for category, changes in impact.items():\n",
+        "#     print(f\"\\n{category.capitalize()} Faces:\")\n",
+        "#     for change in changes:\n",
+        "#         print(change)\n"
+      ]
+    }
+  ],
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+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "3361f1363a274662bb683dad5c04484a": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Model-Training/Evaluated_model_try2_vs_try3.ipynb b/Model-Training/Evaluated_model_try2_vs_try3.ipynb
new file mode 100644
index 0000000..7b5d770
--- /dev/null
+++ b/Model-Training/Evaluated_model_try2_vs_try3.ipynb
@@ -0,0 +1,19153 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Devasy23/FaceRec/blob/feat%2FModel-evaluation/Model-Training/Evaluated_model_try2_vs_try3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7dpZF1CUCJjJ"
+      },
+      "source": [
+        "# Pre-setup\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ca5SxWfjCGjM",
+        "outputId": "0a8afad8-5fc1-4abf-870e-87fbf648f7ce"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting deepface\n",
+            "  Downloading deepface-0.0.92-py3-none-any.whl (105 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.5/105.5 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: requests>=2.27.1 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.31.0)\n",
+            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (1.25.2)\n",
+            "Requirement already satisfied: pandas>=0.23.4 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.0.3)\n",
+            "Requirement already satisfied: gdown>=3.10.1 in /usr/local/lib/python3.10/dist-packages (from deepface) (5.1.0)\n",
+            "Requirement already satisfied: tqdm>=4.30.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (4.66.4)\n",
+            "Requirement already satisfied: Pillow>=5.2.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (9.4.0)\n",
+            "Requirement already satisfied: opencv-python>=4.5.5.64 in /usr/local/lib/python3.10/dist-packages (from deepface) (4.8.0.76)\n",
+            "Requirement already satisfied: tensorflow>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.15.0)\n",
+            "Requirement already satisfied: keras>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.15.0)\n",
+            "Requirement already satisfied: Flask>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from deepface) (2.2.5)\n",
+            "Collecting mtcnn>=0.1.0 (from deepface)\n",
+            "  Downloading mtcnn-0.1.1-py3-none-any.whl (2.3 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting retina-face>=0.0.1 (from deepface)\n",
+            "  Downloading retina_face-0.0.17-py3-none-any.whl (25 kB)\n",
+            "Collecting fire>=0.4.0 (from deepface)\n",
+            "  Downloading fire-0.6.0.tar.gz (88 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.4/88.4 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting gunicorn>=20.1.0 (from deepface)\n",
+            "  Downloading gunicorn-22.0.0-py3-none-any.whl (84 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.4/84.4 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from fire>=0.4.0->deepface) (1.16.0)\n",
+            "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from fire>=0.4.0->deepface) (2.4.0)\n",
+            "Requirement already satisfied: Werkzeug>=2.2.2 in /usr/local/lib/python3.10/dist-packages (from Flask>=1.1.2->deepface) (3.0.3)\n",
+            "Requirement already satisfied: Jinja2>=3.0 in /usr/local/lib/python3.10/dist-packages (from Flask>=1.1.2->deepface) (3.1.4)\n",
+            "Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.10/dist-packages (from Flask>=1.1.2->deepface) (2.2.0)\n",
+            "Requirement already satisfied: click>=8.0 in /usr/local/lib/python3.10/dist-packages (from Flask>=1.1.2->deepface) (8.1.7)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown>=3.10.1->deepface) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown>=3.10.1->deepface) (3.15.4)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from gunicorn>=20.1.0->deepface) (24.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23.4->deepface) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23.4->deepface) (2023.4)\n",
+            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23.4->deepface) (2024.1)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (3.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (2024.7.4)\n",
+            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (1.4.0)\n",
+            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (1.6.3)\n",
+            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (24.3.25)\n",
+            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (0.6.0)\n",
+            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (0.2.0)\n",
+            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (3.9.0)\n",
+            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (18.1.1)\n",
+            "Requirement already satisfied: ml-dtypes~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (0.2.0)\n",
+            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (3.3.0)\n",
+            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (3.20.3)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (67.7.2)\n",
+            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (4.12.2)\n",
+            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (1.14.1)\n",
+            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (0.37.1)\n",
+            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (1.64.1)\n",
+            "Requirement already satisfied: tensorboard<2.16,>=2.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (2.15.2)\n",
+            "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.9.0->deepface) (2.15.0)\n",
+            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow>=1.9.0->deepface) (0.43.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=3.0->Flask>=1.1.2->deepface) (2.1.5)\n",
+            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (2.27.0)\n",
+            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (1.2.1)\n",
+            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (3.6)\n",
+            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (0.7.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown>=3.10.1->deepface) (2.5)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->deepface) (1.7.1)\n",
+            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (5.4.0)\n",
+            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (0.4.0)\n",
+            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (4.9)\n",
+            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (1.3.1)\n",
+            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (0.6.0)\n",
+            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (3.2.2)\n",
+            "Building wheels for collected packages: fire\n",
+            "  Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for fire: filename=fire-0.6.0-py2.py3-none-any.whl size=117029 sha256=67bc03a9e6bdcc6f65bb0b6581d1d41d76ee9413e12d009a71a4696a6b32e126\n",
+            "  Stored in directory: /root/.cache/pip/wheels/d6/6d/5d/5b73fa0f46d01a793713f8859201361e9e581ced8c75e5c6a3\n",
+            "Successfully built fire\n",
+            "Installing collected packages: gunicorn, fire, mtcnn, retina-face, deepface\n",
+            "Successfully installed deepface-0.0.92 fire-0.6.0 gunicorn-22.0.0 mtcnn-0.1.1 retina-face-0.0.17\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install deepface"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KxpbRTJDCGjU"
+      },
+      "source": [
+        "## Downloading Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NI6P1qt_h_hm",
+        "outputId": "dec9f11e-da1e-4019-b085-d7fb0ad763ad"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2024-07-20 05:17:12--  http://vis-www.cs.umass.edu/lfw/lfw.tgz\n",
+            "Resolving vis-www.cs.umass.edu (vis-www.cs.umass.edu)... 128.119.244.95\n",
+            "Connecting to vis-www.cs.umass.edu (vis-www.cs.umass.edu)|128.119.244.95|:80... failed: Connection timed out.\n",
+            "Retrying.\n",
+            "\n",
+            "--2024-07-20 05:19:24--  (try: 2)  http://vis-www.cs.umass.edu/lfw/lfw.tgz\n",
+            "Connecting to vis-www.cs.umass.edu (vis-www.cs.umass.edu)|128.119.244.95|:80... failed: Connection timed out.\n",
+            "Retrying.\n",
+            "\n",
+            "--2024-07-20 05:21:36--  (try: 3)  http://vis-www.cs.umass.edu/lfw/lfw.tgz\n",
+            "Connecting to vis-www.cs.umass.edu (vis-www.cs.umass.edu)|128.119.244.95|:80... failed: Connection timed out.\n",
+            "Retrying.\n",
+            "\n",
+            "--2024-07-20 05:23:48--  (try: 4)  http://vis-www.cs.umass.edu/lfw/lfw.tgz\n",
+            "Connecting to vis-www.cs.umass.edu (vis-www.cs.umass.edu)|128.119.244.95|:80... failed: Connection timed out.\n",
+            "Retrying.\n",
+            "\n",
+            "--2024-07-20 05:26:04--  (try: 5)  http://vis-www.cs.umass.edu/lfw/lfw.tgz\n",
+            "Connecting to vis-www.cs.umass.edu (vis-www.cs.umass.edu)|128.119.244.95|:80... ^C\n"
+          ]
+        }
+      ],
+      "source": [
+        "!wget http://vis-www.cs.umass.edu/lfw/lfw.tgz"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JgJ3e_jmh_X3",
+        "outputId": "d73c3143-1b81-409b-cfe8-57118ccee89a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
+            "lfw/Luis_Sanchez/\n",
+            "lfw/Luis_Sanchez/Luis_Sanchez_0001.jpg\n",
+            "lfw/Juliette_Binoche/\n",
+            "lfw/Juliette_Binoche/Juliette_Binoche_0001.jpg\n",
+            "lfw/Joel_Todd/\n",
+            "lfw/Joel_Todd/Joel_Todd_0001.jpg\n",
+            "lfw/Allan_Wagner/\n",
+            "lfw/Allan_Wagner/Allan_Wagner_0001.jpg\n",
+            "lfw/Alejandro_Fernandez/\n",
+            "lfw/Alejandro_Fernandez/Alejandro_Fernandez_0001.jpg\n",
+            "lfw/Guillaume_Depardieu/\n",
+            "lfw/Guillaume_Depardieu/Guillaume_Depardieu_0001.jpg\n",
+            "lfw/Delphine_Chuillot/\n",
+            "lfw/Delphine_Chuillot/Delphine_Chuillot_0001.jpg\n",
+            "lfw/Paul_LeClerc/\n",
+            "lfw/Paul_LeClerc/Paul_LeClerc_0001.jpg\n",
+            "lfw/Eddie_Jordan/\n",
+            "lfw/Eddie_Jordan/Eddie_Jordan_0001.jpg\n",
+            "lfw/Lionel_Richie/\n",
+            "lfw/Lionel_Richie/Lionel_Richie_0001.jpg\n",
+            "lfw/Lionel_Richie/Lionel_Richie_0002.jpg\n",
+            "lfw/Jose_Viegas_Filho/\n",
+            "lfw/Jose_Viegas_Filho/Jose_Viegas_Filho_0001.jpg\n",
+            "lfw/Jose_Viegas_Filho/Jose_Viegas_Filho_0002.jpg\n",
+            "lfw/Roger_Mahony/\n",
+            "lfw/Roger_Mahony/Roger_Mahony_0001.jpg\n",
+            "lfw/Clay_Aiken/\n",
+            "lfw/Clay_Aiken/Clay_Aiken_0001.jpg\n",
+            "lfw/Clay_Aiken/Clay_Aiken_0002.jpg\n",
+            "lfw/Clay_Aiken/Clay_Aiken_0003.jpg\n",
+            "lfw/Clay_Aiken/Clay_Aiken_0004.jpg\n",
+            "lfw/Clay_Aiken/Clay_Aiken_0005.jpg\n",
+            "lfw/Clay_Aiken/Clay_Aiken_0006.jpg\n",
+            "lfw/Edward_Egan/\n",
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+            "lfw/Hector_Babenco/Hector_Babenco_0001.jpg\n",
+            "lfw/Hector_Babenco/Hector_Babenco_0002.jpg\n",
+            "lfw/Hector_Babenco/Hector_Babenco_0003.jpg\n",
+            "lfw/Maria_Luisa_Mendonca/\n",
+            "lfw/Maria_Luisa_Mendonca/Maria_Luisa_Mendonca_0001.jpg\n",
+            "lfw/Maria_Luisa_Mendonca/Maria_Luisa_Mendonca_0002.jpg\n",
+            "lfw/Caio_Blat/\n",
+            "lfw/Caio_Blat/Caio_Blat_0001.jpg\n",
+            "lfw/Jodie_Kidd/\n",
+            "lfw/Jodie_Kidd/Jodie_Kidd_0001.jpg\n",
+            "lfw/Andy_Rooney/\n",
+            "lfw/Andy_Rooney/Andy_Rooney_0001.jpg\n",
+            "lfw/Pascal_Rheaume/\n",
+            "lfw/Pascal_Rheaume/Pascal_Rheaume_0001.jpg\n",
+            "lfw/Amber_Frey/\n",
+            "lfw/Amber_Frey/Amber_Frey_0001.jpg\n",
+            "lfw/Stellan_Skarsgard/\n",
+            "lfw/Stellan_Skarsgard/Stellan_Skarsgard_0001.jpg\n",
+            "lfw/Stellan_Skarsgard/Stellan_Skarsgard_0002.jpg\n",
+            "lfw/Faye_Alibocus/\n",
+            "lfw/Faye_Alibocus/Faye_Alibocus_0001.jpg\n",
+            "lfw/Joshua_Davey/\n",
+            "lfw/Joshua_Davey/Joshua_Davey_0001.jpg\n",
+            "lfw/Barbara_Bach/\n",
+            "lfw/Barbara_Bach/Barbara_Bach_0001.jpg\n",
+            "lfw/Ringo_Starr/\n",
+            "lfw/Ringo_Starr/Ringo_Starr_0001.jpg\n",
+            "lfw/Bernice_Wong/\n",
+            "lfw/Bernice_Wong/Bernice_Wong_0001.jpg\n",
+            "lfw/Roger_Lyons/\n",
+            "lfw/Roger_Lyons/Roger_Lyons_0001.jpg\n",
+            "lfw/Na_Na_Keum/\n",
+            "lfw/Na_Na_Keum/Na_Na_Keum_0001.jpg\n",
+            "lfw/Alexandra_Vodjanikova/\n",
+            "lfw/Alexandra_Vodjanikova/Alexandra_Vodjanikova_0001.jpg\n",
+            "lfw/Alexandra_Vodjanikova/Alexandra_Vodjanikova_0002.jpg\n",
+            "lfw/Yannos_Papantoniou/\n",
+            "lfw/Yannos_Papantoniou/Yannos_Papantoniou_0001.jpg\n",
+            "lfw/Sanja_Papic/\n",
+            "lfw/Sanja_Papic/Sanja_Papic_0001.jpg\n",
+            "lfw/Paul_Walker/\n",
+            "lfw/Paul_Walker/Paul_Walker_0001.jpg\n",
+            "lfw/Jason_Sehorn/\n",
+            "lfw/Jason_Sehorn/Jason_Sehorn_0001.jpg\n",
+            "lfw/Darko_Milicic/\n",
+            "lfw/Darko_Milicic/Darko_Milicic_0001.jpg\n",
+            "lfw/Katerina_Smrzova/\n",
+            "lfw/Katerina_Smrzova/Katerina_Smrzova_0001.jpg\n",
+            "lfw/David_Carradine/\n",
+            "lfw/David_Carradine/David_Carradine_0001.jpg\n",
+            "lfw/Coco_dEste/\n",
+            "lfw/Coco_dEste/Coco_dEste_0001.jpg\n",
+            "lfw/Rene_Antonio_Leon_Rodriguez/\n",
+            "lfw/Rene_Antonio_Leon_Rodriguez/Rene_Antonio_Leon_Rodriguez_0001.jpg\n",
+            "lfw/Carlos_Quintanilla_Schmidt/\n",
+            "lfw/Carlos_Quintanilla_Schmidt/Carlos_Quintanilla_Schmidt_0001.jpg\n",
+            "lfw/Carlos_Quintanilla_Schmidt/Carlos_Quintanilla_Schmidt_0002.jpg\n",
+            "lfw/Bill_Parsons/\n",
+            "lfw/Bill_Parsons/Bill_Parsons_0001.jpg\n",
