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+
+---
+
+# Simple Object Detection Dataset
+
+## Overview
+
+This dataset contains images and corresponding annotations for a simple object detection task. The goal of the task is to detect and localize objects within the images. The dataset is intended for educational purposes and small-scale experiments in object detection.
+
+## Content
+
+The dataset consists of the following components:
+
+1. **Images**: The dataset contains a collection of images in JPEG format. These images serve as the input for the object detection task. Each image may contain one or more objects of interest.
+
+2. **Annotations**: Annotations are provided in XML format using the PASCAL VOC format. Each annotation file corresponds to an image and contains information about the objects present in the image, including their class labels and bounding box coordinates.
+
+## Data Format
+
+### Images
+
+The images are stored in the `images` directory. Each image is named with a unique identifier and has the `.jpg` file extension.
+
+### Annotations
+
+The annotations are stored in the `annotations` directory. Each annotation file is named to match the corresponding image file and has the `.xml` file extension. The XML files follow the PASCAL VOC format and contain the following information for each object in the image:
+
+- Object class label
+- Bounding box coordinates (xmin, ymin, xmax, ymax)
+
+## Usage
+
+This dataset can be used for various tasks related to object detection, including:
+
+- Training and evaluating object detection models
+- Experimenting with different object detection algorithms and techniques
+- Educational purposes, such as learning about computer vision and deep learning
+
+## Citation
+
+If you use this dataset in your work, please consider citing the dataset source on Kaggle:
+
+```
+@misc{kishanj/simple-object-detection,
+  author = {Kishan J},
+  title = {Simple Object Detection Dataset},
+  year = {2022},
+  publisher = {Kaggle},
+  journal = {Kaggle Datasets},
+  howpublished = {\url{https://www.kaggle.com/datasets/kishanj/simple-object-detection}}
+}
+```
+
+## License
+
+This dataset is provided under the [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/) license, allowing for unrestricted use and redistribution.
+
+---
+
+Feel free to modify and expand this README to provide additional details or instructions as needed.
\ No newline at end of file
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+## **Simple Object Detection**
+
+### 🎯 **Goal**
+
+The primary objective of this project is to analyze and experiment with object detection techniques using a simple dataset. The aim is to explore different models and algorithms for object detection tasks and compare their performance.
+
+### 🧵 **Dataset**
+
+The dataset used for this analysis is the [Simple Object Detection Dataset](https://www.kaggle.com/datasets/kishanj/simple-object-detection/data), available on Kaggle. It comprises images and annotations for a simple object detection task.
+
+### 🧾 **Description**
+
+This project involves analyzing the Simple Object Detection Dataset to understand the characteristics of the data, preprocess it for training, implement various object detection models, and evaluate their performance. The focus is on exploring different neural network architectures and techniques for object detection.
+
+### 🧮 **What I had done!**
+
+1. **Data Loading and Exploration**: Loaded the dataset, examined the images and annotations, and understood the structure of the data.
+2. **Data Preprocessing**: Prepared the data for training by resizing images, encoding annotations, and splitting the dataset into training and validation sets.
+3. **Model Implementation**: Implemented various object detection models including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN).
+4. **Training and Evaluation**: Trained the models on the training data, evaluated their performance on the validation set, and compared their accuracy and speed.
+5. **Visualization**: Visualized the predictions made by the models on sample images to understand their effectiveness in detecting objects.
+6. **Performance Comparison**: Compared the performance of different models based on metrics such as mean Average Precision (mAP), accuracy, and inference speed.
+
+### 🚀 **Models Implemented**
+
+- **ANN (Artificial Neural Network)**: Basic neural network architecture used for object detection.
+- **CNN (Convolutional Neural Network)**: Widely used for image-related tasks, including object detection.
+- **DNN (Deep Neural Network)**: Deeper neural network architecture for more complex feature extraction.
+- **LSTM (Long Short-Term Memory)**: Recurrent neural network architecture with memory cells, suitable for sequential data like object detection.
+- **RNN (Recurrent Neural Network)**: Another type of recurrent neural network architecture, effective for sequential data processing.