+            "lfw/Danny_Ainge/\n",
+            "lfw/Danny_Ainge/Danny_Ainge_0001.jpg\n",
+            "lfw/Brad_Alexander_Smith/\n",
+            "lfw/Brad_Alexander_Smith/Brad_Alexander_Smith_0001.jpg\n",
+            "lfw/Alex_Zanardi/\n",
+            "lfw/Alex_Zanardi/Alex_Zanardi_0001.jpg\n",
+            "lfw/Steve_Avery/\n",
+            "lfw/Steve_Avery/Steve_Avery_0001.jpg\n",
+            "lfw/Matt_Anderson/\n",
+            "lfw/Matt_Anderson/Matt_Anderson_0001.jpg\n",
+            "lfw/Richard_Jefferson/\n",
+            "lfw/Richard_Jefferson/Richard_Jefferson_0001.jpg\n",
+            "lfw/Leon_Barmore/\n",
+            "lfw/Leon_Barmore/Leon_Barmore_0001.jpg\n",
+            "lfw/Jose_Luis_Santiago_Vasconcelos/\n",
+            "lfw/Jose_Luis_Santiago_Vasconcelos/Jose_Luis_Santiago_Vasconcelos_0001.jpg\n",
+            "lfw/Mary_Matalin/\n",
+            "lfw/Mary_Matalin/Mary_Matalin_0001.jpg\n",
+            "lfw/David_Ho/\n",
+            "lfw/David_Ho/David_Ho_0001.jpg\n",
+            "lfw/Thomas_Ulrich/\n",
+            "lfw/Thomas_Ulrich/Thomas_Ulrich_0001.jpg\n",
+            "lfw/Graciano_Rocchigiani/\n",
+            "lfw/Graciano_Rocchigiani/Graciano_Rocchigiani_0001.jpg\n",
+            "lfw/John_Moe/\n",
+            "lfw/John_Moe/John_Moe_0001.jpg\n",
+            "lfw/Tora_Takagi/\n",
+            "lfw/Tora_Takagi/Tora_Takagi_0001.jpg\n",
+            "lfw/Chris_Neil/\n",
+            "lfw/Chris_Neil/Chris_Neil_0001.jpg\n",
+            "lfw/Barbara_Bodine/\n",
+            "lfw/Barbara_Bodine/Barbara_Bodine_0001.jpg\n",
+            "lfw/Rolf_Eckrodt/\n",
+            "lfw/Rolf_Eckrodt/Rolf_Eckrodt_0001.jpg\n",
+            "lfw/Rolf_Eckrodt/Rolf_Eckrodt_0002.jpg\n",
+            "lfw/Carol_Niedermayer/\n",
+            "lfw/Carol_Niedermayer/Carol_Niedermayer_0001.jpg\n",
+            "lfw/Martha_Sahagun_de_Fox/\n",
+            "lfw/Martha_Sahagun_de_Fox/Martha_Sahagun_de_Fox_0001.jpg\n",
+            "lfw/Armando_Calderon_Sol/\n",
+            "lfw/Armando_Calderon_Sol/Armando_Calderon_Sol_0001.jpg\n",
+            "lfw/Terunobu_Maeda/\n",
+            "lfw/Terunobu_Maeda/Terunobu_Maeda_0001.jpg\n",
+            "lfw/Alex_Corretja/\n",
+            "lfw/Alex_Corretja/Alex_Corretja_0001.jpg\n",
+            "lfw/Elena_Tihomirova/\n",
+            "lfw/Elena_Tihomirova/Elena_Tihomirova_0001.jpg\n",
+            "lfw/Emmy_Rossum/\n",
+            "lfw/Emmy_Rossum/Emmy_Rossum_0001.jpg\n",
+            "lfw/Andrea_Yates/\n",
+            "lfw/Andrea_Yates/Andrea_Yates_0001.jpg\n",
+            "lfw/Laura_Romero/\n",
+            "lfw/Laura_Romero/Laura_Romero_0001.jpg\n",
+            "lfw/Szu_Yu_Chen/\n",
+            "lfw/Szu_Yu_Chen/Szu_Yu_Chen_0001.jpg\n",
+            "lfw/Rudy_Tomjanovich/\n",
+            "lfw/Rudy_Tomjanovich/Rudy_Tomjanovich_0001.jpg\n",
+            "lfw/Norman_Mineta/\n",
+            "lfw/Norman_Mineta/Norman_Mineta_0001.jpg\n",
+            "lfw/Michael_Killeen/\n",
+            "lfw/Michael_Killeen/Michael_Killeen_0001.jpg\n",
+            "lfw/Kristen_Rivera/\n",
+            "lfw/Kristen_Rivera/Kristen_Rivera_0001.jpg\n",
+            "lfw/Alicia_Hollowell/\n",
+            "lfw/Alicia_Hollowell/Alicia_Hollowell_0001.jpg\n",
+            "lfw/Peter_Hollingworth/\n",
+            "lfw/Peter_Hollingworth/Peter_Hollingworth_0001.jpg\n",
+            "lfw/Ashlea_Talbot/\n",
+            "lfw/Ashlea_Talbot/Ashlea_Talbot_0001.jpg\n",
+            "lfw/Christine_Rau/\n",
+            "lfw/Christine_Rau/Christine_Rau_0001.jpg\n",
+            "lfw/Wei_Wu/\n",
+            "lfw/Wei_Wu/Wei_Wu_0001.jpg\n",
+            "lfw/Eugene_Melnyk/\n",
+            "lfw/Eugene_Melnyk/Eugene_Melnyk_0001.jpg\n",
+            "lfw/Jim_Paxson/\n",
+            "lfw/Jim_Paxson/Jim_Paxson_0001.jpg\n",
+            "lfw/Annie_Chaplin/\n",
+            "lfw/Annie_Chaplin/Annie_Chaplin_0001.jpg\n",
+            "lfw/Jeanne_Moreau/\n",
+            "lfw/Jeanne_Moreau/Jeanne_Moreau_0001.jpg\n",
+            "lfw/Jeanne_Moreau/Jeanne_Moreau_0002.jpg\n",
+            "lfw/Ornella_Muti/\n",
+            "lfw/Ornella_Muti/Ornella_Muti_0001.jpg\n",
+            "lfw/Yuri_Luzhkov/\n",
+            "lfw/Yuri_Luzhkov/Yuri_Luzhkov_0001.jpg\n",
+            "lfw/Mark_Butcher/\n",
+            "lfw/Mark_Butcher/Mark_Butcher_0001.jpg\n",
+            "lfw/Michelle_Lecky/\n",
+            "lfw/Michelle_Lecky/Michelle_Lecky_0001.jpg\n",
+            "lfw/Tina_Pisnik/\n",
+            "lfw/Tina_Pisnik/Tina_Pisnik_0001.jpg\n",
+            "lfw/Jeffrey_Katzenberg/\n",
+            "lfw/Jeffrey_Katzenberg/Jeffrey_Katzenberg_0001.jpg\n",
+            "lfw/Andrew_Bunner/\n",
+            "lfw/Andrew_Bunner/Andrew_Bunner_0001.jpg\n",
+            "lfw/Andrew_Bunner/Andrew_Bunner_0002.jpg\n",
+            "lfw/David_Welch/\n",
+            "lfw/David_Welch/David_Welch_0001.jpg\n",
+            "lfw/Jane_Rooney/\n",
+            "lfw/Jane_Rooney/Jane_Rooney_0001.jpg\n",
+            "lfw/Mickey_Rooney/\n",
+            "lfw/Mickey_Rooney/Mickey_Rooney_0001.jpg\n",
+            "lfw/Peter_Hunt/\n",
+            "lfw/Peter_Hunt/Peter_Hunt_0001.jpg\n",
+            "lfw/Francis_Crick/\n",
+            "lfw/Francis_Crick/Francis_Crick_0001.jpg\n",
+            "lfw/Kay_Behrensmeyer/\n",
+            "lfw/Kay_Behrensmeyer/Kay_Behrensmeyer_0001.jpg\n",
+            "lfw/Gala_Leon_Garcia/\n",
+            "lfw/Gala_Leon_Garcia/Gala_Leon_Garcia_0001.jpg\n",
+            "lfw/Wanda_de_la_Jesus/\n",
+            "lfw/Wanda_de_la_Jesus/Wanda_de_la_Jesus_0001.jpg\n",
+            "lfw/Jimmy_Smits/\n",
+            "lfw/Jimmy_Smits/Jimmy_Smits_0001.jpg\n",
+            "lfw/Jane_Leeves/\n",
+            "lfw/Jane_Leeves/Jane_Leeves_0001.jpg\n",
+            "lfw/Kelsey_Grammer/\n",
+            "lfw/Kelsey_Grammer/Kelsey_Grammer_0001.jpg\n",
+            "lfw/Donna_Ralston/\n",
+            "lfw/Donna_Ralston/Donna_Ralston_0001.jpg\n",
+            "lfw/Larry_Ralston/\n",
+            "lfw/Larry_Ralston/Larry_Ralston_0001.jpg\n",
+            "lfw/Barry_Diller/\n",
+            "lfw/Barry_Diller/Barry_Diller_0001.jpg\n",
+            "lfw/Dominique_Perben/\n",
+            "lfw/Dominique_Perben/Dominique_Perben_0001.jpg\n",
+            "lfw/Peri_Gilpin/\n",
+            "lfw/Peri_Gilpin/Peri_Gilpin_0001.jpg\n",
+            "lfw/David_Bowie/\n",
+            "lfw/David_Bowie/David_Bowie_0001.jpg\n",
+            "lfw/Elizabeth_Regan/\n",
+            "lfw/Elizabeth_Regan/Elizabeth_Regan_0001.jpg\n",
+            "lfw/Whoopi_Goldberg/\n",
+            "lfw/Whoopi_Goldberg/Whoopi_Goldberg_0001.jpg\n",
+            "lfw/Dany_Heatley/\n",
+            "lfw/Dany_Heatley/Dany_Heatley_0001.jpg\n",
+            "lfw/Chris_Kolanas/\n",
+            "lfw/Chris_Kolanas/Chris_Kolanas_0001.jpg\n",
+            "lfw/Chuck_Hagel/\n",
+            "lfw/Chuck_Hagel/Chuck_Hagel_0001.jpg\n",
+            "lfw/Jamir_Miller/\n",
+            "lfw/Jamir_Miller/Jamir_Miller_0001.jpg\n",
+            "lfw/Tatiana_Kennedy_Schlossberg/\n",
+            "lfw/Tatiana_Kennedy_Schlossberg/Tatiana_Kennedy_Schlossberg_0001.jpg\n",
+            "lfw/James_Smith/\n",
+            "lfw/James_Smith/James_Smith_0001.jpg\n",
+            "lfw/James_Smith/James_Smith_0002.jpg\n",
+            "lfw/Doug_Christie/\n",
+            "lfw/Doug_Christie/Doug_Christie_0001.jpg\n",
+            "lfw/Desiree_McKenzie/\n",
+            "lfw/Desiree_McKenzie/Desiree_McKenzie_0001.jpg\n",
+            "lfw/Dan_Prinster/\n",
+            "lfw/Dan_Prinster/Dan_Prinster_0001.jpg\n",
+            "lfw/Henning_Scherf/\n",
+            "lfw/Henning_Scherf/Henning_Scherf_0001.jpg\n",
+            "lfw/Kang_Gum-sil/\n",
+            "lfw/Kang_Gum-sil/Kang_Gum-sil_0001.jpg\n",
+            "lfw/Denys_Arcand/\n",
+            "lfw/Denys_Arcand/Denys_Arcand_0001.jpg\n",
+            "lfw/Dwayne_Williams/\n",
+            "lfw/Dwayne_Williams/Dwayne_Williams_0001.jpg\n",
+            "lfw/Se_Hyuk_Joo/\n",
+            "lfw/Se_Hyuk_Joo/Se_Hyuk_Joo_0001.jpg\n",
+            "lfw/Daniel_Scioli/\n",
+            "lfw/Daniel_Scioli/Daniel_Scioli_0001.jpg\n",
+            "lfw/Marie-Josee_Croze/\n",
+            "lfw/Marie-Josee_Croze/Marie-Josee_Croze_0001.jpg\n",
+            "lfw/Rachel_Kempson/\n",
+            "lfw/Rachel_Kempson/Rachel_Kempson_0001.jpg\n",
+            "lfw/Tony_Elias/\n",
+            "lfw/Tony_Elias/Tony_Elias_0001.jpg\n",
+            "lfw/Wang_Nan/\n",
+            "lfw/Wang_Nan/Wang_Nan_0001.jpg\n",
+            "lfw/Gus_Van_Sant/\n",
+            "lfw/Gus_Van_Sant/Gus_Van_Sant_0001.jpg\n",
+            "lfw/Gus_Van_Sant/Gus_Van_Sant_0002.jpg\n",
+            "lfw/Gus_Van_Sant/Gus_Van_Sant_0003.jpg\n",
+            "lfw/Werner_Schlager/\n",
+            "lfw/Werner_Schlager/Werner_Schlager_0001.jpg\n",
+            "lfw/Nan_Wang/\n",
+            "lfw/Nan_Wang/Nan_Wang_0001.jpg\n",
+            "lfw/Nan_Wang/Nan_Wang_0002.jpg\n",
+            "lfw/Nan_Wang/Nan_Wang_0003.jpg\n",
+            "lfw/Nan_Wang/Nan_Wang_0004.jpg\n",
+            "lfw/Helio_Castroneves/\n",
+            "lfw/Helio_Castroneves/Helio_Castroneves_0001.jpg\n",
+            "lfw/Peter_Greenaway/\n",
+            "lfw/Peter_Greenaway/Peter_Greenaway_0001.jpg\n",
+            "lfw/Peter_Greenaway/Peter_Greenaway_0002.jpg\n",
+            "lfw/Eva_Amurri/\n",
+            "lfw/Eva_Amurri/Eva_Amurri_0001.jpg\n",
+            "lfw/Hugh_Carey/\n",
+            "lfw/Hugh_Carey/Hugh_Carey_0001.jpg\n",
+            "lfw/Hal_Sellers/\n",
+            "lfw/Hal_Sellers/Hal_Sellers_0001.jpg\n",
+            "lfw/Valentina_Cervi/\n",
+            "lfw/Valentina_Cervi/Valentina_Cervi_0001.jpg\n",
+            "lfw/Diana_Taylor/\n",
+            "lfw/Diana_Taylor/Diana_Taylor_0001.jpg\n",
+            "lfw/Hermogenes_Ebdane_Jr/\n",
+            "lfw/Hermogenes_Ebdane_Jr/Hermogenes_Ebdane_Jr_0001.jpg\n",
+            "lfw/Steven_Tyler/\n",
+            "lfw/Steven_Tyler/Steven_Tyler_0001.jpg\n",
+            "lfw/Patricia_Medina/\n",
+            "lfw/Patricia_Medina/Patricia_Medina_0001.jpg\n",
+            "lfw/Jean-Marc_Olive/\n",
+            "lfw/Jean-Marc_Olive/Jean-Marc_Olive_0001.jpg\n",
+            "lfw/Antonio_Cassano/\n",
+            "lfw/Antonio_Cassano/Antonio_Cassano_0001.jpg\n",
+            "lfw/Jose_Santos/\n",
+            "lfw/Jose_Santos/Jose_Santos_0001.jpg\n",
+            "lfw/Dianne_Reeves/\n",
+            "lfw/Dianne_Reeves/Dianne_Reeves_0001.jpg\n",
+            "lfw/Prince_Felipe/\n",
+            "lfw/Prince_Felipe/Prince_Felipe_0001.jpg\n",
+            "lfw/Infanta_Cristina/\n",
+            "lfw/Infanta_Cristina/Infanta_Cristina_0001.jpg\n",
+            "lfw/Cuba_Gooding_Jr/\n",
+            "lfw/Cuba_Gooding_Jr/Cuba_Gooding_Jr_0001.jpg\n",
+            "lfw/Glenn_Rivers/\n",
+            "lfw/Glenn_Rivers/Glenn_Rivers_0001.jpg\n",
+            "lfw/Dino_de_Laurentis/\n",
+            "lfw/Dino_de_Laurentis/Dino_de_Laurentis_0001.jpg\n",
+            "lfw/Dino_de_Laurentis/Dino_de_Laurentis_0002.jpg\n",
+            "lfw/Francis_Ricciardone/\n",
+            "lfw/Francis_Ricciardone/Francis_Ricciardone_0001.jpg\n",
+            "lfw/Leticia_Dolera/\n",
+            "lfw/Leticia_Dolera/Leticia_Dolera_0001.jpg\n",
+            "lfw/Joseph_Fiennes/\n",
+            "lfw/Joseph_Fiennes/Joseph_Fiennes_0001.jpg\n",
+            "lfw/Colin_Prescot/\n",
+            "lfw/Colin_Prescot/Colin_Prescot_0001.jpg\n"
+          ]
+        }
+      ],
+      "source": [
+        "!tar -xzvf /content/drive/MyDrive/lfw.tgz"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1X2EFr3WiOKo",
+        "outputId": "f332fad9-1f65-4e52-dfc6-49368acd687e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Removed 5326 folders\n",
+            "Remaining folders: 423\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "import shutil\n",
+        "count=0\n",
+        "for folder in os.listdir('lfw'):\n",
+        "    if len(os.listdir(f'lfw/{folder}')) < 5:\n",
+        "        shutil.rmtree(f'lfw/{folder}')\n",
+        "        count+=1\n",
+        "print(f\"Removed {count} folders\")\n",
+        "print(f\"Remaining folders: {len(os.listdir('lfw'))}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pz1vtNniCGjf"
+      },
+      "source": [
+        "## Downloading Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 384,
+          "referenced_widgets": [
+            "05b31ee914704cf6aa23b016e9605fa6",
+            "626e68b358004c4e92eef018313f703f",
+            "0a2e149c631d4c0c9723e3dc20cef824",
+            "3f149795afdc4f72bfd4819c2dd24e7e",
+            "5acf74fda9cd44399872a32f7e797d53",
+            "b77e47d27983498a9d5cc1b8d7a6f0a6",
+            "b37c1463ede74729998741f9ca74b9a0",
+            "09f275e257dc4f35887c189de0d0eeae",
+            "a83437e262784123ad7ab7356de9f4ad",
+            "b831e7f5cb914676b1579fc31e0466f5",
+            "b58b054a34ac4cfdb06561b956329fdb",
+            "91d1a5c7baca43f09729be8b10c0fac9",
+            "a7c63853647c43c9a98fb2c34229c25b",
+            "f32b27d0a22f442484533f98a7de6f42",
+            "e6e30d397e8d462cbfb01d22878b212e",
+            "55654d4205474d72a91581407194d839",
+            "d2ee1ee53b10436e99cd3cc7c6c23c1e",
+            "56026f29dfe44623b29bea01eae5a9f3",
+            "62e3046887594c5a8492daf015dfa1ea",
+            "1941abf3e1414fb0b7651cadee353a7c",
+            "3810d05f97ab46d2a7febde0ff055e2f",
+            "2185e42006c94abaa2d33786851bfa4d",
+            "d61f94c1b2e24bfb8bd491a7ad9869f1",
+            "71679224e9d841d1938df73a32764c47",
+            "476c1471d9fc4c7dad180d3ce7d5a081",
+            "522947b5b08940b789cf05f354272787",
+            "3831c6a3d72c4084ab38e755137d94c9",
+            "38049d813cfc4ef8af57584efa87578b",
+            "60df876c4d8742ff959ea20b8b509cdb",
+            "359b1bc569834267a81037494578c1bd",
+            "7430967a5b0f47e28715bc6d720f946d",
+            "b21c359b208448a298daf97c9371d6ab",
+            "0ceee2cb7916489ea96520ce488598a1",
+            "5e1636ca1090486cad4cf157c618e986",
+            "532a0bbeace044a792951905c6848e75",
+            "0d15f4bdda034d8ab4349fc3ec35eb55",
+            "bc3ef7720305454f9908fc32e48b529c",
+            "b7f0ca5b9f9b42ddb387aeb9271d80b2",
+            "21224c28636a40518f0f4e182080ae2b",
+            "8c5ddf1e3ec0415cb8b699fe967b347a",
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+          ]
+        },
+        "id": "BzMfQwalCGji",
+        "outputId": "73e0b9b9-4580-4ee7-dc47-d3e4c052001a"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "05b31ee914704cf6aa23b016e9605fa6",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Fetching 6 files:   0%|          | 0/6 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "91d1a5c7baca43f09729be8b10c0fac9",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "variables/variables.index:   0%|          | 0.00/61.2k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d61f94c1b2e24bfb8bd491a7ad9869f1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              ".gitattributes:   0%|          | 0.00/1.59k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5e1636ca1090486cad4cf157c618e986",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "saved_model.pb:   0%|          | 0.00/18.1M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1e74eb5ac4c144b9996faecafdb8c77d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "keras_metadata.pb:   0%|          | 0.00/2.52M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d9999a65dfac41d1b9fc79fdcfd53bde",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "fingerprint.pb:   0%|          | 0.00/56.0 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1037e6dd39494d96842994d401595ae7",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "variables.data-00000-of-00001:   0%|          | 0.00/282M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'/content/model'"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "finetuned_model_dir = \"model\"\n",
+        "\n",
+        "from huggingface_hub import snapshot_download\n",
+        "snapshot_download(repo_id='DnD11/FaceNet_Finetuned', repo_type=\"model\", local_dir=finetuned_model_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mlEhvK1rh_IX"
+      },
+      "source": [
+        "# Evaluation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {
+        "id": "v5_9g5wwh0oi"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import numpy as np\n",
+        "from tqdm import tqdm_notebook\n",
+        "from keras.models import load_model\n",
+        "from keras.preprocessing import image\n",
+        "from sklearn.metrics.pairwise import euclidean_distances\n",
+        "from deepface import DeepFace\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "# Function to load and preprocess images\n",
+        "def load_and_preprocess_image(img_path, target_size=(160, 160)):\n",
+        "    img = image.load_img(img_path, target_size=target_size)\n",
+        "    img_array = image.img_to_array(img)\n",
+        "    img_array = np.expand_dims(img_array, axis=0)\n",
+        "    img_array /= 255.0\n",
+        "    return img_array\n",
+        "\n",
+        "# Function to generate embeddings\n",
+        "def generate_embeddings(model, dataset_path, deepface = False):\n",
+        "    embeddings = {}\n",
+        "\n",
+        "    for class_name in tqdm_notebook(os.listdir(dataset_path)):\n",
+        "        class_path = os.path.join(dataset_path, class_name)\n",
+        "        if not os.path.isdir(class_path):\n",
+        "            continue\n",
+        "        embeddings[class_name] = []\n",
+        "\n",
+        "        for img_name in os.listdir(class_path):\n",
+        "            img_path = os.path.join(class_path, img_name)\n",
+        "            img_array = load_and_preprocess_image(img_path)\n",
+        "            if deepface:\n",
+        "                embedding = DeepFace.represent(img_path=img_path, model_name = \"Facenet512\", enforce_detection=False, detector_backend=\"skip\")[0]['embedding']\n",
+        "            else:\n",
+        "                embedding = model.predict(img_array)\n",
+        "            embeddings[class_name].append(embedding)\n",
+        "        embeddings[class_name] = np.array(embeddings[class_name])\n",
+        "\n",
+        "\n",
+        "\n",
+        "    return embeddings\n",
+        "\n",
+        "# Function to calculate intra-cluster distances\n",
+        "def calculate_intra_cluster_metrics(embeddings):\n",
+        "    metrics = {}\n",
+        "\n",
+        "    for label, cluster_embeddings in embeddings.items():\n",
+        "        cluster_embeddings = np.squeeze(cluster_embeddings, axis=1)\n",
+        "\n",
+        "        avg_embedding = np.mean(cluster_embeddings, axis=0)\n",
+        "        distances = euclidean_distances(cluster_embeddings, [avg_embedding])\n",
+        "        max_distance = np.max(distances)\n",
+        "        min_distance = np.min(distances)\n",
+        "        variance = np.var(distances)\n",
+        "        standard_deviation = np.std(distances)\n",
+        "        percentile_25 = np.percentile(distances, 25)\n",
+        "        percentile_50 = np.percentile(distances, 50)\n",
+        "        percentile_75 = np.percentile(distances, 75)\n",
+        "\n",
+        "        metrics[label] = {\"center\": avg_embedding, \"max_radius\": max_distance, \"min_radius\":min_distance, \"num_samples\": len(cluster_embeddings), \"variance\": variance, \"standard_deviation\": standard_deviation, \"percentile_25\": percentile_25, \"percentile_50\": percentile_50, \"percentile_75\": percentile_75}\n",
+        "\n",
+        "    return metrics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!wget https://huggingface.co/DnD11/FaceNet_Finetuned/resolve/main/embedding_trial3.h5"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jazKxH07kWza",
+        "outputId": "c5163b81-d1ab-44b7-be85-fd8aff4df3e0"
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2024-07-20 06:43:23--  https://huggingface.co/DnD11/FaceNet_Finetuned/resolve/main/embedding_trial3.h5\n",
+            "Resolving huggingface.co (huggingface.co)... 18.239.69.83, 18.239.69.50, 18.239.69.31, ...\n",
+            "Connecting to huggingface.co (huggingface.co)|18.239.69.83|:443... connected.\n",
+            "HTTP request sent, awaiting response... 302 Found\n",
+            "Location: https://cdn-lfs-us-1.huggingface.co/repos/03/4f/034f80180b5ba08a0d9fe97aa18e17eb02604e9ba5c3e985eaee5a613e4810f6/2c4295909be34cae21ff3eacd9ae88bc875395030fc4d76fb2865f1e33135177?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27embedding_trial3.h5%3B+filename%3D%22embedding_trial3.h5%22%3B&Expires=1721717003&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyMTcxNzAwM319LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzAzLzRmLzAzNGY4MDE4MGI1YmEwOGEwZDlmZTk3YWExOGUxN2ViMDI2MDRlOWJhNWMzZTk4NWVhZWU1YTYxM2U0ODEwZjYvMmM0Mjk1OTA5YmUzNGNhZTIxZmYzZWFjZDlhZTg4YmM4NzUzOTUwMzBmYzRkNzZmYjI4NjVmMWUzMzEzNTE3Nz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=ey%7EfqBWw8lOuUYww6KLHaSOBW7NmgO7sJ9X8EUwB%7EGSZokvSjyiM28ZUKQO%7Eo-TUYyi0ICc6gzx-Ir7OFYyoOaTi0U7i6TzCL9Q4MJBQb%7EzOr69A3gOfIg-J8qAxyagjjyqk1qaeNmh2TUJEmeVO1Qv-lUKl22Sd5F8XAH0lA2Z7PXbqxpWOLEJqMFyL9k%7EAjNof5mJrPphH19caK9ykV10NC4RN%7EtAx4ekxSXmLZIVc1v8vxhUwrknlSfQ0kurjoxHlnyB434vvX9D9soUrnFbBug19o650fhADD-rXo0oXZi0SWjyWfoZ4MZRERTtnhQXvoIlFV2OxUIvkjJl4mg__&Key-Pair-Id=K24J24Z295AEI9 [following]\n",
+            "--2024-07-20 06:43:23--  https://cdn-lfs-us-1.huggingface.co/repos/03/4f/034f80180b5ba08a0d9fe97aa18e17eb02604e9ba5c3e985eaee5a613e4810f6/2c4295909be34cae21ff3eacd9ae88bc875395030fc4d76fb2865f1e33135177?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27embedding_trial3.h5%3B+filename%3D%22embedding_trial3.h5%22%3B&Expires=1721717003&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyMTcxNzAwM319LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzAzLzRmLzAzNGY4MDE4MGI1YmEwOGEwZDlmZTk3YWExOGUxN2ViMDI2MDRlOWJhNWMzZTk4NWVhZWU1YTYxM2U0ODEwZjYvMmM0Mjk1OTA5YmUzNGNhZTIxZmYzZWFjZDlhZTg4YmM4NzUzOTUwMzBmYzRkNzZmYjI4NjVmMWUzMzEzNTE3Nz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=ey%7EfqBWw8lOuUYww6KLHaSOBW7NmgO7sJ9X8EUwB%7EGSZokvSjyiM28ZUKQO%7Eo-TUYyi0ICc6gzx-Ir7OFYyoOaTi0U7i6TzCL9Q4MJBQb%7EzOr69A3gOfIg-J8qAxyagjjyqk1qaeNmh2TUJEmeVO1Qv-lUKl22Sd5F8XAH0lA2Z7PXbqxpWOLEJqMFyL9k%7EAjNof5mJrPphH19caK9ykV10NC4RN%7EtAx4ekxSXmLZIVc1v8vxhUwrknlSfQ0kurjoxHlnyB434vvX9D9soUrnFbBug19o650fhADD-rXo0oXZi0SWjyWfoZ4MZRERTtnhQXvoIlFV2OxUIvkjJl4mg__&Key-Pair-Id=K24J24Z295AEI9\n",
+            "Resolving cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)... 18.239.94.3, 18.239.94.84, 18.239.94.40, ...\n",
+            "Connecting to cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)|18.239.94.3|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 94732576 (90M) [binary/octet-stream]\n",
+            "Saving to: ‘embedding_trial3.h5’\n",
+            "\n",
+            "embedding_trial3.h5 100%[===================>]  90.34M  54.6MB/s    in 1.7s    \n",
+            "\n",
+            "2024-07-20 06:43:25 (54.6 MB/s) - ‘embedding_trial3.h5’ saved [94732576/94732576]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "61f4cb6d462345e4895d7c27d6c13b56",
+            "46c306e6724344f2b063656d0270c64d",
+            "08557b0eedc94451857161bf87c168fa",
+            "39a56b83ac3e46f3b4c43c9b10a6bd7a",
+            "691364b4ef1141dbb060f70ff8219f7d",
+            "dcb74213b9304df690148f10a25e894c",
+            "9657e4d50ad0483081737a7613731ed5",
+            "4814fcbc031c43a2b0b13de634d63f24",
+            "40e3e66ef43248a09a0990948efd6e46",
+            "d30a6c5221814f5997e742f15f1405e1",
+            "f8886dd2d1e34f4c8ad238ca83a3365f"
+          ]
+        },
+        "id": "XCtTmUv0iJy1",
+        "outputId": "ea18b443-db75-472c-8926-69d8101ea7b4"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/423 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "61f4cb6d462345e4895d7c27d6c13b56"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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+            "1/1 [==============================] - 0s 30ms/step\n",
+            "1/1 [==============================] - 0s 31ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Path to the dataset\n",
+        "dataset_path = 'lfw'\n",
+        "model=tf.keras.models.load_model('/content/embedding_trial3.h5')\n",
+        "# Generate embeddings for the original model\n",
+        "embeddings_original = generate_embeddings(model, dataset_path)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "00a64b4ea5314bccb09759474d50b522",
+            "ab160914cad6459da642e8edeb9c7147",
+            "ad8933fcf2484969a181c1b5e1f33fe4",
+            "778cc63f3ee34a418bbf31fb9826f284",
+            "f724e59099c746569f4f9a92362a5edf",
+            "d3be755af47b4ca5908aa51fdffe2149",
+            "f198114222f2481ca3005e14313844e6",
+            "be856f34b6e64662a97b98f4629c6068",
+            "970c3d69252f4f0f8f6e76a307a01cf6",
+            "3d9bac658ba9442888e9464b57fa551f",
+            "3361f1363a274662bb683dad5c04484a"
+          ]
+        },
+        "id": "fLYr8we0iJy1",
+        "outputId": "f72bce45-8b70-417d-987c-c34e3779f2fe"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/423 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "00a64b4ea5314bccb09759474d50b522"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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+            "1/1 [==============================] - 0s 30ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
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+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 29ms/step\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "1/1 [==============================] - 0s 30ms/step\n",
+            "1/1 [==============================] - 0s 30ms/step\n",
+            "1/1 [==============================] - 0s 28ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load the fine-tuned model\n",
+        "import tensorflow as tf\n",
+        "finetuned_model = tf.keras.models.load_model('/content/embedding_trial2.h5')\n",
+        "embeddings_finetuned = generate_embeddings(finetuned_model, dataset_path)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pickle\n",
+        "with open('embeddings_trial3.pkl', 'wb') as f:\n",
+        "    pickle.dump(embeddings_original, f)\n",
+        "with open('embeddings_finetuned.pkl', 'wb') as f:\n",
+        "    pickle.dump(embeddings_finetuned, f)"
+      ],
+      "metadata": {
+        "id": "SVsPGCy1qnT6"
+      },
+      "execution_count": 50,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mlpjNEeRiJy2"
+      },
+      "source": [
+        "## Calculate Metrics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "embeddings_finetuned[\"Wen_Jiabao\"].shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rQXD7DSwsuSl",
+        "outputId": "d874e86b-76e7-4634-c226-773a2f02171b"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(13, 1, 512)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "id": "OuTDI5iqiJy2"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate intra-cluster distances for both models\n",
+        "metrics_original = calculate_intra_cluster_metrics(embeddings_original)\n",
+        "metrics_finetuned = calculate_intra_cluster_metrics(embeddings_finetuned)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 927
+        },
+        "id": "Xn4zoiytiJy3",