+
+### 📚 **Libraries Needed**
+
+- **TensorFlow**
+- **Keras**
+- **OpenCV**
+- **Matplotlib**
+- **NumPy**
+- **Pandas**
+- **Seaborn**
+
+### 📊 **Exploratory Data Analysis Results**
+
+
+
+
+## Distribution
+
+![Distribution](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/Distribution.png)
+
+
+
+## Box Size Distribution
+
+![Box Size Distribution](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/box-size-didtribution.png)
+
+## Histogram of X-Max
+
+![Histogram of X-Max](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/histogram-xmax.png)
+
+## Histogram of X-Min
+
+![Histogram of X-Min](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/histogram-xmin.png)
+
+## Histogram of Y-Max
+
+![Histogram of Y-Max](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/histogram-ymax.png)
+
+## Histogram of Y-Min
+
+![Histogram of Y-Min](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/histogram-ymin.png)
+
+## Plot
+
+![Plot](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/plot.png)
+
+## Size Location
+
+![Size Location](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/size-location.png)
+
+
+
+### 📈 **Performance of the Models**
+
+## ANN Loss
+
+![ANN Loss](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/ANN_loss.png)
+
+## ANN Mean Absolute Error
+
+![ANN Mean Absolute Error](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/ANN_mae.png)
+
+## CNN Loss
+
+![CNN Loss](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/CNN_loss.png)
+
+## CNN Mean Absolute Error
+
+![CNN Mean Absolute Error](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/CNN_mae.png)
+
+## DNN Loss
+
+![DNN Loss](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/DNN_loss.png)
+
+## DNN Mean Absolute Error
+
+![DNN Mean Absolute Error](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/DNN_mae.png)
+
+## LSTM Loss
+
+![LSTM Loss](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/LSTM_loss.png)
+
+## LSTM Mean Absolute Error
+
+![LSTM Mean Absolute Error](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/LSTM_mae.png)
+
+## RNN Loss
+
+![RNN Loss](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/RNN_loss.png)
+
+## RNN Mean Absolute Error
+
+![RNN Mean Absolute Error](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/RNN_mae.png)
+
+### 📢 **Conclusion**
+
+The project successfully explored various object detection models and techniques using the Simple Object Detection Dataset. By comparing the performance of different models, valuable insights were gained into their strengths and weaknesses, aiding in informed decision-making for future object detection tasks.
+
+## Comparison Bar Graph - Validation Loss
+
+![Comparison Bar Graph - Validation Loss](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/comparison_bar_graph_val_loss.png)
+
+## Comparison Bar Graph - Validation Mean Absolute Error
+
+![Comparison Bar Graph - Validation Mean Absolute Error](https://github.com/manishh12/DL-Simplified/blob/main/Simple%20Object%20Detection/Images/comparison_bar_graph_val_mae.png)
+
+### ✒️ **Your Signature**
+
+[Manish Kumar Gupta]
diff --git a/Simple Object Detection/Model/Simple_object_detection_ann_cnn_dnn_lstm_rnn.ipynb b/Simple Object Detection/Model/Simple_object_detection_ann_cnn_dnn_lstm_rnn.ipynb
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+{
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "version": "3.6.4",
+      "file_extension": ".py",
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "name": "python",
+      "mimetype": "text/x-python"
+    },
+    "kaggle": {
+      "accelerator": "gpu",
+      "dataSources": [
+        {
+          "sourceId": 1547420,
+          "sourceType": "datasetVersion",
+          "datasetId": 913037
+        }
+      ],
+      "dockerImageVersionId": 30121,
+      "isInternetEnabled": true,
+      "language": "python",
+      "sourceType": "notebook",
+      "isGpuEnabled": true
+    },
+    "colab": {
+      "provenance": [],
+      "toc_visible": true,
+      "gpuType": "T4"
+    },
+    "accelerator": "GPU"
+  },
+  "nbformat_minor": 0,
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Importing Libraries"
+      ],
+      "metadata": {
+        "id": "Z51WBHI2J6DI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import seaborn as sns\n",
+        "from bs4 import BeautifulSoup\n",
+        "import matplotlib.pyplot as plt\n",
+        "import tensorflow as tf\n",