+        "outputId": "dcae4ea6-1dd6-45cc-a608-c87f00a9421a"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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RNu2XLl3K9ksrJSVFffv21X333acmTZpoxowZ6tKlixo0aGBXjf3799fs2bM1ZswYdenSxboq58KFC1q1apXNTdPHjx+3ee31eYqLi1OrVq2s7RkZGTp+/LgeeOCBW+67RIkSOV4GzOmMmJubmzp27KiOHTsqKytLAwYM0KJFizR27Nibng2sUKGCPD09s9Ut5U+I3bdvnw4fPqxly5apT58+1va8XgK7UWpqao5nc44fP67SpUurTJkydzQ+zIV7ZPA/5cZLKl5eXqpWrZrNkuLrn+Fy4/LO9u3bKzMzU//9739t2mfPni2LxaJ27dpJkvWzRubPn2/T7+233851ndfPpNz4r/HC+qTV9u3b57i/WbNmSdItV2DlRcuWLTV58mT997//VUBAwE37OTs7Z5uLlStX6vTp09n6vvLKK4qPj9eyZcs0a9YsVapUSWFhYTY/47xwcXHR8OHDdfDgQevS3px+Punp6dl+5vXr11eZMmW0cOFCpaenW9tjYmKyvb9yUrVqVf3+++82n2T7yy+/ZPsqgRvf105OTtbLpbc6bldXV9WvXz/HT8suXrx4ri793EpO82QYRq6WhaekpOjq1avZ2j/55BNdvHgxx8vLu3btUkhIyB1UDDPijAz+p9x3331q0aKFgoODVbJkSe3cuVMff/yxBg0aZO0THBwsSRo8eLDatGkjZ2dn9ejRQx07dlTLli01evRo/fHHH3rggQe0YcMGff755xo6dKh1eW5wcLC6deumOXPm6MKFC9bl14cPH5aUu3/p+vj46JFHHtGMGTOUkZGhe+65Rxs2bMjxX84F4YEHHlBYWJgWL15svYyyfft2LVu2TJ07d1bLli3zZT9OTk4aM2bMbfs9/vjjmjRpkvr27asmTZpo3759Wr58ebb7TDZv3qz58+dr/Pjx1mXc0dHRatGihcaOHasZM2bYVWd4eLjGjRun119/XZ07d1aTJk1UokQJhYWFafDgwbJYLHrvvfeyhS1XV1dNmTJF/fv3V6tWrdS9e3cdP35c0dHRubpHpl+/fpo1a5batGmjiIgInTt3TgsXLtT9999vc1/Oc889p7/++kutWrVS+fLldeLECb399tuqV6+ezRm1nHTq1EmjR49WcnKyfHx8rO3BwcH68MMPFRkZqQYNGsjLyyvPH5pXs2ZNVa1aVSNGjNDp06fl4+NjDSK3ExcXp9DQUHXv3l01a9aUk5OTdu7cqffff1+VKlXKtqz83Llz+vXXXzVw4MA81Yi7gEPWSgF2uL78eseOHTlub968+W2XX0+ZMsVo2LCh4efnZ3h6eho1a9Y0XnvtNSM9Pd3a59q1a8bLL79slClTxrBYLDZLQi9fvmwMGzbMKFeunOHq6mpUr17dmDlzppGVlWWz35SUFGPgwIFGyZIlrct/Dx06ZEiyWQ59fcnp+fPnsx3PqVOnjC5duhh+fn6Gr6+v8dRTTxlnzpy56RLuG8e42bLonOYpJxkZGcbEiRONypUrG66urkZQUJAxatQo69Li2+0nJ7npe7Pl18OHDzcCAwMNT09Po2nTpsbWrVttlicnJycbFStWNB566CEjIyPDZsxhw4YZTk5OxtatW2+5b0nGwIEDc9w2YcIEm6XFP/74o9G4cWPD09PTKFeunDFy5Ejr0u9/Lz82DMOYP3++UblyZcPd3d2oX7++8d1332VbWp3T8mvDMIz333/fqFKliuHm5mbUq1fPWL9+fbbl1x9//LHx2GOPGWXLljXc3NyMChUqGP379zfOnj17y+M1DMNITEw0XFxcjPfee8+m/cqVK0bPnj0NPz8/Q5J1f9eXWK9cuTLbWDktvz5w4IARGhpqeHl5GaVLlzaef/5545dffsl2rDcuvz5//rzxwgsvGDVr1jSKFy9uuLm5GdWrVzeGDh2a45+XBQsWGMWKFTOSk5Nve8y4u1gMw47b1QHk2d69e/Xggw/q/fffV69evRxdDmAVERGhw4cP5/iJw2bx4IMPqkWLFjYfTIn/DdwjAxSAnD5+fc6cOXJycrrtJ+oChW38+PHasWNHtntvzGLdunWKi4vTqFGjHF0KHIAzMkABmDhxonbt2qWWLVvKxcXFuiT2hRdeyNOHswEAbo0gAxSAjRs3auLEiTpw4ICuXLmiChUq6Nlnn9Xo0aPl4sI99gCQXwgyAADAtLhHBgAAmBZBBgAAmNZdf7E+KytLZ86ckbe3N18kBgCASRiGocuXL6tcuXLZvrT13+76IHPmzBkFBQU5ugwAAGCHkydPqnz58jfdftcHmetf7nfy5Embj98GAABFV3JysoKCgmy+pDcnd32QuX45ycfHhyADAIDJ3O62EG72BQAApkWQAQAApkWQAQAApkWQAQAApuXwIHP69Gn17t1bpUqVkqenp+rUqaOdO3datxuGoXHjxikwMFCenp4KDQ1VXFycAysGAABFhUODzMWLF9W0aVO5urpq7dq1OnDggN58802VKFHC2mfGjBl66623tHDhQv38888qXry42rRpo9TUVAdWDgAAigKHfmnkq6++qh9//FHff/99jtsNw1C5cuU0fPhwjRgxQpKUlJQkf39/xcTEqEePHrfdR3Jysnx9fZWUlMTyawAATCK3v78dekbmiy++UP369fXUU0+pbNmyevDBB7VkyRLr9uPHjyshIUGhoaHWNl9fXzVq1Ehbt251RMkAAKAIcWiQOXbsmBYsWKDq1atr/fr1eumllzR48GAtW7ZMkpSQkCBJ8vf3t3mdv7+/dduN0tLSlJycbPMAAAB3J4d+sm9WVpbq16+vqVOnSpIefPBB/fbbb1q4cKHCwsLsGnPatGmaOHFifpYJAACKKIeekQkMDNR9991n01arVi3Fx8dLkgICAiRJiYmJNn0SExOt2240atQoJSUlWR8nT54sgMoBAEBR4NAg07RpUx06dMim7fDhw6pYsaIkqXLlygoICNCmTZus25OTk/Xzzz8rJCQkxzHd3d2t36vE9ysBAHB3c+ilpWHDhqlJkyaaOnWqnn76aW3fvl2LFy/W4sWLJf3zRVFDhw7VlClTVL16dVWuXFljx45VuXLl1LlzZ0eWDgAAigCHBpkGDRro008/1ahRozRp0iRVrlxZc+bMUa9evax9Ro4cqZSUFL3wwgu6dOmSmjVrpnXr1snDw8OBlQMAgKLAoZ8jUxj4HBkAAMwnt7+/HXpGBsitiJgdBTJuVHiDAhkXAFA4HP5dSwAAAPYiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANMiyAAAANNycXQBwN0qImZHgYwbFd6gQMYFADPijAwAADAthwaZCRMmyGKx2Dxq1qxp3Z6amqqBAweqVKlS8vLyUrdu3ZSYmOjAigEAQFHi8DMy999/v86ePWt9/PDDD9Ztw4YN0+rVq7Vy5Up9++23OnPmjLp27erAagEAQFHi8HtkXFxcFBAQkK09KSlJUVFRWrFihVq1aiVJio6OVq1atbRt2zY1bty4sEsFAABFjMPPyMTFxalcuXKqUqWKevXqpfj4eEnSrl27lJGRodDQUGvfmjVrqkKFCtq6detNx0tLS1NycrLNAwAA3J0cGmQaNWqkmJgYrVu3TgsWLNDx48f18MMP6/Lly0pISJCbm5v8/PxsXuPv76+EhISbjjlt2jT5+vpaH0FBQQV8FAAAwFEcemmpXbt21v+vW7euGjVqpIoVK+qjjz6Sp6enXWOOGjVKkZGR1ufJycmEGQAA7lIOv7T0b35+fqpRo4aOHDmigIAApaen69KlSzZ9EhMTc7yn5jp3d3f5+PjYPAAAwN2pSAWZK1eu6OjRowoMDFRwcLBcXV21adMm6/ZDhw4pPj5eISEhDqwSAAAUFQ69tDRixAh17NhRFStW1JkzZzR+/Hg5OzvrmWeeka+vryIiIhQZGamSJUvKx8dHL7/8skJCQlixBAAAJDk4yJw6dUrPPPOMLly4oDJlyqhZs2batm2bypQpI0maPXu2nJyc1K1bN6WlpalNmzaaP3++I0sGAABFiEODTGxs7C23e3h4aN68eZo3b14hVQQAAMykSN0jAwAAkBcEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFouji4AQN5ExOwosLGjwhsU2NgAUBA4IwMAAEyLIAMAAEyLIAMAAEyLIAMAAEyLIAMAAEyLIAMAAEyLIAMAAEyLIAMAAEyLIAMAAEyLIAMAAEyLIAMAAEyL71rC/7SC/N4iAEDB44wMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwLYIMAAAwrSITZKZPny6LxaKhQ4da21JTUzVw4ECVKlVKXl5e6tatmxITEx1XJAAAKFKKRJDZsWOHFi1apLp169q0Dxs2TKtXr9bKlSv17bff6syZM+ratauDqgQAAEWNw4PMlStX1KtXLy1ZskQlSpSwticlJSkqKkqzZs1Sq1atFBwcrOjoaP3000/atm2bAysGAABFhcODzMCBA9WhQweFhobatO/atUsZGRk27TVr1lSFChW0devWm46Xlpam5ORkmwcAALg7uThy57Gxsdq9e7d27NiRbVtCQoLc3Nzk5+dn0+7v76+EhISbjjlt2jRNnDgxv0sFAABFkMPOyJw8eVJDhgzR8uXL5eHhkW/jjho1SklJSdbHyZMn821sAABQtDgsyOzatUvnzp3TQw89JBcXF7m4uOjbb7/VW2+9JRcXF/n7+ys9PV2XLl2yeV1iYqICAgJuOq67u7t8fHxsHgAA4O7ksEtLrVu31r59+2za+vbtq5o1a+qVV15RUFCQXF1dtWnTJnXr1k2SdOjQIcXHxyskJMQRJQMAgCLGYUHG29tbtWvXtmkrXry4SpUqZW2PiIhQZGSkSpYsKR8fH7388ssKCQlR48aNHVEyAAAoYhx6s+/tzJ49W05OTurWrZvS0tLUpk0bzZ8/39FlAQCAIsJiGIbh6CIKUnJysnx9fZWUlMT9MiYWEZN9ZRvyX1R4A0eXAACScv/72+GfIwMAAGAvggwAADAtggwAADAtggwAADAtggwAADCtIr38GubCyiLzK6ifIauhABQUzsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTsivIHDt2LL/rAAAAyDO7gky1atXUsmVLvf/++0pNTc3vmgAAAHLFriCze/du1a1bV5GRkQoICFD//v21ffv2/K4NAADgluwKMvXq1dPcuXN15swZLV26VGfPnlWzZs1Uu3ZtzZo1S+fPn8/vOgEAALK5o5t9XVxc1LVrV61cuVKvv/66jhw5ohEjRigoKEh9+vTR2bNn86tOAACAbO4oyOzcuVMDBgxQYGCgZs2apREjRujo0aPauHGjzpw5o06dOuVXnQAAANm42POiWbNmKTo6WocOHVL79u317rvvqn379nJy+icXVa5cWTExMapUqVJ+1goAAGDDriCzYMEC9evXT+Hh4QoMDMyxT9myZRUVFXVHxQEAANyKXUEmLi7utn3c3NwUFhZmz/AAAAC5Ytc9MtHR0Vq5cmW29pUrV2rZsmV3XBQAAEBu2BVkpk2bptKlS2drL1u2rKZOnXrHRQEAAOSGXUEmPj5elStXztZesWJFxcfH33FRAAAAuWFXkClbtqx+/fXXbO2//PKLSpUqdcdFAQAA5IZdQeaZZ57R4MGD9c033ygzM1OZmZnavHmzhgwZoh49euR3jQAAADmya9XS5MmT9ccff6h169ZycflniKysLPXp04d7ZAAAQKGxK8i4ubnpww8/1OTJk/XLL7/I09NTderUUcWKFfO7PgAAgJuyK8hcV6NGDdWoUSO/agEAAMgTu4JMZmamYmJitGnTJp07d05ZWVk22zdv3pwvxQEAANyKXUFmyJAhiomJUYcOHVS7dm1ZLJb8rgsAAOC27AoysbGx+uijj9S+ffv8rgcAACDX7Fp+7ebmpmrVquV3LQAAAHliV5AZPny45s6dK8Mw8rseAACAXLPr0tIPP/ygb775RmvXrtX9998vV1dXm+2rVq3Kl+IAAABuxa4g4+fnpy5duuR3LQAAAHliV5CJjo7O7zoAAADyzK57ZCTp2rVr+vrrr7Vo0SJdvnxZknTmzBlduXIl34oDAAC4FbvOyJw4cUJt27ZVfHy80tLS9Oijj8rb21uvv/660tLStHDhwvyuEwAAIBu7PxCvfv36+uWXX1SqVClre5cuXfT888/nW3EA7g4RMTsKbOyo8AYFNjaAos+uIPP999/rp59+kpubm017pUqVdPr06XwpDAAA4HbsukcmKytLmZmZ2dpPnTolb2/vOy4KAAAgN+wKMo899pjmzJljfW6xWHTlyhWNHz+ery0AAACFxq5LS2+++abatGmj++67T6mpqerZs6fi4uJUunRpffDBB/ldIwAAQI7sCjLly5fXL7/8otjYWP3666+6cuWKIiIi1KtXL3l6euZ3jQAAADmyK8hIkouLi3r37p2ftQAAAOSJXUHm3XffveX2Pn365GqcBQsWaMGCBfrjjz8kSffff7/GjRundu3aSZJSU1M1fPhwxcbGKi0tTW3atNH8+fPl7+9vT9kAAOAuY/fnyPxbRkaGrl69Kjc3NxUrVizXQaZ8+fKaPn26qlevLsMwtGzZMnXq1El79uzR/fffr2HDhmnNmjVauXKlfH19NWjQIHXt2lU//vijPWUDAIC7jF1B5uLFi9na4uLi9NJLL+k///lPrsfp2LGjzfPXXntNCxYs0LZt21S+fHlFRUVpxYoVatWqlaR/vuOpVq1a2rZtmxo3bmxP6QAA4C5i93ct3ah69eqaPn16trM1uZWZmanY2FilpKQoJCREu3btUkZGhkJDQ619atasqQoVKmjr1q35VTYAADAxu2/2zXEwFxedOXMmT6/Zt2+fQkJClJqaKi8vL3366ae67777tHfvXrm5ucnPz8+mv7+/vxISEm46XlpamtLS0qzPk5OT81QPAAAwD7uCzBdffGHz3DAMnT17Vv/973/VtGnTPI117733au/evUpKStLHH3+ssLAwffvtt/aUJUmaNm2aJk6caPfrAQCAedgVZDp37mzz3GKxqEyZMmrVqpXefPPNPI3l5uamatWqSZKCg4O1Y8cOzZ07V927d1d6erouXbpkc1YmMTFRAQEBNx1v1KhRioyMtD5PTk5WUFBQnmoCAADmYFeQycrKyu86bMZOS0tTcHCwXF1dtWnTJnXr1k2SdOjQIcXHxyskJOSmr3d3d5e7u3uB1QcAAIqOfL1HJq9GjRqldu3aqUKFCrp8+bJWrFihLVu2aP369fL19VVERIQiIyNVsmRJ+fj46OWXX1ZISAgrlgAAgCQ7g8y/L93czqxZs2667dy5c+rTp4/Onj0rX19f1a1bV+vXr9ejjz4qSZo9e7acnJzUrVs3mw/EAwAAkOwMMnv27NGePXuUkZGhe++9V5J0+PBhOTs766GHHrL2s1gstxwnKirqlts9PDw0b948zZs3z54yAQDAXc6uINOxY0d5e3tr2bJlKlGihKR/PiSvb9++evjhhzV8+PB8LRIAACAndn0g3ptvvqlp06ZZQ4wklShRQlOmTMnzqiUAAAB72RVkkpOTdf78+Wzt58+f1+XLl++4KAAAgNywK8h06dJFffv21apVq3Tq1CmdOnVKn3zyiSIiItS1a9f8rhEAACBHdt0js3DhQo0YMUI9e/ZURkbGPwO5uCgiIkIzZ87M1wIBAABuxq4gU6xYMc2fP18zZ87U0aNHJUlVq1ZV8eLF87U4AACAW7mjb78+e/aszp49q+rVq6t48eIyDCO/6gIAALgtu4LMhQsX1Lp1a9WoUUPt27fX2bNnJUkREREsvQYAAIXGriAzbNgwubq6Kj4+XsWKFbO2d+/eXevWrcu34gAAAG7FrntkNmzYoPXr16t8+fI27dWrV9eJEyfypTAAAIDbseuMTEpKis2ZmOv++usvvnkaAAAUGruCzMMPP6x3333X+txisSgrK0szZsxQy5Yt8604AACAW7Hr0tKMGTPUunVr7dy5U+np6Ro5cqT279+vv/76Sz/++GN+1wgAAJAju87I1K5dW4cPH1azZs3UqVMnpaSkqGvXrtqzZ4+qVq2a3zUCAADkKM9nZDIyMtS2bVstXLhQo0ePLoiaAAAAciXPZ2RcXV3166+/FkQtAAAAeWLXpaXevXsrKioqv2sBAADIE7tu9r127ZqWLl2qr7/+WsHBwdm+Y2nWrFn5UhwAAMCt5CnIHDt2TJUqVdJvv/2mhx56SJJ0+PBhmz4WiyX/qgMAALiFPAWZ6tWr6+zZs/rmm28k/fOVBG+99Zb8/f0LpDgAAIBbydM9Mjd+u/XatWuVkpKSrwUBAADkll03+153Y7ABAAAoTHkKMhaLJds9MNwTAwAAHCVP98gYhqHw8HDrF0OmpqbqxRdfzLZqadWqVflXIQAAwE3kKciEhYXZPO/du3e+FgMAAJAXeQoy0dHRBVUHAABAnt3Rzb4AAACORJABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACm5eLoAlD4ImJ2OLoEAADyBWdkAACAaTk0yEybNk0NGjSQt7e3ypYtq86dO+vQoUM2fVJTUzVw4ECVKlVKXl5e6tatmxITEx1UMQAAKEocGmS+/fZbDRw4UNu2bdPGjRuVkZGhxx57TCkpKdY+w4YN0+rVq7Vy5Up9++23OnPmjLp27erAqgEAQFHh0Htk1q1bZ/M8JiZGZcuW1a5du/TII48oKSlJUVFRWrFihVq1aiVJio6OVq1atbRt2zY1btzYEWUDAIAiokjdI5OUlCRJKlmypCRp165dysjIUGhoqLVPzZo1VaFCBW3dujXHMdLS0pScnGzzAAAAd6ciE2SysrI0dOhQNW3aVLVr15YkJSQkyM3NTX5+fjZ9/f39lZCQkOM406ZNk6+vr/URFBRU0KUDAAAHKTJBZuDAgfrtt98UGxt7R+OMGjVKSUlJ1sfJkyfzqUIAAFDUFInPkRk0aJC+/PJLfffddypfvry1PSAgQOnp6bp06ZLNWZnExEQFBATkOJa7u7vc3d0LumQAAFAEOPSMjGEYGjRokD799FNt3rxZlStXttkeHBwsV1dXbdq0ydp26NAhxcfHKyQkpLDLBQAARYxDz8gMHDhQK1as0Oeffy5vb2/rfS++vr7y9PSUr6+vIiIiFBkZqZIlS8rHx0cvv/yyQkJCWLEEAAAcG2QWLFggSWrRooVNe3R0tMLDwyVJs2fPlpOTk7p166a0tDS1adNG8+fPL+RKAQBAUeTQIGMYxm37eHh4aN68eZo3b14hVAQAAMykyKxaAgAAyCuCDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC0XRxcAAHciImaHo0vIs6jwBo4uAbhrcEYGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYloujC0DOImJ2OLoEALAqyL+TosIbFNjYuPtxRgYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWq5YAoJCxAgjIP5yRAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApuXQIPPdd9+pY8eOKleunCwWiz777DOb7YZhaNy4cQoMDJSnp6dCQ0MVFxfnmGIBAECR49Agk5KSogceeEDz5s3LcfuMGTP01ltvaeHChfr5559VvHhxtWnTRqmpqYVcKQAAKIoc+sm+7dq1U7t27XLcZhiG5syZozFjxqhTp06SpHfffVf+/v767LPP1KNHj8IsFQAAFEFF9h6Z48ePKyEhQaGhodY2X19fNWrUSFu3br3p69LS0pScnGzzAAAAd6ciG2QSEhIkSf7+/jbt/v7+1m05mTZtmnx9fa2PoKCgAq0TAAA4TpENMvYaNWqUkpKSrI+TJ086uiQAAFBAimyQCQgIkCQlJibatCcmJlq35cTd3V0+Pj42DwAAcHcqskGmcuXKCggI0KZNm6xtycnJ+vnnnxUSEuLAygAAQFHh0FVLV65c0ZEjR6zPjx8/rr1796pkyZKqUKGChg4dqilTpqh69eqqXLmyxo4dq3Llyqlz586OKxoAABQZDg0yO3fuVMuWLa3PIyMjJUlhYWGKiYnRyJEjlZKSohdeeEGXLl1Ss2bNtG7dOnl4eDiqZAAAUIQ4NMi0aNFChmHcdLvFYtGkSZM0adKkQqwKAACYRZG9RwYAAOB2CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0HLpqCQCQvyJidji6BKBQcUYGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFkEGAACYFt+1dAf4ThMAAByLMzIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0WLUEALgrFeTK0qjwBgU2NvKGMzIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0+K4lAIBDFeR3IuHuxxkZAABgWgQZAABgWgQZAABgWgQZAABgWgQZAABgWqxaAgCgiDDjCq6o8AYO3T9nZAAAgGkRZAAAgGkRZAAAgGkRZAAAgGkRZAAAgGkRZAAAgGmZIsjMmzdPlSpVkoeHhxo1aqTt27c7uiQAAFAEFPkg8+GHHyoyMlLjx4/X7t279cADD6hNmzY6d+6co0sDAAAOVuSDzKxZs/T888+rb9++uu+++7Rw4UIVK1ZMS5cudXRpAADAwYp0kElPT9euXbsUGhpqbXNyclJoaKi2bt3qwMoAAEBRUKS/ouDPP/9UZmam/P39bdr9/f31+++/5/iatLQ0paWlWZ8nJSVJkpKTk/O9vvS/r+T7mACAoq8gfqdI5vy9UlBzcX1cwzBu2a9IBxl7TJs2TRMnTszWHhQU5IBqAAB3o/cHOLqCoqOg5+Ly5cvy9fW96fYiHWRKly4tZ2dnJSYm2rQnJiYqICAgx9eMGjVKkZGR1udZWVn666+/VKpUKVkslgKt1x7JyckKCgrSyZMn5ePj4+hyigzmJWfMS86Yl5wxLzljXnJW1ObFMAxdvnxZ5cqVu2W/Ih1k3NzcFBwcrE2bNqlz586S/gkmmzZt0qBBg3J8jbu7u9zd3W3a/Pz8CrjSO+fj41Mk3jhFDfOSM+YlZ8xLzpiXnDEvOStK83KrMzHXFekgI0mRkZEKCwtT/fr11bBhQ82ZM0cpKSnq27evo0sDAAAOVuSDTPfu3XX+/HmNGzdOCQkJqlevntatW5ftBmAAAPC/p8gHGUkaNGjQTS8lmZ27u7vGjx+f7XLY/zrmJWfMS86Yl5wxLzljXnJm1nmxGLdb1wQAAFBEFekPxAMAALgVggwAADAtggwAADAtggwAADAtgkwhmDdvnipVqiQPDw81atRI27dvv2nfJUuW6OGHH1aJEiVUokQJhYaG3rK/meVlXlatWqX69evLz89PxYsXV7169fTee+8VYrWFJy/z8m+xsbGyWCzWD4+82+RlXmJiYmSxWGweHh4ehVht4cnr++XSpUsaOHCgAgMD5e7urho1auirr74qpGoLT17mpUWLFtneLxaLRR06dCjEigtHXt8vc+bM0b333itPT08FBQVp2LBhSk1NLaRqc8lAgYqNjTXc3NyMpUuXGvv37zeef/55w8/Pz0hMTMyxf8+ePY158+YZe/bsMQ4ePGiEh4cbvr6+xqlTpwq58oKV13n55ptvjFWrVhkHDhwwjhw5YsyZM8dwdnY21q1bV8iVF6y8zst1x48fN+655x7j4YcfNjp16lQ4xRaivM5LdHS04ePjY5w9e9b6SEhIKOSqC15e5yUtLc2oX7++0b59e+OHH34wjh8/bmzZssXYu3dvIVdesPI6LxcuXLB5r/z222+Gs7OzER0dXbiFF7C8zsvy5csNd3d3Y/ny5cbx48eN9evXG4GBgcawYcMKufJbI8gUsIYNGxoDBw60Ps/MzDTKlStnTJs2LVevv3btmuHt7W0sW7asoEp0iDudF8MwjAcffNAYM2ZMQZTnMPbMy7Vr14wmTZoY77zzjhEWFnZXBpm8zkt0dLTh6+tbSNU5Tl7nZcGCBUaVKlWM9PT0wirRIe7075fZs2cb3t7expUrVwqqRIfI67wMHDjQaNWqlU1bZGSk0bRp0wKtM6+4tFSA0tPTtWvXLoWGhlrbnJycFBoaqq1bt+ZqjKtXryojI0MlS5YsqDIL3Z3Oi2EY2rRpkw4dOqRHHnmkIEstVPbOy6RJk1S2bFlFREQURpmFzt55uXLliipWrKigoCB16tRJ+/fvL4xyC4098/LFF18oJCREAwcOlL+/v2rXrq2pU6cqMzOzsMoucPnx925UVJR69Oih4sWLF1SZhc6eeWnSpIl27dplvfx07NgxffXVV2rfvn2h1JxbpvhkX7P6888/lZmZme3rFPz9/fX777/naoxXXnlF5cqVs3nzmZ2985KUlKR77rlHaWlpcnZ21vz58/Xoo48WdLmFxp55+eGHHxQVFaW9e/cWQoWOYc+83HvvvVq6dKnq1q2rpKQkvfHGG2rSpIn279+v8uXLF0bZBc6eeTl27Jg2b96sXr166auvvtKRI0c0YMAAZWRkaPz48YVRdoG70793t2/frt9++01RUVEFVaJD2DMvPXv21J9//qlmzZrJMAxdu3ZNL774ov7v//6vMErONYJMETZ9+nTFxsZqy5Ytd+2Ninnh7e2tvXv36sqVK9q0aZMiIyNVpUoVtWjRwtGlOcTly5f17LPPasmSJSpdurSjyylSQkJCFBISYn3epEkT1apVS4sWLdLkyZMdWJljZWVlqWzZslq8eLGcnZ0VHBys06dPa+bMmXdNkLlTUVFRqlOnjho2bOjoUhxuy5Ytmjp1qubPn69GjRrpyJEjGjJkiCZPnqyxY8c6ujwrgkwBKl26tJydnZWYmGjTnpiYqICAgFu+9o033tD06dP19ddfq27dugVZZqGzd16cnJxUrVo1SVK9evV08OBBTZs27a4JMnmdl6NHj+qPP/5Qx44drW1ZWVmSJBcXFx06dEhVq1Yt2KILwZ38ObrO1dVVDz74oI4cOVIQJTqEPfMSGBgoV1dXOTs7W9tq1aqlhIQEpaeny83NrUBrLgx38n5JSUlRbGysJk2aVJAlOoQ98zJ27Fg9++yzeu655yRJderUUUpKil544QWNHj1aTk5F4+6UolHFXcrNzU3BwcHatGmTtS0rK0ubNm2y+dfijWbMmKHJkydr3bp1ql+/fmGUWqjsnZcbZWVlKS0trSBKdIi8zkvNmjW1b98+7d271/p44okn1LJlS+3du1dBQUGFWX6ByY/3S2Zmpvbt26fAwMCCKrPQ2TMvTZs21ZEjR6yBV5IOHz6swMDAuyLESHf2flm5cqXS0tLUu3fvgi6z0NkzL1evXs0WVq6HYKMofU2jg282vuvFxsYa7u7uRkxMjHHgwAHjhRdeMPz8/KxLQZ999lnj1VdftfafPn264ebmZnz88cc2ywEvX77sqEMoEHmdl6lTpxobNmwwjh49ahw4cMB44403DBcXF2PJkiWOOoQCkdd5udHdumopr/MyceJEY/369cbRo0eNXbt2GT169DA8PDyM/fv3O+oQCkRe5yU+Pt7w9vY2Bg0aZBw6dMj48ssvjbJlyxpTpkxx1CEUCHv/HDVr1szo3r17YZdbaPI6L+PHjze8vb2NDz74wDh27JixYcMGo2rVqsbTTz/tqEPIEUGmELz99ttGhQoVDDc3N6Nhw4bGtm3brNuaN29uhIWFWZ9XrFjRkJTtMX78+MIvvIDlZV5Gjx5tVKtWzfDw8DBKlChhhISEGLGxsQ6ouuDlZV5udLcGGcPI27wMHTrU2tff399o3769sXv3bgdUXfDy+n756aefjEaNGhnu7u5GlSpVjNdee824du1aIVdd8PI6L7///rshydiwYUMhV1q48jIvGRkZxoQJE4yqVasaHh4eRlBQkDFgwADj4sWLhV/4LVgMoyidHwIAAMg97pEBAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZAB8D9vwoQJqlevnvV5eHi4Onfu7LB6AOQeQQbAbYWHh8tisejFF1/Mtm3gwIGyWCwKDw8v0BpiYmJksVhksVjk5OSkwMBAde/eXfHx8fm+r7lz5yomJibfxwWQ/wgyAHIlKChIsbGx+vvvv61tqampWrFihSpUqFAoNfj4+Ojs2bM6ffq0PvnkEx06dEhPPfVUvu/H19dXfn5++T4ugPxHkAGQKw899JCCgoK0atUqa9uqVatUoUIFPfjggzZ9161bp2bNmsnPz0+lSpXS448/rqNHj1q3v/vuu/Ly8lJcXJy1bcCAAapZs6auXr160xosFosCAgIUGBioJk2aKCIiQtu3b1dycrK1zyuvvKIaNWqoWLFiqlKlisaOHauMjAybcaZPny5/f395e3srIiJCqampNttvvLRUqVIlzZkzx6ZPvXr1NGHCBEn/fBPwhAkTVKFCBbm7u6tcuXIaPHjwTY8DQP4hyADItX79+ik6Otr6fOnSperbt2+2fikpKYqMjNTOnTu1adMmOTk5qUuXLsrKypIk9enTR+3bt1evXr107do1rVmzRu+8846WL1+uYsWK5aqWc+fO6dNPP5Wzs7OcnZ2t7d7e3oqJidGBAwc0d+5cLVmyRLNnz7Zu/+ijjzRhwgRNnTpVO3fuVGBgoObPn2/vlEiSPvnkE82ePVuLFi1SXFycPvvsM9WpU+eOxgSQOy6OLgCAefTu3VujRo3SiRMnJEk//vijYmNjtWXLFpt+3bp1s3m+dOlSlSlTRgcOHFDt2rUlSYsWLVLdunU1ePBgrVq1ShMmTFBwcPAt95+UlCQvLy8ZhmE9czN48GAVL17c2mfMmDHW/69UqZJGjBih2NhYjRw5UpI0Z84cRUREKCIiQpI0ZcoUff3119nOyuRFfHy8AgICFBoaKldXV1WoUEENGza0ezwAuccZGQC5VqZMGXXo0EExMTGKjo5Whw4dVLp06Wz94uLi9Mwzz6hKlSry8fFRpUqVJMnmxtwSJUooKipKCxYsUNWqVfXqq6/edv/e3t7au3evdu7cqTfffFMPPfSQXnvtNZs+H374oZo2baqAgAB5eXlpzJgxNvs9ePCgGjVqZPOakJCQvExDNk899ZT+/vtvValSRc8//7w+/fRTXbt27Y7GBJA7BBkAedKvXz/FxMRo2bJl6tevX459OnbsqL/++ktLlizRzz//rJ9//lmSlJ6ebtPvu+++k7Ozs86ePauUlJTb7tvJyUnVqlVTrVq1FBkZqcaNG+ull16ybt+6dat69eql9u3b68svv9SePXs0evTobPvNKycnJxmGYdP27/tugoKCdOjQIc2fP1+enp4aMGCAHnnkkWz35gDIfwQZAHnStm1bpaenKyMjQ23atMm2/cKFCzp06JDGjBmj1q1bq1atWrp48WK2fj/99JNef/11rV69Wl5eXho0aFCea3n11Vf14Ycfavfu3dYxK1asqNGjR6t+/fqqXr269TLYdbVq1bIGq+u2bdt2y/2UKVNGZ8+etT5PTk7W8ePHbfp4enqqY8eOeuutt7RlyxZt3bpV+/bty/MxAcgb7pEBkCfOzs46ePCg9f9vVKJECZUqVUqLFy9WYGCg4uPjs102unz5sp599lkNHjxY7dq1U/ny5dWgQQN17NhRTz75ZK5rCQoKUpcuXTRu3Dh9+eWXql69uuLj4xUbG6sGDRpozZo1+vTTT21eM2TIEIWHh6t+/fpq2rSpli9frv3796tKlSo33U+rVq0UExOjjh07ys/PT+PGjbM59piYGGVmZqpRo0YqVqyY3n//fXl6eqpixYq5PhYA9uGMDIA88/HxkY+PT47bnJycFBsbq127dql27doaNmyYZs6cadNnyJAhKl68uKZOnSpJqlOnjqZOnar+/fvr9OnTeapl2LBhWrNmjbZv364nnnhCw4YN06BBg1SvXj399NNPGjt2rE3/7t27a+zYsRo5cqSCg4N14sQJm8tTORk1apSaN2+uxx9/XB06dFDnzp1VtWpV63Y/Pz8tWbJETZs2Vd26dfX1119r9erVKlWqVJ6OBUDeWYwbL/wCAACYBGdkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaf0/FovAVrU+WFQAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def plot_histogram(metrics, key, title, xlabel):\n",
+        "    values = [metrics[label][key] for label in metrics]\n",
+        "    plt.figure()\n",
+        "    plt.hist(values, bins=20, alpha=0.7)\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('Frequency')\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_histogram(metrics_original, 'max_radius', 'Histogram of Max Radius (trial3)', 'Max Radius')\n",
+        "plot_histogram(metrics_finetuned, 'max_radius', 'Histogram of Max Radius (trial2)', 'Max Radius')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 887
+        },
+        "id": "PeTLOsthiJy3",
+        "outputId": "cb8144af-2cf6-4c99-ebb7-d964c4070e49"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def plot_boxplot(metrics, key, title, ylabel):\n",
+        "    values = [metrics[label][key] for label in metrics]\n",
+        "    plt.figure()\n",
+        "    plt.boxplot(values)\n",
+        "    plt.title(title)\n",
+        "    plt.ylabel(ylabel)\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_boxplot(metrics_original, 'percentile_50', 'Boxplot of Median Distances (trial3)', 'Median Distance')\n",
+        "plot_boxplot(metrics_finetuned, 'percentile_50', 'Boxplot of Median Distances (trial2)', 'Median Distance')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 56,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "tx0_WPHWiJy3",
+        "outputId": "be4a3aaf-2167-42a5-9a9b-6a7780002705"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def plot_histogram(metrics, title, xlabel):\n",
+        "    values = list(metrics.values()) # Extract values from the dictionary\n",
+        "    plt.figure()\n",
+        "    plt.hist(values, bins=20, alpha=0.7)\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('Frequency')\n",
+        "    plt.show()\n",
+        "\n",
+        "def compare_metrics(metrics_original, metrics_finetuned, key):\n",
+        "    changes = {}\n",
+        "    for label in metrics_original:\n",
+        "        if label in metrics_finetuned:\n",
+        "            change = metrics_finetuned[label][key] - metrics_original[label][key]\n",
+        "            changes[label] = change\n",
+        "    return changes\n",
+        "\n",
+        "max_radius_change = compare_metrics(metrics_original, metrics_finetuned, 'max_radius')\n",
+        "plot_histogram(max_radius_change, 'Histogram of Max Radius Change', 'Max Radius Change')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {
+        "id": "UMkNB6xviJy3"
+      },
+      "outputs": [],
+      "source": [
+        "def calculate_inter_cluster_metrics(embeddings):\n",
+        "    centers = {label: metrics['center'] for label, metrics in embeddings.items()}\n",
+        "    labels = list(centers.keys())\n",
+        "    inter_cluster_distances = euclidean_distances(list(centers.values()))\n",
+        "\n",
+        "    metrics = {}\n",
+        "    for i, label1 in enumerate(labels):\n",
+        "        for j, label2 in enumerate(labels):\n",
+        "            if i >= j:\n",
+        "                continue\n",
+        "            distance = inter_cluster_distances[i, j]\n",
+        "            pair = tuple(sorted((label1, label2)))\n",
+        "            metrics[pair] = distance\n",
+        "\n",
+        "    return metrics\n",
+        "\n",
+        "inter_cluster_metrics_original = calculate_inter_cluster_metrics(metrics_original)\n",
+        "inter_cluster_metrics_finetuned = calculate_inter_cluster_metrics(metrics_finetuned)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "K9Und3BkiJy4",
+        "outputId": "2a3a513f-0026-4928-90e9-86b6218f632f"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def compare_inter_cluster_metrics(metrics_original, metrics_finetuned):\n",
+        "    changes = {}\n",
+        "    for pair in metrics_original:\n",
+        "        if pair in metrics_finetuned:\n",
+        "            change = metrics_finetuned[pair] - metrics_original[pair]\n",
+        "            changes[pair] = change\n",
+        "    return changes\n",
+        "\n",
+        "inter_cluster_distance_change = compare_inter_cluster_metrics(inter_cluster_metrics_original, inter_cluster_metrics_finetuned)\n",
+        "\n",
+        "def plot_inter_cluster_changes(inter_cluster_distance_change, title, xlabel):\n",
+        "    values = list(inter_cluster_distance_change.values())\n",
+        "    plt.figure()\n",
+        "    plt.hist(values, bins=20, alpha=0.7)\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel(xlabel)\n",
+        "    plt.ylabel('Frequency')\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_inter_cluster_changes(inter_cluster_distance_change, 'Histogram of Inter-Cluster Distance Changes', 'Inter-Cluster Distance Change')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QforM1ZSiJy4"
+      },
+      "source": [
+        "## Impact on faces"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EmxEp0iDiJy4",
+        "outputId": "9ad75c66-35e9-4846-a737-b756a01674c8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Positively Impacted Faces: 24584\n",
+            "Negatively Impacted Faces: 54711\n",
+            "Averagely Impacted Faces: 10804\n"
+          ]
+        }
+      ],
+      "source": [
+        "def classify_impact(intra_metrics_original, intra_metrics_finetuned, inter_metrics_original, inter_metrics_finetuned):\n",
+        "    impact = {\n",
+        "        'positively_impacted': [],\n",
+        "        'negatively_impacted': [],\n",
+        "        'averagely_impacted': []\n",
+        "    }\n",
+        "\n",
+        "    # Define thresholds for considering a change as significant\n",
+        "    threshold = 0.01  # You can adjust this value based on your needs\n",
+        "\n",
+        "    # Classify intra-cluster metric changes\n",
+        "    for label in intra_metrics_original:\n",
+        "        if label in intra_metrics_finetuned:\n",
+        "            max_radius_change = intra_metrics_finetuned[label]['max_radius'] - intra_metrics_original[label]['max_radius']\n",
+        "            if max_radius_change < -threshold:\n",
+        "                impact['positively_impacted'].append((label, 'max_radius'))\n",
+        "            elif max_radius_change > threshold:\n",
+        "                impact['negatively_impacted'].append((label, 'max_radius'))\n",
+        "            else:\n",
+        "                impact['averagely_impacted'].append((label, 'max_radius'))\n",
+        "\n",
+        "            # Add other metrics here if needed\n",
+        "            # Example: variance, standard_deviation, etc.\n",
+        "            variance_change = intra_metrics_finetuned[label]['variance'] - intra_metrics_original[label]['variance']\n",
+        "            if variance_change < -threshold:\n",
+        "                impact['positively_impacted'].append((label, 'variance'))\n",
+        "            elif variance_change > threshold:\n",
+        "                impact['negatively_impacted'].append((label, 'variance'))\n",
+        "            else:\n",
+        "                impact['averagely_impacted'].append((label, 'variance'))\n",
+        "\n",
+        "    # Classify inter-cluster metric changes\n",
+        "    for pair in inter_metrics_original:\n",
+        "        if pair in inter_metrics_finetuned:\n",
+        "            distance_change = inter_metrics_finetuned[pair] - inter_metrics_original[pair]\n",
+        "            if distance_change < -threshold:\n",
+        "                impact['positively_impacted'].append((pair, 'inter_cluster_distance'))\n",
+        "            elif distance_change > threshold:\n",
+        "                impact['negatively_impacted'].append((pair, 'inter_cluster_distance'))\n",
+        "            else:\n",
+        "                impact['averagely_impacted'].append((pair, 'inter_cluster_distance'))\n",
+        "\n",
+        "    return impact\n",
+        "\n",
+        "# Assuming metrics_original and metrics_finetuned are dictionaries containing the calculated metrics\n",
+        "impact = classify_impact(metrics_original, metrics_finetuned, inter_cluster_metrics_original, inter_cluster_metrics_finetuned)\n",
+        "\n",
+        "# Print the impact classification\n",
+        "print(f\"Positively Impacted Faces: {len(impact['positively_impacted'])}\")\n",
+        "print(f\"Negatively Impacted Faces: {len(impact['negatively_impacted'])}\")\n",
+        "print(f\"Averagely Impacted Faces: {len(impact['averagely_impacted'])}\")\n",
+        "\n",
+        "# Optional: Print detailed impact for each face\n",
+        "# for category, changes in impact.items():\n",
+        "#     print(f\"\\n{category.capitalize()} Faces:\")\n",
+        "#     for change in changes:\n",
+        "#         print(change)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
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diff --git a/Model-Training/eval-mark-I.py b/Model-Training/eval-mark-I.py
new file mode 100644
index 0000000..17d9aa0
--- /dev/null
+++ b/Model-Training/eval-mark-I.py
@@ -0,0 +1,78 @@
+import os
+import numpy as np
+from keras.models import load_model
+from keras.preprocessing import image
+from sklearn.metrics.pairwise import euclidean_distances
+
+# Function to load and preprocess images
+def load_and_preprocess_image(img_path, target_size=(160, 160)):
+    img = image.load_img(img_path, target_size=target_size)
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array /= 255.0
+    return img_array
+
+# Function to generate embeddings
+def generate_embeddings(model, dataset_path):
+    embeddings = []
+    labels = []
+    
+    for class_name in os.listdir(dataset_path):
+        class_path = os.path.join(dataset_path, class_name)
+        if not os.path.isdir(class_path):
+            continue
+        
+        for img_name in os.listdir(class_path):
+            img_path = os.path.join(class_path, img_name)
+            img_array = load_and_preprocess_image(img_path)
+            embedding = model.predict(img_array)
+            embeddings.append(embedding[0])
+            labels.append(class_name)
+    
+    embeddings = np.array(embeddings)
+    labels = np.array(labels)
+    return embeddings, labels
+
+# Function to calculate intra-cluster distances
+def calculate_intra_cluster_distances(embeddings, labels):
+    unique_labels = np.unique(labels)
+    distances = []
+
+    for label in unique_labels:
+        cluster_embeddings = embeddings[labels == label]
+        avg_embedding = np.mean(cluster_embeddings, axis=0)
+        max_distance = np.max(euclidean_distances(cluster_embeddings, [avg_embedding]))
+        distances.append(max_distance)
+    
+    return np.array(distances)
+
+# Load the pre-trained FaceNet model (replace 'facenet_model.h5' with your model file)
+model_path = 'facenet_model.h5'
+model = load_model(model_path)
+
+# Path to the dataset
+dataset_path = 'path_to_your_dataset'
+
+# Generate embeddings for the original model
+embeddings_original, labels = generate_embeddings(model, dataset_path)
+
+# Load the fine-tuned model (replace 'facenet_model_finetuned.h5' with your fine-tuned model file)
+finetuned_model_path = 'facenet_model_finetuned.h5'
+finetuned_model = load_model(finetuned_model_path)
+
+# Generate embeddings for the fine-tuned model
+embeddings_finetuned, _ = generate_embeddings(finetuned_model, dataset_path)
+
+# Calculate intra-cluster distances for both models
+intra_distances_original = calculate_intra_cluster_distances(embeddings_original, labels)
+intra_distances_finetuned = calculate_intra_cluster_distances(embeddings_finetuned, labels)
+
+# Compare intra-cluster distances
+intra_distance_change = intra_distances_finetuned - intra_distances_original
+
+# Output the results
+print(f"Intra-Cluster Distance Change: {intra_distance_change}")
+print(f"Mean Distance Change: {np.mean(intra_distance_change)}")
+print(f"Positive Impact: {np.sum(intra_distance_change < 0)}")
+print(f"Negative Impact: {np.sum(intra_distance_change > 0)}")
+print(f"Average Impact: {np.sum(intra_distance_change == 0)}")
diff --git a/Model-Training/facerec_indianfaces_comapred.ipynb b/Model-Training/facerec_indianfaces_comapred.ipynb
new file mode 100644
index 0000000..cda7cca
--- /dev/null
+++ b/Model-Training/facerec_indianfaces_comapred.ipynb
@@ -0,0 +1 @@