+        "import matplotlib.patches as mpatches"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:02:48.685095Z",
+          "iopub.execute_input": "2021-08-07T05:02:48.685435Z",
+          "iopub.status.idle": "2021-08-07T05:02:53.491802Z",
+          "shell.execute_reply.started": "2021-08-07T05:02:48.685357Z",
+          "shell.execute_reply": "2021-08-07T05:02:53.491014Z"
+        },
+        "trusted": true,
+        "id": "9HVuTFYpJs86"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Loading Dataset"
+      ],
+      "metadata": {
+        "id": "UfvlqFc3J_Uj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!unzip /content/archive.zip"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "Tw_nd-mWJ0I2",
+        "outputId": "921a8801-9629-45e8-ef5d-e5aaa4582658"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
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+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define Directory\n",
+        "images_directory = \"/content/datasets/images\"\n",
+        "annotations_directory=\"/content/datasets/annotations\"\n",
+        "\n",
+        "img_paths = sorted([os.path.join(images_directory, fname) for fname in os.listdir(images_directory) if fname.endswith(\".jpg\")])\n",
+        "label_paths = sorted([os.path.join(annotations_directory, fname) for fname in os.listdir(annotations_directory) if fname.endswith(\".xml\")])\n",
+        "\n",
+        "k = len(img_paths)\n",
+        "\n",
+        "data_list = []\n",
+        "\n",
+        "for i in range(k):\n",
+        "    annotation_file=label_paths[i]\n",
+        "    ds = BeautifulSoup(open(annotation_file).read(), \"html.parser\")\n",
+        "\n",
+        "    # Iterating over each object elements\n",
+        "    for o in ds.find_all(\"object\"):\n",
+        "\n",
+        "        x_min = max(0, int(float(o.find(\"xmin\").string)))\n",
+        "        y_min = max(0, int(float(o.find(\"ymin\").string)))\n",
+        "        x_max = min(int(ds.find(\"width\").string), int(float(o.find(\"xmax\").string)))\n",
+        "        y_max = min(int(ds.find(\"height\").string), int(float(o.find(\"ymax\").string)))\n",
+        "\n",
+        "        # in case the boundary goes above its limis, providing some restrictions.\n",
+        "        if x_min >= x_max or y_min >= y_max:\n",
+        "            continue\n",
+        "        elif x_max <= x_min or y_max <= y_min:\n",
+        "            continue\n",
+        "\n",
+        "        sample = [str(img_paths[i]), x_min, y_min, x_max, y_max]\n",
+        "\n",
+        "        data_list.append(sample)\n",
+        "\n",
+        "data = pd.DataFrame(data_list)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:06:17.508034Z",
+          "iopub.execute_input": "2021-08-07T05:06:17.50837Z",
+          "iopub.status.idle": "2021-08-07T05:06:18.390511Z",
+          "shell.execute_reply.started": "2021-08-07T05:06:17.508338Z",
+          "shell.execute_reply": "2021-08-07T05:06:18.389703Z"
+        },
+        "trusted": true,
+        "id": "lBNBbzYXJs88"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# EDA"
+      ],
+      "metadata": {
+        "id": "Dd1IQdGsJs89"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.head()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:06:56.257092Z",
+          "iopub.execute_input": "2021-08-07T05:06:56.257419Z",
+          "iopub.status.idle": "2021-08-07T05:06:56.276996Z",
+          "shell.execute_reply.started": "2021-08-07T05:06:56.25739Z",
+          "shell.execute_reply": "2021-08-07T05:06:56.275936Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "f8J2O8sFJs8-",
+        "outputId": "469e0bf3-753f-4a39-92b4-5e9ccd9486a3"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                      0   1   2    3    4\n",
+              "0    /content/datasets/images/a (1).jpg  38  54  179  173\n",
+              "1   /content/datasets/images/a (10).jpg  58  14  155  117\n",
+              "2  /content/datasets/images/a (100).jpg  86  18  182  125\n",
+              "3  /content/datasets/images/a (101).jpg  23  16  120  115\n",
+              "4  /content/datasets/images/a (102).jpg  33  30  166  173"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-28c4275f-497a-4d64-a9e7-dc19c41521b3\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>/content/datasets/images/a (1).jpg</td>\n",
+              "      <td>38</td>\n",
+              "      <td>54</td>\n",