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installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current 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install deepface","metadata":{"execution":{"iopub.status.busy":"2024-07-21T14:22:00.754819Z","iopub.execute_input":"2024-07-21T14:22:00.755356Z","iopub.status.idle":"2024-07-21T14:22:20.307083Z","shell.execute_reply.started":"2024-07-21T14:22:00.755325Z","shell.execute_reply":"2024-07-21T14:22:20.305955Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Collecting deepface\n  Downloading deepface-0.0.92-py3-none-any.whl.metadata (27 kB)\nRequirement already satisfied: requests>=2.27.1 in /opt/conda/lib/python3.10/site-packages (from deepface) (2.32.3)\nRequirement already satisfied: numpy>=1.14.0 in /opt/conda/lib/python3.10/site-packages (from deepface) (1.26.4)\nRequirement already satisfied: pandas>=0.23.4 in /opt/conda/lib/python3.10/site-packages (from deepface) (2.2.2)\nCollecting gdown>=3.10.1 (from deepface)\n  Downloading gdown-5.2.0-py3-none-any.whl.metadata (5.8 kB)\nRequirement already satisfied: tqdm>=4.30.0 in 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/opt/conda/lib/python3.10/site-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (1.2.0)\nRequirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (3.5.2)\nRequirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (0.7.2)\nRequirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.10/site-packages (from beautifulsoup4->gdown>=3.10.1->deepface) (2.5)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging->gunicorn>=20.1.0->deepface) (3.1.1)\nRequirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown>=3.10.1->deepface) (1.7.1)\nRequirement already satisfied: cachetools<6.0,>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from 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requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow>=1.9.0->deepface) (3.2.2)\nRequirement already satisfied: markdown-it-py>=2.2.0 in /opt/conda/lib/python3.10/site-packages (from rich->keras>=2.2.0->deepface) (3.0.0)\nRequirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/conda/lib/python3.10/site-packages (from rich->keras>=2.2.0->deepface) (2.17.2)\nRequirement already satisfied: mdurl~=0.1 in /opt/conda/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->keras>=2.2.0->deepface) (0.1.2)\nDownloading deepface-0.0.92-py3-none-any.whl (105 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.5/105.5 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading gdown-5.2.0-py3-none-any.whl (18 kB)\nDownloading gunicorn-22.0.0-py3-none-any.whl (84 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.4/84.4 kB\u001b[0m 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fire\nInstalling collected packages: keras, fire, mtcnn, gunicorn, gdown, retina-face, deepface\n  Attempting uninstall: keras\n    Found existing installation: keras 3.4.1\n    Uninstalling keras-3.4.1:\n      Successfully uninstalled keras-3.4.1\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\ntensorflow-decision-forests 1.8.1 requires wurlitzer, which is not installed.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed deepface-0.0.92 fire-0.6.0 gdown-5.2.0 gunicorn-22.0.0 keras-2.15.0 mtcnn-0.1.1 retina-face-0.0.17\n","output_type":"stream"}]},{"cell_type":"code","source":"\nimport os\nimport shutil\ncount=0\npath = \"/kaggle/input/indian-faces-image-classification/Image_Train\"\nfor folder in os.listdir(path):\n    if len(os.listdir(f'{path}/{folder}')) < 5:\n        shutil.rmtree(f'{path}/{folder}')\n        count+=1\nprint(f\"Removed {count} folders\")\nprint(f\"Remaining folders: {len(os.listdir(path))}\")","metadata":{"execution":{"iopub.status.busy":"2024-07-21T14:23:25.750583Z","iopub.execute_input":"2024-07-21T14:23:25.751326Z","iopub.status.idle":"2024-07-21T14:23:25.768627Z","shell.execute_reply.started":"2024-07-21T14:23:25.751291Z","shell.execute_reply":"2024-07-21T14:23:25.767752Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Removed 0 folders\nRemaining folders: 16\n","output_type":"stream"}]},{"cell_type":"code","source":"import os\nimport numpy as np\nfrom tqdm import tqdm_notebook\nfrom keras.models import load_model\nfrom keras.preprocessing import image\nfrom sklearn.metrics.pairwise import euclidean_distances\nfrom deepface import DeepFace\nimport tensorflow as tf\nimport cv2\n\n# Function to load and preprocess images\ndef load_and_preprocess_image(img_path, target_size=(160, 160), extract=False):\n    \n    if extract:\n        img_array = DeepFace.extract_faces(img_path, enforce_detection=False)[0]['face']\n        img_array = cv2.resize(img_array, target_size, interpolation=cv2.INTER_AREA)\n    else:\n        img = image.load_img(img_path, target_size=target_size)\n        img_array = image.img_to_array(img)\n    img_array = np.expand_dims(img_array, axis=0)\n    img_array /= 255.0\n    return img_array\n\n# Function to generate embeddings\ndef generate_embeddings(model, dataset_path, deepface = False):\n    embeddings = {}\n\n    for class_name in tqdm_notebook(os.listdir(dataset_path)):\n        class_path = os.path.join(dataset_path, class_name)\n        if not os.path.isdir(class_path):\n            continue\n        embeddings[class_name] = []\n\n        for img_name in os.listdir(class_path):\n            img_path = os.path.join(class_path, img_name)\n            img_array = load_and_preprocess_image(img_path, extract=True)\n            if deepface:\n                embedding = DeepFace.represent(img_path=img_path, model_name = \"Facenet512\", enforce_detection=False)[0]['embedding']\n            else:\n                embedding = model.predict(img_array, verbose=0)\n            embeddings[class_name].append(embedding)\n        embeddings[class_name] = np.array(embeddings[class_name])\n\n\n\n    return embeddings\n\n# Function to calculate intra-cluster distances\ndef calculate_intra_cluster_metrics(embeddings):\n    metrics = {}\n\n    for label, cluster_embeddings in embeddings.items():\n        cluster_embeddings = np.squeeze(cluster_embeddings, axis=1)\n\n        avg_embedding = np.mean(cluster_embeddings, axis=0)\n        distances = euclidean_distances(cluster_embeddings, [avg_embedding])\n        max_distance = np.max(distances)\n        min_distance = np.min(distances)\n        variance = np.var(distances)\n        standard_deviation = np.std(distances)\n        percentile_25 = np.percentile(distances, 25)\n        percentile_50 = np.percentile(distances, 50)\n        percentile_75 = np.percentile(distances, 75)\n\n        metrics[label] = {\"center\": avg_embedding, \"max_radius\": max_distance, \"min_radius\":min_distance, \"num_samples\": len(cluster_embeddings), \"variance\": variance, \"standard_deviation\": standard_deviation, \"percentile_25\": percentile_25, \"percentile_50\": percentile_50, \"percentile_75\": percentile_75}\n\n    return metrics","metadata":{"execution":{"iopub.status.busy":"2024-07-21T15:21:43.42234Z","iopub.execute_input":"2024-07-21T15:21:43.422726Z","iopub.status.idle":"2024-07-21T15:21:43.438046Z","shell.execute_reply.started":"2024-07-21T15:21:43.422694Z","shell.execute_reply":"2024-07-21T15:21:43.437048Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"\n# Path to the dataset\ndataset_path = '/kaggle/input/indian-faces-image-classification/Image_Train'\nmodel=1\n# Generate embeddings for the original model\nembeddings_original = generate_embeddings(model, dataset_path, True)","metadata":{"execution":{"iopub.status.busy":"2024-07-21T14:26:08.023908Z","iopub.execute_input":"2024-07-21T14:26:08.02536Z","iopub.status.idle":"2024-07-21T14:27:30.271108Z","shell.execute_reply.started":"2024-07-21T14:26:08.02532Z","shell.execute_reply":"2024-07-21T14:27:30.270199Z"},"trusted":true},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/16 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"097befe6ba5a4b5a85a9aa172798b153"}},"metadata":{}},{"name":"stdout","text":"24-07-21 14:26:11 - facenet512_weights.h5 will be downloaded...\n","output_type":"stream"},{"name":"stderr","text":"Downloading...\nFrom: https://github.com/serengil/deepface_models/releases/download/v1.0/facenet512_weights.h5\nTo: /root/.deepface/weights/facenet512_weights.h5\n\n  0%|          | 0.00/95.0M [00:00<?, ?B/s]\u001b[A\n 30%|███       | 28.8M/95.0M [00:00<00:00, 288MB/s]\u001b[A\n100%|██████████| 95.0M/95.0M [00:00<00:00, 419MB/s]\u001b[A\n","output_type":"stream"}]},{"cell_type":"code","source":"!wget https://huggingface.co/DnD11/FaceNet_Finetuned/resolve/main/embedding_trial2.h5","metadata":{"execution":{"iopub.status.busy":"2024-07-21T14:27:30.272586Z","iopub.execute_input":"2024-07-21T14:27:30.272858Z","iopub.status.idle":"2024-07-21T14:27:32.899872Z","shell.execute_reply.started":"2024-07-21T14:27:30.272834Z","shell.execute_reply":"2024-07-21T14:27:32.89895Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"--2024-07-21 14:27:31--  https://huggingface.co/DnD11/FaceNet_Finetuned/resolve/main/embedding_trial2.h5\nResolving huggingface.co (huggingface.co)... 3.163.189.37, 3.163.189.90, 3.163.189.74, ...\nConnecting to huggingface.co (huggingface.co)|3.163.189.37|:443... connected.\nHTTP request sent, awaiting response... 302 Found\nLocation: https://cdn-lfs-us-1.huggingface.co/repos/03/4f/034f80180b5ba08a0d9fe97aa18e17eb02604e9ba5c3e985eaee5a613e4810f6/901871f1a8502c1e168ce190908e8c5658f350ab7f0626932ab6cea57f67c9be?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27embedding_trial2.h5%3B+filename%3D%22embedding_trial2.h5%22%3B&Expires=1721831251&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyMTgzMTI1MX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzAzLzRmLzAzNGY4MDE4MGI1YmEwOGEwZDlmZTk3YWExOGUxN2ViMDI2MDRlOWJhNWMzZTk4NWVhZWU1YTYxM2U0ODEwZjYvOTAxODcxZjFhODUwMmMxZTE2OGNlMTkwOTA4ZThjNTY1OGYzNTBhYjdmMDYyNjkzMmFiNmNlYTU3ZjY3YzliZT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=RTUO6SbNNSXWV-dqp2ebxeqNu3rJtAcDkjCw8-nx-UsES2HpeguoWe9DeH4hZhn6FaOqOydMOUFGVSy%7Eqpie9ZjoGxuwQmarElPqpV6rpS5x6etU10K%7EBRTF8Z1FncWo2yokyR5v56sQKAcfDZcUPTtqbPV5w3klNodYNF5-Nn8ZcYqWk5qd7cIrFL2ggV1TWS775zo5yKzaVd18f%7EmGcCvLFVwE8cc7Wly0OuMt4CWYBfPL%7EgZbpefFTtvSaaIGXsyBdmC0nKrvHii8OAvUOCuHWZQSOUxfU6jxqtk0rWG0LkOSHWkI-hrSogtek8OcZ23CPjTQp%7Erg1ci2lM6L%7Eg__&Key-Pair-Id=K24J24Z295AEI9 [following]\n--2024-07-21 14:27:31--  https://cdn-lfs-us-1.huggingface.co/repos/03/4f/034f80180b5ba08a0d9fe97aa18e17eb02604e9ba5c3e985eaee5a613e4810f6/901871f1a8502c1e168ce190908e8c5658f350ab7f0626932ab6cea57f67c9be?response-content-disposition=inline%3B+filename*%3DUTF-8''embedding_trial2.h5%3B+filename%3D%22embedding_trial2.h5%22%3B&Expires=1721831251&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyMTgzMTI1MX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzAzLzRmLzAzNGY4MDE4MGI1YmEwOGEwZDlmZTk3YWExOGUxN2ViMDI2MDRlOWJhNWMzZTk4NWVhZWU1YTYxM2U0ODEwZjYvOTAxODcxZjFhODUwMmMxZTE2OGNlMTkwOTA4ZThjNTY1OGYzNTBhYjdmMDYyNjkzMmFiNmNlYTU3ZjY3YzliZT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=RTUO6SbNNSXWV-dqp2ebxeqNu3rJtAcDkjCw8-nx-UsES2HpeguoWe9DeH4hZhn6FaOqOydMOUFGVSy~qpie9ZjoGxuwQmarElPqpV6rpS5x6etU10K~BRTF8Z1FncWo2yokyR5v56sQKAcfDZcUPTtqbPV5w3klNodYNF5-Nn8ZcYqWk5qd7cIrFL2ggV1TWS775zo5yKzaVd18f~mGcCvLFVwE8cc7Wly0OuMt4CWYBfPL~gZbpefFTtvSaaIGXsyBdmC0nKrvHii8OAvUOCuHWZQSOUxfU6jxqtk0rWG0LkOSHWkI-hrSogtek8OcZ23CPjTQp~rg1ci2lM6L~g__&Key-Pair-Id=K24J24Z295AEI9\nResolving cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)... 3.163.189.91, 3.163.189.127, 3.163.189.28, ...\nConnecting to cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)|3.163.189.91|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 94732496 (90M) [binary/octet-stream]\nSaving to: 'embedding_trial2.h5'\n\nembedding_trial2.h5 100%[===================>]  90.34M  78.1MB/s    in 1.2s    \n\n2024-07-21 14:27:32 (78.1 MB/s) - 'embedding_trial2.h5' saved [94732496/94732496]\n\n","output_type":"stream"}]},{"cell_type":"code","source":"\n# Load the fine-tuned model\nimport tensorflow as tf\nfinetuned_model = tf.keras.models.load_model('/kaggle/working/embedding_trial2.h5')\nembeddings_finetuned = generate_embeddings(finetuned_model, dataset_path)","metadata":{"execution":{"iopub.status.busy":"2024-07-21T15:15:13.455875Z","iopub.execute_input":"2024-07-21T15:15:13.456621Z","iopub.status.idle":"2024-07-21T15:21:08.703036Z","shell.execute_reply.started":"2024-07-21T15:15:13.456589Z","shell.execute_reply":"2024-07-21T15:21:08.701946Z"},"trusted":true},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/16 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"adc8a730e1d7404c95dadc21a6d5ff2c"}},"metadata":{}}]},{"cell_type":"code","source":"\n# Calculate intra-cluster distances for both models\nmetrics_original = calculate_intra_cluster_metrics(embeddings_original)\n# ","metadata":{"execution":{"iopub.status.busy":"2024-07-21T15:21:26.057772Z","iopub.execute_input":"2024-07-21T15:21:26.058385Z","iopub.status.idle":"2024-07-21T15:21:26.077871Z","shell.execute_reply.started":"2024-07-21T15:21:26.058353Z","shell.execute_reply":"2024-07-21T15:21:26.077167Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"metrics_finetuned = calculate_intra_cluster_metrics(embeddings_finetuned)","metadata":{"execution":{"iopub.status.busy":"2024-07-21T15:21:47.308651Z","iopub.execute_input":"2024-07-21T15:21:47.309426Z","iopub.status.idle":"2024-07-21T15:21:47.329496Z","shell.execute_reply.started":"2024-07-21T15:21:47.309393Z","shell.execute_reply":"2024-07-21T15:21:47.328731Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\ndef plot_histogram(metrics, key, title, xlabel):\n    values = [metrics[label][key] for label in metrics]\n    plt.figure()\n    plt.hist(values, bins=20, alpha=0.7)\n    plt.title(title)\n    plt.xlabel(xlabel)\n    plt.ylabel('Frequency')\n    plt.show()\n\nplot_histogram(metrics_original, 'max_radius', 'Histogram of Max Radius (Original Model)', 'Max Radius')\nplot_histogram(metrics_finetuned, 'max_radius', 'Histogram of Max Radius (Fine-tuned Model)', 'Max Radius')","metadata":{"execution":{"iopub.status.busy":"2024-07-21T15:21:49.214264Z","iopub.execute_input":"2024-07-21T15:21:49.214628Z","iopub.status.idle":"2024-07-21T15:21:49.837735Z","shell.execute_reply.started":"2024-07-21T15:21:49.214602Z","shell.execute_reply":"2024-07-21T15:21:49.836789Z"},"trusted":true},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}}]},{"cell_type":"code","source":"def plot_boxplot(metrics, key, title, ylabel):\n    values = [metrics[label][key] for label in metrics]\n    plt.figure()\n    plt.boxplot(values)\n    plt.title(title)\n    plt.ylabel(ylabel)\n    plt.show()\n\nplot_boxplot(metrics_original, 'percentile_50', 'Boxplot of Median Distances (Original Model)', 'Median Distance')\nplot_boxplot(metrics_finetuned, 'percentile_50', 'Boxplot of Median Distances (Fine-tuned Model)', 'Median Distance')","metadata":{"execution":{"iopub.status.busy":"2024-07-21T15:21:58.907446Z","iopub.execute_input":"2024-07-21T15:21:58.908328Z","iopub.status.idle":"2024-07-21T15:21:59.355775Z","shell.execute_reply.started":"2024-07-21T15:21:58.908295Z","shell.execute_reply":"2024-07-21T15:21:59.354892Z"},"trusted":true},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAjcAAAGzCAYAAADT4Tb9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABBBklEQVR4nO3deVwVZf//8fcR8IhsaopAIeCSILmk3pZLKrml5lKJWyZoemtappndUre7ZnrnlpWmlgumZeFtZuWCueTSnWmambjihrgraCIazO+PfpxvR0A5ih4cX8/H4zz0XHPNzGcOh3PezFwzYzEMwxAAAIBJFHJ2AQAAAPmJcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcIMCx2KxaPjw4c4uw86WLVtUp04deXh4yGKxaPv27c4uKZuGDRuqYcOGtueHDh2SxWLRnDlznFaTI66vH7m7dOmSfH199emnn96xdURHRys4OPiW5h0+fLgsFkv+FnSdtWvXymKxaO3atXd0PbfjVj/LcvrdHTx4sB577LH8K87kCDf3kTlz5shisdg9fH19FRERoe+++87Z5d2233//XcOHD9ehQ4fydbnXrl1TZGSkzp07p0mTJik2NlZBQUE59s36wLVYLJo/f36OferWrSuLxaJHHnkkX+ssSKKjo+3eZ56enipbtqzatWunuLg4ZWZm5st6Nm3apOHDh+vChQv5srx7xZQpU+Tl5aWOHTtmm7Zx40Y988wzKl26tKxWq4KDg9WrVy8dOXLECZU6398/9zZs2JBtumEYCgwMlMVi0dNPP+2ECvOmf//+2rFjh5YuXersUu4JhJv70MiRIxUbG6t58+bpjTfe0OnTp9WiRQstW7bM2aXdlt9//10jRozI93Bz4MABHT58WK+//rr++c9/qkuXLipevPgN5ylSpIgWLFiQrf3QoUPatGmTihQpkq815iQoKEhpaWl64YUX7vi6cmK1WhUbG6vY2FhNmjRJnTt31r59+9SuXTs1atRIqampdv1XrlyplStXOrSOTZs2acSIEfdVuLl27ZqmTJmiHj16yMXFxW7a1KlT9cQTT2jnzp165ZVX9OGHH6pdu3b6/PPPVaVKFW3atCnP65k5c6b27NlzSzX++9//Vlpa2i3Ne6fk9ju5bt06HTt2TFar1QlV5Z2fn5/atGmjd99919ml3BNcnV0A7r7mzZurZs2atucvvviiSpcurYULFxbov1yc5dSpU5KkYsWK5XmeFi1aaOnSpTpz5oxKlixpa1+wYIFKly6tChUq6Pz58/ldqh2LxXJXQlRuXF1d1aVLF7u20aNH65133lFMTIx69uypzz//3DatcOHCd7vEe9KyZct0+vRptW/f3q5948aN6t+/v+rVq6fly5eraNGitmkvvfSS6tatq3bt2mnXrl03DOd//PGHPDw85Obmdss1urq6ytW1YH29tGjRQl988YXee+89u9oWLFigGjVq6MyZM06sLm/at2+vyMhIHTx4UGXLlnV2OQUae26gYsWKyd3dPduH0R9//KGBAwcqMDBQVqtVFStW1LvvvqusG8mnpaUpNDRUoaGhdn+lnTt3Tv7+/qpTp44yMjIk/XWYwtPTUwcPHlSzZs3k4eGhgIAAjRw5Unm5Mf0vv/yi5s2by9vbW56enmrUqJF+/PFH2/Q5c+YoMjJSkhQREWHbDX2z4/Hff/+9nnjiCXl4eKhYsWJq06aNdu/ebZseHR2tBg0aSJIiIyNlsVjyNC6kTZs2slqt+uKLL+zaFyxYoPbt22f7izvL/PnzVaNGDbm7u6tEiRLq2LGjjh49mq3fjBkzVK5cObm7u6tWrVr64YcfsvXJ6bj9r7/+qujoaJUtW1ZFihSRn5+funfvrrNnz9rNmzVmYv/+/YqOjlaxYsXk4+Ojbt266fLlyzfd/hsZPHiwmjZtqi+++EJ79+61tec05mbq1KkKDw9X0aJFVbx4cdWsWdP21/fw4cM1aNAgSVJISIjtZ56152727Nl68skn5evrK6vVqkqVKmnatGnZ6gkODtbTTz+tDRs2qFatWipSpIjKli2refPmZet74cIFDRgwQMHBwbJarXrooYfUtWtXuy/G9PR0DRs2TOXLl5fValVgYKDeeOMNpaen2y1r1apVqlevnooVKyZPT09VrFhRb7755k1fvyVLlig4OFjlypWzax81apQsFovmzp1rF2wkqVy5cho/frySk5P10Ucf2dqzfi8PHDigFi1ayMvLS88//7xt2vVjbs6ePasXXnhB3t7eKlasmKKiorRjx45s77OcxtxYLBa9/PLLWrJkiR555BFZrVaFh4dr+fLldv0OHz6sPn36qGLFinJ3d9cDDzygyMjI294j26lTJ509e1arVq2ytV29elVffvmlOnfunOM8N/sMzJKenq4BAwaoVKlS8vLyUuvWrXXs2LEcl5mUlKTu3bvbDhuGh4frk08+ydM2NG7cWJL01Vdf5an//Yxwcx9KSUnRmTNndPr0ae3atUsvvfSSLl26ZPdXtmEYat26tSZNmqSnnnpKEydOVMWKFTVo0CC99tprkiR3d3fNnTtX+/fv11tvvWWbt2/fvkpJSdGcOXPsvsQzMjL01FNPqXTp0ho/frxq1KihYcOGadiwYTesd9euXXriiSe0Y8cOvfHGGxoyZIgSExPVsGFD/e9//5Mk1a9fX/369ZMkvfnmm7bDIWFhYbkuNz4+Xs2aNdOpU6c0fPhwvfbaa9q0aZPq1q1r+yDt1auX7QunX79+io2NtdvW3BQtWlRt2rTRwoULbW07duzQrl27cv0gHTNmjLp27aoKFSpo4sSJ6t+/v1avXq369evbHXb5+OOP1atXL/n5+Wn8+PGqW7euWrdunWMIut6qVat08OBBdevWTVOnTlXHjh312WefqUWLFjmGzPbt2+vixYsaO3as2rdvrzlz5mjEiBE3Xc/NvPDCCzIMw+6L5nozZ85Uv379VKlSJU2ePFkjRoxQtWrVbD/zZ599Vp06dZIk21io2NhYlSpVSpI0bdo0BQUF6c0339SECRMUGBioPn366IMPPsi2rv3796tdu3Zq0qSJJkyYoOLFiys6Olq7du2y9bl06ZKeeOIJTZ06VU2bNtWUKVPUu3dvJSQk2L7IMjMz1bp1a7377rtq1aqVpk6dqrZt22rSpEnq0KGDbVm7du3S008/rfT0dI0cOVITJkxQ69attXHjxpu+dps2bVL16tXt2i5fvqzVq1friSeeUEhISI7zdejQQVarNdvh5z///FPNmjWTr6+v3n33XT333HM5zp+ZmalWrVpp4cKFioqK0pgxY5ScnKyoqKib1pxlw4YN6tOnjzp27Kjx48frypUreu655+zC9ZYtW7Rp0yZ17NhR7733nnr37q3Vq1erYcOGtxWsg4ODVbt2bbvfye+++04pKSk5jl3Ky2dglh49emjy5Mlq2rSp3nnnHbm5ually5bZlnny5Ek9/vjjio+P18svv6wpU6aofPnyevHFFzV58uSbboOPj4/KlSuXp/fJfc/AfWP27NmGpGwPq9VqzJkzx67vkiVLDEnG6NGj7drbtWtnWCwWY//+/ba2mJgYo1ChQsb69euNL774wpBkTJ482W6+qKgoQ5Lxyiuv2NoyMzONli1bGoULFzZOnz5ta5dkDBs2zPa8bdu2RuHChY0DBw7Y2o4fP254eXkZ9evXt7VlrXvNmjV5ej2qVatm+Pr6GmfPnrW17dixwyhUqJDRtWtXW9uaNWsMScYXX3xx02X+ve+yZcsMi8ViHDlyxDAMwxg0aJBRtmxZwzAMo0GDBkZ4eLhtvkOHDhkuLi7GmDFj7Ja3c+dOw9XV1dZ+9epVw9fX16hWrZqRnp5u6zdjxgxDktGgQQNbW2JioiHJmD17tq3t8uXL2WpeuHChIclYv369rW3YsGGGJKN79+52fZ955hnjgQceuOnrEBUVZXh4eOQ6/ZdffjEkGQMGDLC1NWjQwK7+Nm3a2L1GOfnPf/5jSDISExOzTctpW5s1a2b7GWQJCgrKtv2nTp0yrFarMXDgQFvb0KFDDUnG4sWLsy03MzPTMAzDiI2NNQoVKmT88MMPdtOnT59uSDI2btxoGIZhTJo0yZBk977Pi2vXrhkWi8WuLsMwjO3btxuSjFdfffWG81epUsUoUaKE7XnW7+XgwYOz9Y2KijKCgoJsz+Pi4rL9bmdkZBhPPvlktvdZ1vvn7yQZhQsXtvvs2LFjhyHJmDp1qq0tp5/b5s2bDUnGvHnzbG1Zv2s3+33P+tzbsmWL8f777xteXl62dURGRhoRERGGYfz1PmjZsqVtvrx+Bma99n369LHr17lz52yfZS+++KLh7+9vnDlzxq5vx44dDR8fH1tdOf3uZmnatKkRFhZ2w22GYbDn5j70wQcfaNWqVVq1apXmz5+viIgI9ejRQ4sXL7b1+fbbb+Xi4mLbG5Jl4MCBMgzD7uyq4cOHKzw8XFFRUerTp48aNGiQbb4sL7/8su3/Wbupr169qvj4+Bz7Z2RkaOXKlWrbtq3dMWZ/f3917txZGzZsyDYwNS+Sk5O1fft2RUdHq0SJErb2KlWqqEmTJvr2228dXub1mjZtqhIlSuizzz6TYRj67LPPbHsarrd48WJlZmaqffv2OnPmjO3h5+enChUqaM2aNZKkn3/+WadOnVLv3r3txqhER0fLx8fnpjW5u7vb/n/lyhWdOXNGjz/+uCRp27Zt2fr37t3b7vkTTzyhs2fP3tJr/neenp6SpIsXL+bap1ixYjp27Ji2bNlyS+v4+7Zm7a1s0KCBDh48qJSUFLu+lSpV0hNPPGF7XqpUKVWsWFEHDx60tcXFxalq1ap65plnsq0r6xDMF198obCwMIWGhtr9HJ988klJsv0cs8ZvffXVVw6dOXbu3DkZhpFtzEzW6+jl5XXD+b28vHL82b300ks3Xffy5cvl5uamnj172toKFSqkvn375qV0SX8dVvn74bQqVarI29vb7nX++8/t2rVrOnv2rMqXL69ixYrl+B51RPv27ZWWlqZly5bp4sWLWrZsWa57UvP6GZj1WXF9v/79+9s9NwxDcXFxatWqlQzDsHt/NGvWTCkpKXnavuLFi98T44OcjXBzH6pVq5YaN26sxo0b6/nnn9c333yjSpUq2YKG9Ndx74CAgGwfllmHeQ4fPmxrK1y4sD755BMlJibq4sWLmj17do7XuChUqFC2QXAPP/ywJOV6PP306dO6fPmyKlasmG1aWFiYMjMz83Q45npZ9ee23DNnzuiPP/5weLl/5+bmpsjISC1YsEDr16/X0aNHc/0g3bdvnwzDUIUKFVSqVCm7x+7du22DmrPqrlChQrZ15WWA4blz5/Tqq6+qdOnScnd3V6lSpWyHMa7/wpekMmXK2D3P+lK93cHQly5dknTjL+N//etf8vT0VK1atVShQgX17dvXod3xGzduVOPGjW3jqUqVKmU7xHj9tl6/ndJf2/r37Txw4MBNT9/ft2+fdu3ale1nmPU+z/o5dujQQXXr1lWPHj1UunRpdezYUYsWLcpz0DGuO4SY9TreKCxmTb/+NXd1ddVDDz1003UePnxY/v7+2cbzlC9fPi8lS8rb65yWlqahQ4faxrmULFlSpUqV0oULF3J8jzqiVKlSaty4sRYsWKDFixcrIyND7dq1y7FvXj8DDx8+rEKFCmUbA3X9Z8vp06d14cIFzZgxI9v7o1u3bpL+7/1xI4Zh3PFrCJlBwRrODqcoVKiQIiIiNGXKFO3bt0/h4eEOL2PFihWS/tobsG/fvlyP+99vOnfurOnTp2v48OGqWrWqKlWqlGO/zMxMWSwWfffddzkONs7a03G72rdvr02bNmnQoEGqVq2aPD09lZmZqaeeeirHL9bcBj5f/+XqqN9++03Sjb8Yw8LCtGfPHi1btkzLly9XXFycPvzwQw0dOvSm434OHDigRo0aKTQ0VBMnTlRgYKAKFy6sb7/9VpMmTcq2rfm1nZmZmapcubImTpyY4/TAwEBJf+2dWL9+vdasWaNvvvlGy5cv1+eff64nn3xSK1euzLWeEiVKyGKxZAuX5cuXl6urq3799ddca0tPT9eePXvszpSU/jplv1Chu/N3bl5e51deeUWzZ89W//79Vbt2bfn4+Mhisahjx475cn2kzp07q2fPnjpx4oSaN2/u0FmQtyOr9i5duuQ6TqlKlSo3Xc758+ftzsBEzgg3kPTXoELp//6iDgoKUnx8fLa/9BISEmzTs/z6668aOXKkunXrpu3bt6tHjx7auXNntsMkmZmZOnjwoO2vWEm2s2VyuxJqqVKlVLRo0Ryvt5GQkKBChQrZvjAc+Wsmq/7clluyZEl5eHjkeXm5qVevnsqUKaO1a9dq3LhxufYrV66cDMNQSEiI3euTW9379u2zHeqQ/tp9n5iYqKpVq+Y67/nz57V69WqNGDFCQ4cOtbXv27fPkU3KF7GxsbJYLGrSpMkN+3l4eKhDhw7q0KGDrl69qmeffVZjxoxRTEyMihQpkuvP/Ouvv1Z6erqWLl1qt7cg67DQrShXrpwtlN2oz44dO9SoUaObvh8LFSqkRo0aqVGjRpo4caLefvttvfXWW1qzZo3trJjrubq6qly5ckpMTLRr9/DwUEREhL7//nsdPnw4x4tMLlq0SOnp6bd8uYegoCCtWbNGly9fttt7s3///ltaXm6+/PJLRUVFacKECba2K1eu5Nu1jJ555hn16tVLP/74o92lCK6X18/AoKAgZWZm6sCBA3Z7a67/bMk6kyojIyPXn29e3Oz3HH/hsBR07do1rVy5UoULF7btcm3RooUyMjL0/vvv2/WdNGmSLBaLmjdvbps3OjpaAQEBmjJliubMmaOTJ09qwIABOa7r78szDEPvv/++3Nzc1KhRoxz7u7i4qGnTpvrqq6/sDl2dPHlSCxYsUL169eTt7S1JtjCSlw9Bf39/VatWTXPnzrXr/9tvv2nlypVq0aLFTZeRFxaLRe+9956GDRt2w4vpPfvss3JxcdGIESOy7S0wDMN2NknNmjVVqlQpTZ8+3XYIUfrrVPibbXfWX83XLz8vZ2nkp3feeUcrV65Uhw4dsh1e+7vrT08vXLiwKlWqJMMwdO3aNUm5/8xz2taUlBTNnj37lut+7rnntGPHDv33v//NNi1rPe3bt1dSUpJmzpyZrU9aWprtUOe5c+eyTa9WrZokZTtl/Hq1a9fWzz//nK393//+twzDUHR0dLYL6CUmJuqNN96Qv7+/evXqdcPl56ZZs2a6du2a3bZlZmbmePbZ7XBxccn2Hp06dartshK3y9PTU9OmTdPw4cPVqlWrXPvl9TMw69/33nvPrt/1v1cuLi567rnnFBcXl2NIPn369E1rT0lJ0YEDB1SnTp2b9r3fsefmPvTdd9/Z/vo4deqUFixYoH379mnw4MG2oNCqVStFRETorbfe0qFDh1S1alWtXLlSX331lfr37287vjx69Ght375dq1evlpeXl6pUqaKhQ4fq3//+t9q1a2cXEooUKaLly5crKipKjz32mL777jt98803evPNN22n7+Zk9OjRtmuC9OnTR66urvroo4+Unp6u8ePH2/pVq1ZNLi4uGjdunFJSUmS1Wm3XOcnJf/7zHzVv3ly1a9fWiy++qLS0NE2dOlU+Pj75em+rNm3aqE2bNjfsU65cOY0ePVoxMTE6dOiQ2rZtKy8vLyUmJuq///2v/vnPf+r111+Xm5ubRo8erV69eunJJ59Uhw4dlJiYqNmzZ990zI23t7fq16+v8ePH69q1a3rwwQe1cuXKbHsB8suff/5puwXFlStXdPjwYS1dulS//vqrIiIiNGPGjBvO37RpU/n5+alu3boqXbq0du/erffff18tW7a0/SVdo0YNSdJbb72ljh07ys3NTa1atVLTpk1VuHBhtWrVSr169dKlS5c0c+ZM+fr6Kjk5+Za2Z9CgQfryyy8VGRmp7t27q0aNGjp37pyWLl2q6dOnq2rVqnrhhRe0aNEi9e7dW2vWrFHdunWVkZGhhIQELVq0SCtWrFDNmjU1cuRIrV+/Xi1btlRQUJBOnTqlDz/8UA899JDq1at3wzratGmj2NhY7d27124vX/369fXuu+/qtddeU5UqVRQdHS1/f38lJCRo5syZyszM1LfffnvTq2vnpm3btqpVq5YGDhyo/fv3KzQ0VEuXLrUFtfwaB/L0008rNjZWPj4+qlSpkjZv3qz4+Hg98MAD+bJ8SXk6fT2vn4HVqlVTp06d9OGHHyolJUV16tTR6tWrc9yj9c4772jNmjV67LHH1LNnT1WqVEnnzp3Ttm3bFB8fn2Po/bv4+HgZhnHTzxOIU8HvJzmdCl6kSBGjWrVqxrRp02yns2a5ePGiMWDAACMgIMBwc3MzKlSoYPznP/+x9du6davh6upqd3q3YRjGn3/+afzjH/8wAgICjPPnzxuG8X+nBh84cMBo2rSpUbRoUaN06dLGsGHDjIyMDLv5dd3pk4ZhGNu2bTOaNWtmeHp6GkWLFjUiIiKMTZs2ZdvGmTNnGmXLljVcXFzydJpofHy8UbduXcPd3d3w9vY2WrVqZfz+++92fW71VPAbuf5U8CxxcXFGvXr1DA8PD8PDw8MIDQ01+vbta+zZs8eu34cffmiEhIQYVqvVqFmzprF+/fpsp1LndDrpsWPHjGeeecYoVqyY4ePjY0RGRhrHjx/P9ppnncp7/anKWe+hnE69/rusU4yzHkWLFjWCg4ON5557zvjyyy+z/cyzXpO/1//RRx8Z9evXNx544AHDarUa5cqVMwYNGmSkpKTYzTdq1CjjwQcfNAoVKmRX29KlS40qVaoYRYoUMYKDg41x48YZn3zySbb6rz8FOLd6DMMwzp49a7z88svGgw8+aBQuXNh46KGHjKioKLtTe69evWqMGzfOCA8PN6xWq1G8eHGjRo0axogRI2y1r1692mjTpo0REBBgFC5c2AgICDA6depk7N2794avq2EYRnp6ulGyZElj1KhROU5fv3690aZNG6NkyZKGm5ubUaZMGaNnz57GoUOHsvW90Sn7158KbhiGcfr0aaNz586Gl5eX4ePjY0RHRxsbN240JBmfffaZrV9up4L37ds323qCgoKMqKgo2/Pz588b3bp1M0qWLGl4enoazZo1MxISErL1u5VTwW8kp/fBzT4Ds6SlpRn9+vUzHnjgAcPDw8No1aqVcfTo0Rw/y06ePGn07dvXCAwMNNzc3Aw/Pz+jUaNGxowZM2x9cjsVvEOHDka9evVuuB34i8UwbnNkIJAH0dHR+vLLL21jegDculGjRmn27Nnat29froN075YlS5bomWee0YYNG1S3bl2n1mJmJ06cUEhIiD777DP23OQBY24A4B4zYMAAXbp0SZ999tldXe/1Y3kyMjI0depUeXt7Z7tqMvLX5MmTVblyZYJNHjHmBgDuMZ6ennm6Jkp+e+WVV5SWlqbatWsrPT1dixcv1qZNm/T222/bXXwP+e+dd95xdgn3FMINACBPnnzySU2YMEHLli3TlStXVL58eU2dOtXuyuNAQcCYGwAAYCqMuQEAAKZCuAEAAKZy3425yczM1PHjx+Xl5cXNxwAAuEcYhqGLFy8qICDgpvdDu+/CzfHjx233IgIAAPeWo0eP3vRO9vdduMm6bPvRo0dttxoAAAAFW2pqqgIDA+1uZJqb+y7cZB2K8vb2JtwAAHCPycuQEgYUAwAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAU7nvbpwJoOC5fPmyEhISbns5aWlpOnTokIKDg+Xu7p4PlUmhoaEqWrRoviwLwN1BuAHgdAkJCapRo4azy8jR1q1bVb16dWeXAcABhBsAThcaGqqtW7fe9nJ2796tLl26aP78+QoLC8uHyv6qDcC9hXADwOmKFi2ar3tHwsLC2NsC3McYUAwAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEzFqeHm4sWL6t+/v4KCguTu7q46depoy5YtufZfu3atLBZLtseJEyfuYtUAAKAgc3Xmynv06KHffvtNsbGxCggI0Pz589W4cWP9/vvvevDBB3Odb8+ePfL29rY99/X1vRvlAgCAe4DT9tykpaUpLi5O48ePV/369VW+fHkNHz5c5cuX17Rp0244r6+vr/z8/GyPQoU4ugYAAP7itFTw559/KiMjQ0WKFLFrd3d314YNG244b7Vq1eTv768mTZpo48aNN+ybnp6u1NRUuwcAADAvp4UbLy8v1a5dW6NGjdLx48eVkZGh+fPna/PmzUpOTs5xHn9/f02fPl1xcXGKi4tTYGCgGjZsqG3btuW6nrFjx8rHx8f2CAwMvFObBAAACgCLYRiGs1Z+4MABde/eXevXr5eLi4uqV6+uhx9+WFu3btXu3bvztIwGDRqoTJkyio2NzXF6enq60tPTbc9TU1MVGBiolJQUu3E7AO5927ZtU40aNbR161ZVr17d2eUAyEepqany8fHJ0/e3UwerlCtXTuvWrdOlS5d09OhR/fTTT7p27ZrKli2b52XUqlVL+/fvz3W61WqVt7e33QMAAJhXgRiJ6+HhIX9/f50/f14rVqxQmzZt8jzv9u3b5e/vfwerAwAA9xKnngq+YsUKGYahihUrav/+/Ro0aJBCQ0PVrVs3SVJMTIySkpI0b948SdLkyZMVEhKi8PBwXblyRbNmzdL333+vlStXOnMzAABAAeLUcJOSkqKYmBgdO3ZMJUqU0HPPPacxY8bIzc1NkpScnKwjR47Y+l+9elUDBw5UUlKSihYtqipVqig+Pl4RERHO2gQAAFDAOHVAsTM4MiAJwL2FAcWAed0zA4oBAADyG+EGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYilPDzcWLF9W/f38FBQXJ3d1dderU0ZYtW244z9q1a1W9enVZrVaVL19ec+bMuTvFAgCAe4JTw02PHj20atUqxcbGaufOnWratKkaN26spKSkHPsnJiaqZcuWioiI0Pbt29W/f3/16NFDK1asuMuVAwCAgspiGIbhjBWnpaXJy8tLX331lVq2bGlrr1Gjhpo3b67Ro0dnm+df//qXvvnmG/3222+2to4dO+rChQtavnx5ntabmpoqHx8fpaSkyNvb+/Y3BECBsW3bNtWoUUNbt25V9erVnV0OgHzkyPe30/bc/Pnnn8rIyFCRIkXs2t3d3bVhw4Yc59m8ebMaN25s19asWTNt3rw51/Wkp6crNTXV7gEAAMzLaeHGy8tLtWvX1qhRo3T8+HFlZGRo/vz52rx5s5KTk3Oc58SJEypdurRdW+nSpZWamqq0tLQc5xk7dqx8fHxsj8DAwHzfFgAAUHA4dcxNbGysDMPQgw8+KKvVqvfee0+dOnVSoUL5V1ZMTIxSUlJsj6NHj+bbsgEAQMHj6syVlytXTuvWrdMff/yh1NRU+fv7q0OHDipbtmyO/f38/HTy5Em7tpMnT8rb21vu7u45zmO1WmW1WvO9dgAAUDAViOvceHh4yN/fX+fPn9eKFSvUpk2bHPvVrl1bq1evtmtbtWqVateufTfKBAAA9wCnhpsVK1Zo+fLlSkxM1KpVqxQREaHQ0FB169ZN0l+HlLp27Wrr37t3bx08eFBvvPGGEhIS9OGHH2rRokUaMGCAszYBAAAUME4NNykpKerbt69CQ0PVtWtX1atXTytWrJCbm5skKTk5WUeOHLH1DwkJ0TfffKNVq1apatWqmjBhgmbNmqVmzZo5axMAAEAB47Tr3DgL17kBzIvr3ADmdU9c5wYAAOBOINwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTua1wc+XKlfyqAwAAIF84HG4yMzM1atQoPfjgg/L09NTBgwclSUOGDNHHH3+c7wUCAAA4wuFwM3r0aM2ZM0fjx49X4cKFbe2PPPKIZs2ala/FAQAAOMrhcDNv3jzNmDFDzz//vFxcXGztVatWVUJCQr4WBwAA4CiHw01SUpLKly+frT0zM1PXrl1zaFkZGRkaMmSIQkJC5O7urnLlymnUqFEyDCPXedauXSuLxZLtceLECUc3BQAAmJCrozNUqlRJP/zwg4KCguzav/zySz366KMOLWvcuHGaNm2a5s6dq/DwcP3888/q1q2bfHx81K9fvxvOu2fPHnl7e9ue+/r6OrRuAABgTg6Hm6FDhyoqKkpJSUnKzMzU4sWLtWfPHs2bN0/Lli1zaFmbNm1SmzZt1LJlS0lScHCwFi5cqJ9++umm8/r6+qpYsWKOlg8AAEzO4cNSbdq00ddff634+Hh5eHho6NCh2r17t77++ms1adLEoWXVqVNHq1ev1t69eyVJO3bs0IYNG9S8efObzlutWjX5+/urSZMm2rhxY6790tPTlZqaavcAAADm5fCeG0l64okntGrVqtte+eDBg5WamqrQ0FC5uLgoIyNDY8aM0fPPP5/rPP7+/po+fbpq1qyp9PR0zZo1Sw0bNtT//vc/Va9ePVv/sWPHasSIEbddKwAAuDc4HG62bNmizMxMPfbYY3bt//vf/+Ti4qKaNWvmeVmLFi3Sp59+qgULFig8PFzbt29X//79FRAQoKioqBznqVixoipWrGh7XqdOHR04cECTJk1SbGxstv4xMTF67bXXbM9TU1MVGBiY5xoBAMC9xeHDUn379tXRo0eztSclJalv374OLWvQoEEaPHiwOnbsqMqVK+uFF17QgAEDNHbsWIeWU6tWLe3fvz/HaVarVd7e3nYPAABgXg6Hm99//z3Hwz+PPvqofv/9d4eWdfnyZRUqZF+Ci4uLMjMzHVrO9u3b5e/v79A8AADAnBw+LGW1WnXy5EmVLVvWrj05OVmuro4trlWrVhozZozKlCmj8PBw/fLLL5o4caK6d+9u6xMTE6OkpCTNmzdPkjR58mSFhIQoPDxcV65c0axZs/T9999r5cqVjm4KAAAwIYfDTdOmTRUTE6OvvvpKPj4+kqQLFy7ozTffdPhsqalTp2rIkCHq06ePTp06pYCAAPXq1UtDhw619UlOTtaRI0dsz69evaqBAwcqKSlJRYsWVZUqVRQfH6+IiAhHNwUAAJiQxbjR5YBzkJSUpPr16+vs2bO2i/Zt375dpUuX1qpVqwr8YN3U1FT5+PgoJSWF8TeAyWzbtk01atTQ1q1bczx8DuDe5cj3t8N7bh588EH9+uuv+vTTT7Vjxw65u7urW7du6tSpk9zc3G65aAAAgPxwS9e58fDw0D//+c/8rgUAAOC23VK42bdvn9asWaNTp05lO7Pp7+NlAAAA7jaHw83MmTP10ksvqWTJkvLz85PFYrFNs1gshBsAAOBUDoeb0aNHa8yYMfrXv/51J+oBAAC4LQ5fxO/8+fOKjIy8E7UAAADcNofDTWRkJBfMAwAABZbDh6XKly+vIUOG6Mcff1TlypWznf7dr1+/fCsOAADAUQ6HmxkzZsjT01Pr1q3TunXr7KZZLBbCDQAAcCqHw01iYuKdqAMAACBfODzmBgAAoCC7pYv4HTt2TEuXLtWRI0d09epVu2kTJ07Ml8IAAABuhcPhZvXq1WrdurXKli2rhIQEPfLIIzp06JAMw+BGdQAAwOkcPiwVExOj119/XTt37lSRIkUUFxeno0ePqkGDBlz/BgAAOJ3D4Wb37t3q2rWrJMnV1VVpaWny9PTUyJEjNW7cuHwvEAAAwBEOhxsPDw/bOBt/f38dOHDANu3MmTP5VxkAAMAtcHjMzeOPP64NGzYoLCxMLVq00MCBA7Vz504tXrxYjz/++J2oEQAAIM8cDjcTJ07UpUuXJEkjRozQpUuX9Pnnn6tChQqcKQUAAJzO4XBTtmxZ2/89PDw0ffr0fC0IAADgdjg85qZs2bI6e/ZstvYLFy7YBR8AAABncDjcHDp0SBkZGdna09PTlZSUlC9FAQAA3Ko8H5ZaunSp7f8rVqyQj4+P7XlGRoZWr16t4ODgfC0OAADAUXkON23btpX0152/o6Ki7Ka5ubkpODhYEyZMyNfiABRs+/bt08WLF51dhs3u3bvt/i0ovLy8VKFCBWeXAdw38hxuMjMzJUkhISHasmWLSpYseceKAlDw7du3Tw8//LCzy8hRly5dnF1CNnv37iXgAHeJw2dLJSYmZmu7cOGCihUrlh/1ALhHZO2xmT9/vsLCwpxczV/S0tJ06NAhBQcHy93d3dnlSPprL1KXLl0K1B4uwOwcDjfjxo1TcHCwOnToIEmKjIxUXFyc/P399e2336pq1ar5XiSAgissLKxA3TS3bt26zi4BgJM5fLbU9OnTFRgYKElatWqV4uPjtXz5cjVv3lyDBg3K9wIBAAAc4fCemxMnTtjCzbJly9S+fXs1bdpUwcHBeuyxx/K9QAAAAEc4vOemePHiOnr0qCRp+fLlaty4sSTJMIwcr38DAABwNzm85+bZZ59V586dVaFCBZ09e1bNmzeXJP3yyy8qX758vhcIAADgCIfDzaRJkxQcHKyjR49q/Pjx8vT0lCQlJyerT58++V4gAACAIxwON25ubnr99deztQ8YMCBfCgIAALgdeQo3S5cuVfPmzeXm5mZ3G4actG7dOl8KAwAAuBV5Cjdt27bViRMn5Ovra7sNQ04sFguDigEAgFPlKdxk3Xrh+v8DAAAUNA6fCg4AAFCQOTSgODMzU3PmzNHixYt16NAhWSwWhYSEqF27dnrhhRdksVjuVJ0AAAB5kuc9N4ZhqHXr1urRo4eSkpJUuXJlhYeH6/Dhw4qOjtYzzzzj8MozMjI0ZMgQhYSEyN3dXeXKldOoUaNkGMYN51u7dq2qV68uq9Wq8uXLa86cOQ6vGwAAmFOe99zMmTNH69ev1+rVqxUREWE37fvvv1fbtm01b948de3aNc8rHzdunKZNm6a5c+cqPDxcP//8s7p16yYfHx/169cvx3kSExPVsmVL9e7dW59++qlWr16tHj16yN/fX82aNcvzugEAgDnlOdwsXLhQb775ZrZgI0lPPvmkBg8erE8//dShcLNp0ya1adNGLVu2lCQFBwdr4cKF+umnn3KdZ/r06QoJCdGECRMk/XVH4g0bNmjSpEmEGwAAkPfDUr/++queeuqpXKc3b95cO3bscGjlderU0erVq7V3715J0o4dO7RhwwbbLR1ysnnzZtv9rLI0a9ZMmzdvzrF/enq6UlNT7R4AAMC88rzn5ty5cypdunSu00uXLq3z5887tPLBgwcrNTVVoaGhcnFxUUZGhsaMGaPnn38+13lOnDiRrY7SpUsrNTVVaWlpcnd3t5s2duxYjRgxwqG6AADAvSvPe24yMjLk6pp7FnJxcdGff/7p0MoXLVqkTz/9VAsWLNC2bds0d+5cvfvuu5o7d65Dy7mRmJgYpaSk2B5ZdzQHAADmlOc9N4ZhKDo6WlarNcfp6enpDq980KBBGjx4sDp27ChJqly5sg4fPqyxY8cqKioqx3n8/Px08uRJu7aTJ0/K29s7214bSbJarbnWDAAAzCfP4Sa3sPF3jgwmlqTLly+rUCH7nUcuLi43vApy7dq19e2339q1rVq1SrVr13Zo3QAAwJzyHG5mz56d7ytv1aqVxowZozJlyig8PFy//PKLJk6cqO7du9v6xMTEKCkpSfPmzZMk9e7dW++//77eeOMNde/eXd9//70WLVqkb775Jt/rAwAA9x6HrlCc36ZOnaohQ4aoT58+OnXqlAICAtSrVy8NHTrU1ic5OVlHjhyxPQ8JCdE333yjAQMGaMqUKXrooYc0a9YsTgMHAACSnBxuvLy8NHnyZE2ePDnXPjldfbhhw4b65Zdf7lxhAADgnsWNMwEAgKkQbgAAgKkQbgAAgKnc0pibffv2ac2aNTp16lS207b/PhgYAADgbnM43MycOVMvvfSSSpYsKT8/P1ksFts0i8VCuAEAAE7lcLgZPXq0xowZo3/96193oh4AAIDb4vCYm/PnzysyMvJO1AIAAHDbHA43kZGRWrly5Z2oBQAA4LY5fFiqfPnyGjJkiH788UdVrlxZbm5udtP79euXb8UBAAA4yuFwM2PGDHl6emrdunVat26d3TSLxUK4AQAATuVwuElMTLwTdQAAAOQLLuIHAABM5ZYu4nfs2DEtXbpUR44c0dWrV+2mTZw4MV8KAwAAuBUOh5vVq1erdevWKlu2rBISEvTII4/o0KFDMgxD1atXvxM1AgAA5JnDh6ViYmL0+uuva+fOnSpSpIji4uJ09OhRNWjQgOvfAAAAp3M43OzevVtdu3aVJLm6uiotLU2enp4aOXKkxo0bl+8FAgAAOMLhcOPh4WEbZ+Pv768DBw7Ypp05cyb/KgMAALgFDo+5efzxx7VhwwaFhYWpRYsWGjhwoHbu3KnFixfr8ccfvxM1AgAA5JnD4WbixIm6dOmSJGnEiBG6dOmSPv/8c1WoUIEzpQAAgNM5HG7Kli1r+7+Hh4emT5+erwUBAADcDi7iBwAATCVPe25KlCihvXv3qmTJkipevLgsFkuufc+dO5dvxQEAADgqT+Fm0qRJ8vLykiRNnjz5TtYDAABwW/IUbqKionL8PwAAQEGTp3CTmpqa5wV6e3vfcjEAAAC3K0/hplixYjccZ/N3GRkZt1UQAADA7chTuFmzZo3t/4cOHdLgwYMVHR2t2rVrS5I2b96suXPnauzYsXemSgAAgDzKU7hp0KCB7f8jR47UxIkT1alTJ1tb69atVblyZc2YMYMxOQAAwKkcvs7N5s2bVbNmzWztNWvW1E8//ZQvRQEAANwqh8NNYGCgZs6cma191qxZCgwMzJeiAAAAbpXDt1+YNGmSnnvuOX333Xd67LHHJEk//fST9u3bp7i4uHwvEAAAwBEO77lp0aKF9u7dq1atWuncuXM6d+6cWrVqpb1796pFixZ3okYAAIA8c3jPjfTXoam33347v2sBAAC4bbd048wffvhBXbp0UZ06dZSUlCRJio2N1YYNG/K1OAAAAEc5HG7i4uLUrFkzubu7a9u2bUpPT5ckpaSksDcHAAA4ncPhZvTo0Zo+fbpmzpwpNzc3W3vdunW1bdu2fC0OAADAUQ6Hmz179qh+/frZ2n18fHThwoX8qAkAAOCWORxu/Pz8tH///mztGzZsUNmyZR1aVnBwsCwWS7ZH3759c+w/Z86cbH2LFCni6CYAAAATc/hsqZ49e+rVV1/VJ598IovFouPHj2vz5s16/fXXNWTIEIeWtWXLFrsbbf72229q0qSJIiMjc53H29tbe/bssT3P6w09AQDA/cHhcDN48GBlZmaqUaNGunz5surXry+r1arXX39dr7zyikPLKlWqlN3zd955R+XKlbO7l9X1LBaL/Pz8HC0bAADcJxw+LGWxWPTWW2/p3Llz+u233/Tjjz/q9OnTGjVq1G0VcvXqVc2fP1/du3e/4d6YS5cuKSgoSIGBgWrTpo127dp1w+Wmp6crNTXV7gEAAMzrlq5zI0mFCxdWpUqVVKtWLXl6et52IUuWLNGFCxcUHR2da5+KFSvqk08+0VdffaX58+crMzNTderU0bFjx3KdZ+zYsfLx8bE9uP8VAADmlufDUt27d89Tv08++eSWCvn444/VvHlzBQQE5Nqndu3aql27tu15nTp1FBYWpo8++ijXPUcxMTF67bXXbM9TU1MJOAAAmFiew82cOXMUFBSkRx99VIZh5GsRhw8fVnx8vBYvXuzQfG5ubnr00UdzPHsri9VqldVqvd0SAQDAPSLP4eall17SwoULlZiYqG7duqlLly4qUaJEvhQxe/Zs+fr6qmXLlg7Nl5GRoZ07d3LDTgAAYJPnMTcffPCBkpOT9cYbb+jrr79WYGCg2rdvrxUrVtzWnpzMzEzNnj1bUVFRcnW1z1pdu3ZVTEyM7fnIkSO1cuVKHTx4UNu2bVOXLl10+PBh9ejR45bXDwAAzMWhAcVWq1WdOnXSqlWr9Pvvvys8PFx9+vRRcHCwLl26dEsFxMfH68iRIzmO6Tly5IiSk5Ntz8+fP6+ePXsqLCxMLVq0UGpqqjZt2qRKlSrd0roBAID5OHydmyyFChWSxWKRYRh2F+JzVNOmTXPd87N27Vq755MmTdKkSZNueV0AAMD8HNpzk56eroULF6pJkyZ6+OGHtXPnTr3//vs6cuRIvpwODgAAcLvyvOemT58++uyzzxQYGKju3btr4cKFKlmy5J2sDQAAwGF5DjfTp09XmTJlVLZsWa1bt07r1q3LsZ+jp3MDAADkpzyHm65du3KTSgAAUOA5dBE/AACAgu6W7y0FAABQEBFuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqeT53lIA8HeWP6/oUb9Ccr+wVzrO30m5cb+wV4/6FZLlzyvOLgW4bxBuANySIpeOaFsvT2l9L2m9s6spuMIkbevlqd2Xjkiq4+xygPsC4QbALbniWUbVP7qkTz/9VGGhoc4up8DanZCg559/Xh+3KOPsUoD7BuEGwC0xXIvolxOZSiv2sBRQzdnlFFhpJzL1y4lMGa5FnF0KcN/gQDkAADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVp4ab4OBgWSyWbI++ffvmOs8XX3yh0NBQFSlSRJUrV9a33357FysGAAAFnVPDzZYtW5ScnGx7rFq1SpIUGRmZY/9NmzapU6dOevHFF/XLL7+obdu2atu2rX777be7WTYAACjAnBpuSpUqJT8/P9tj2bJlKleunBo0aJBj/ylTpuipp57SoEGDFBYWplGjRql69ep6//3373LlAACgoCowY26uXr2q+fPnq3v37rJYLDn22bx5sxo3bmzX1qxZM23evDnX5aanpys1NdXuAQAAzKvAhJslS5bowoULio6OzrXPiRMnVLp0abu20qVL68SJE7nOM3bsWPn4+NgegYGB+VUyAAAogApMuPn444/VvHlzBQQE5OtyY2JilJKSYnscPXo0X5cPAAAKFldnFyBJhw8fVnx8vBYvXnzDfn5+fjp58qRd28mTJ+Xn55frPFarVVarNV/qBAAABV+B2HMze/Zs+fr6qmXLljfsV7t2ba1evdqubdWqVapdu/adLA8AANxDnB5uMjMzNXv2bEVFRcnV1X5HUteuXRUTE2N7/uqrr2r58uWaMGGCEhISNHz4cP388896+eWX73bZAACggHJ6uImPj9eRI0fUvXv3bNOOHDmi5ORk2/M6depowYIFmjFjhqpWraovv/xSS5Ys0SOPPHI3SwYAAAWY08fcNG3aVIZh5Dht7dq12doiIyNzvcgfAACA0/fcAAAA5CfCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBVXZxcA4N50+fJlSdK2bducXMn/SUtL06FDhxQcHCx3d3dnlyNJ2r17t7NLAO47hBsAtyQhIUGS1LNnTydXcm/w8vJydgnAfYNwA+CWtG3bVpIUGhqqokWLOreY/2/37t3q0qWL5s+fr7CwMGeXY+Pl5aUKFSo4uwzgvkG4AXBLSpYsqR49eji7jByFhYWpevXqzi4DgJMwoBgAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJiK08NNUlKSunTpogceeEDu7u6qXLmyfv7551z7r127VhaLJdvjxIkTd7FqAABQUDn1In7nz59X3bp1FRERoe+++06lSpXSvn37VLx48ZvOu2fPHnl7e9ue+/r63slSAQDAPcKp4WbcuHEKDAzU7NmzbW0hISF5mtfX11fFihW7Q5UBAIB7lVMPSy1dulQ1a9ZUZGSkfH199eijj2rmzJl5mrdatWry9/dXkyZNtHHjxlz7paenKzU11e4BAADMy6nh5uDBg5o2bZoqVKigFStW6KWXXlK/fv00d+7cXOfx9/fX9OnTFRcXp7i4OAUGBqphw4batm1bjv3Hjh0rHx8f2yMwMPBObQ4AACgALIZhGM5aeeHChVWzZk1t2rTJ1tavXz9t2bJFmzdvzvNyGjRooDJlyig2NjbbtPT0dKWnp9uep6amKjAwUCkpKXZjdgDc+7Zt26YaNWpo69at3DgTMJnU1FT5+Pjk6fvbqXtu/P39ValSJbu2sLAwHTlyxKHl1KpVS/v3789xmtVqlbe3t90DAACYl1PDTd26dbVnzx67tr179yooKMih5Wzfvl3+/v75WRoAALhHOfVsqQEDBqhOnTp6++231b59e/3000+aMWOGZsyYYesTExOjpKQkzZs3T5I0efJkhYSEKDw8XFeuXNGsWbP0/fffa+XKlc7aDAAAUIA4Ndz84x//0H//+1/FxMRo5MiRCgkJ0eTJk/X888/b+iQnJ9sdprp69aoGDhyopKQkFS1aVFWqVFF8fLwiIiKcsQkAAKCAceqAYmdwZEASgHsLA4oB87pnBhQDAADkN8INAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFVdnFwAAly9fVkJCwm0vZ/fu3Xb/5ofQ0FAVLVo035YH4M4j3ABwuoSEBNWoUSPfltelS5d8W9bWrVtVvXr1fFsegDuPcAPA6UJDQ7V169bbXk5aWpoOHTqk4OBgubu750Nlf9UG4N5iMQzDcHYRd1Nqaqp8fHyUkpIib29vZ5cDAADywJHvbwYUAwAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAU3F1dgF3W9ZN0FNTU51cCQAAyKus7+2s7/Ebue/CzcWLFyVJgYGBTq4EAAA46uLFi/Lx8blhH4uRlwhkIpmZmTp+/Li8vLxksVicXQ6AfJSamqrAwEAdPXpU3t7ezi4HQD4yDEMXL15UQECAChW68aia+y7cADCv1NRU+fj4KCUlhXAD3McYUAwAAEyFcAMAAEyFcAPANKxWq4YNGyar1ersUgA4EWNuAACAqbDnBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBsA9b/369WrVqpUCAgJksVi0ZMkSZ5cEwIkINwDueX/88YeqVq2qDz74wNmlACgA7ru7ggMwn+bNm6t58+bOLgNAAcGeGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqcLQXgnnfp0iXt37/f9jwxMVHbt29XiRIlVKZMGSdWBsAZLIZhGM4uAgBux9q1axUREZGtPSoqSnPmzLn7BQFwKsINAAAwFcbcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAU/l/gzxR627ZWGIAAAAASUVORK5CYII="},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\ndef plot_histogram(metrics, title, xlabel):\n    values = list(metrics.values()) # Extract values from the dictionary\n    plt.figure()\n    plt.hist(values, bins=20, alpha=0.7)\n    plt.title(title)\n    plt.xlabel(xlabel)\n    plt.ylabel('Frequency')\n    plt.show()\n\ndef compare_metrics(metrics_original, metrics_finetuned, key):\n    changes = {}\n    for label in metrics_original:\n        if label in metrics_finetuned:\n            change = metrics_finetuned[label][key] - metrics_original[label][key]\n            changes[label] = change\n    return changes\n\nmax_radius_change = compare_metrics(metrics_original, metrics_finetuned, 'max_radius')\nplot_histogram(max_radius_change, 'Histogram of Max Radius Change', 'Max Radius Change')","metadata":{"execution":{"iopub.status.busy":"2024-07-21T15:22:03.6894Z","iopub.execute_input":"2024-07-21T15:22:03.690229Z","iopub.status.idle":"2024-07-21T15:22:03.992Z","shell.execute_reply.started":"2024-07-21T15:22:03.690192Z","shell.execute_reply":"2024-07-21T15:22:03.990996Z"},"trusted":true},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"\ndef calculate_inter_cluster_metrics(embeddings):\n    centers = {label: metrics['center'] for label, metrics in embeddings.items()}\n    labels = list(centers.keys())\n    inter_cluster_distances = euclidean_distances(list(centers.values()))\n\n    metrics = {}\n    for i, label1 in enumerate(labels):\n        for j, label2 in enumerate(labels):\n            if i >= j:\n                continue\n            distance = inter_cluster_distances[i, j]\n            pair = tuple(sorted((label1, label2)))\n            metrics[pair] = distance\n\n    return metrics\n\ninter_cluster_metrics_original = calculate_inter_cluster_metrics(metrics_original)\ninter_cluster_metrics_finetuned = calculate_inter_cluster_metrics(metrics_finetuned)","metadata":{"execution":{"iopub.status.busy":"2024-07-21T15:22:10.559553Z","iopub.execute_input":"2024-07-21T15:22:10.560331Z","iopub.status.idle":"2024-07-21T15:22:10.56975Z","shell.execute_reply.started":"2024-07-21T15:22:10.560297Z","shell.execute_reply":"2024-07-21T15:22:10.568765Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"inter_cluster_metrics_original","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def compare_inter_cluster_metrics(metrics_original, metrics_finetuned):\n    changes = {}\n    for pair in metrics_original:\n        if pair in metrics_finetuned:\n            change = metrics_finetuned[pair] - metrics_original[pair]\n            changes[pair] = change\n    return changes\n\ninter_cluster_distance_change = compare_inter_cluster_metrics(inter_cluster_metrics_original, inter_cluster_metrics_finetuned)\n\ndef plot_inter_cluster_changes(inter_cluster_distance_change, title, xlabel):\n    values = list(inter_cluster_distance_change.values())\n    plt.figure()\n    plt.hist(values, bins=20, alpha=0.7)\n    plt.title(title)\n    plt.xlabel(xlabel)\n    plt.ylabel('Frequency')\n    plt.show()\n\nplot_inter_cluster_changes(inter_cluster_distance_change, 'Histogram of Inter-Cluster Distance Changes', 'Inter-Cluster Distance Change')\n","metadata":{"execution":{"iopub.status.busy":"2024-07-21T15:22:16.290692Z","iopub.execute_input":"2024-07-21T15:22:16.291356Z","iopub.status.idle":"2024-07-21T15:22:16.609269Z","shell.execute_reply.started":"2024-07-21T15:22:16.291319Z","shell.execute_reply":"2024-07-21T15:22:16.608263Z"},"trusted":true},"execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"\ndef compare_inter_cluster_metrics(metrics_original, metrics_finetuned):\n    changes = {}\n    for pair in metrics_original:\n        if pair in metrics_finetuned:\n            change = metrics_finetuned[pair] - metrics_original[pair]\n            changes[pair] = change\n    return changes\n\ninter_cluster_distance_change = compare_inter_cluster_metrics(inter_cluster_metrics_original, inter_cluster_metrics_finetuned)\n\ndef plot_inter_cluster_changes(inter_cluster_distance_change, title, xlabel):\n    values = list(inter_cluster_distance_change.values())\n    plt.figure()\n    plt.hist(values, bins=20, alpha=0.7)\n    plt.title(title)\n    plt.xlabel(xlabel)\n    plt.ylabel('Frequency')\n    plt.show()\n\nplot_inter_cluster_changes(inter_cluster_distance_change, 'Histogram of Inter-Cluster Distance Changes', 'Inter-Cluster Distance Change')\n","metadata":{"execution":{"iopub.status.busy":"2024-07-21T15:22:30.146349Z","iopub.execute_input":"2024-07-21T15:22:30.146746Z","iopub.status.idle":"2024-07-21T15:22:30.476032Z","shell.execute_reply.started":"2024-07-21T15:22:30.146715Z","shell.execute_reply":"2024-07-21T15:22:30.475012Z"},"trusted":true},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"def classify_impact(intra_metrics_original, intra_metrics_finetuned, inter_metrics_original, inter_metrics_finetuned):\n    impact = {\n        'positively_impacted': [],\n        'negatively_impacted': [],\n        'averagely_impacted': []\n    }\n\n    # Define thresholds for considering a change as significant\n    threshold = 0.01  # You can adjust this value based on your needs\n\n    # Classify intra-cluster metric changes\n    for label in intra_metrics_original:\n        if label in intra_metrics_finetuned:\n            max_radius_change = intra_metrics_finetuned[label]['max_radius'] - intra_metrics_original[label]['max_radius']\n            if max_radius_change < -threshold:\n                impact['positively_impacted'].append((label, 'max_radius'))\n            elif max_radius_change > threshold:\n                impact['negatively_impacted'].append((label, 'max_radius'))\n            else:\n                impact['averagely_impacted'].append((label, 'max_radius'))\n\n            # Add other metrics here if needed\n            # Example: variance, standard_deviation, etc.\n            variance_change = intra_metrics_finetuned[label]['variance'] - intra_metrics_original[label]['variance']\n            if variance_change < -threshold:\n                impact['positively_impacted'].append((label, 'variance'))\n            elif variance_change > threshold:\n                impact['negatively_impacted'].append((label, 'variance'))\n            else:\n                impact['averagely_impacted'].append((label, 'variance'))\n\n    # Classify inter-cluster metric changes\n    for pair in inter_metrics_original:\n        if pair in inter_metrics_finetuned:\n            distance_change = inter_metrics_finetuned[pair] - inter_metrics_original[pair]\n            if distance_change < -threshold:\n                impact['positively_impacted'].append((pair, 'inter_cluster_distance'))\n            elif distance_change > threshold:\n                impact['negatively_impacted'].append((pair, 'inter_cluster_distance'))\n            else:\n                impact['averagely_impacted'].append((pair, 'inter_cluster_distance'))\n\n    return impact\n\n# Assuming metrics_original and metrics_finetuned are dictionaries containing the calculated metrics\nimpact = classify_impact(metrics_original, metrics_finetuned, inter_cluster_metrics_original, inter_cluster_metrics_finetuned)\n\n# Print the impact classification\nprint(f\"Positively Impacted Faces: {len(impact['positively_impacted'])}\")\nprint(f\"Negatively Impacted Faces: {len(impact['negatively_impacted'])}\")\nprint(f\"Averagely Impacted Faces: {len(impact['averagely_impacted'])}\")","metadata":{"execution":{"iopub.status.busy":"2024-07-21T15:22:33.664848Z","iopub.execute_input":"2024-07-21T15:22:33.665235Z","iopub.status.idle":"2024-07-21T15:22:33.679597Z","shell.execute_reply.started":"2024-07-21T15:22:33.665193Z","shell.execute_reply":"2024-07-21T15:22:33.678563Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"Positively Impacted Faces: 152\nNegatively Impacted Faces: 0\nAveragely Impacted Faces: 0\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
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