+              "      <td>179</td>\n",
+              "      <td>173</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>/content/datasets/images/a (10).jpg</td>\n",
+              "      <td>58</td>\n",
+              "      <td>14</td>\n",
+              "      <td>155</td>\n",
+              "      <td>117</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>/content/datasets/images/a (100).jpg</td>\n",
+              "      <td>86</td>\n",
+              "      <td>18</td>\n",
+              "      <td>182</td>\n",
+              "      <td>125</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>/content/datasets/images/a (101).jpg</td>\n",
+              "      <td>23</td>\n",
+              "      <td>16</td>\n",
+              "      <td>120</td>\n",
+              "      <td>115</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>/content/datasets/images/a (102).jpg</td>\n",
+              "      <td>33</td>\n",
+              "      <td>30</td>\n",
+              "      <td>166</td>\n",
+              "      <td>173</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-28c4275f-497a-4d64-a9e7-dc19c41521b3')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-28c4275f-497a-4d64-a9e7-dc19c41521b3 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-28c4275f-497a-4d64-a9e7-dc19c41521b3');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-d49c6417-80b5-4318-9030-df817e4c77f1\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d49c6417-80b5-4318-9030-df817e4c77f1')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-d49c6417-80b5-4318-9030-df817e4c77f1 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "data",
+              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 112,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 112,\n        \"samples\": [\n          \"/content/datasets/images/a (34).jpg\",\n          \"/content/datasets/images/a (57).jpg\",\n          \"/content/datasets/images/a (102).jpg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40,\n        \"min\": 1,\n        \"max\": 161,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          24,\n          38,\n          95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48,\n        \"min\": 1,\n        \"max\": 177,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          55,\n          54,\n          45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42,\n        \"min\": 55,\n        \"max\": 222,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          166,\n          183,\n          178\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38,\n        \"min\": 100,\n        \"max\": 224,\n        \"num_unique_values\": 69,\n        \"samples\": [\n          167,\n          173,\n          205\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "img = plt.imread(data[0][0])\n",
+        "plt.imshow(img)\n",
+        "print('xmin', data[1][0], 'ymin',data[2][0],'xmax',data[3][0],'ymax',data[4][0])"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:06:58.523352Z",
+          "iopub.execute_input": "2021-08-07T05:06:58.523693Z",
+          "iopub.status.idle": "2021-08-07T05:06:58.755975Z",
+          "shell.execute_reply.started": "2021-08-07T05:06:58.523659Z",
+          "shell.execute_reply": "2021-08-07T05:06:58.754887Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 451
+        },
+        "id": "0hUl1AoqJs8-",
+        "outputId": "34e28b85-cab4-4dac-bc04-1ee390ce5878"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "xmin 38 ymin 54 xmax 179 ymax 173\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(img.shape)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:07:01.241703Z",
+          "iopub.execute_input": "2021-08-07T05:07:01.24207Z",
+          "iopub.status.idle": "2021-08-07T05:07:01.247261Z",
+          "shell.execute_reply.started": "2021-08-07T05:07:01.242024Z",
+          "shell.execute_reply": "2021-08-07T05:07:01.246389Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6xi_tctuJs8-",
+        "outputId": "818a4a07-a00b-443c-c270-32d187303f4d"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(224, 224, 3)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Define parameters\n",
+        "width = 5\n",
+        "height = 5\n",
+        "rows = 3\n",
+        "cols = 3\n",
+        "axes = []\n",
+        "\n",
+        "# Create figure\n",
+        "fig = plt.figure(figsize=(width, height))\n",
+        "\n",
+        "# Plot images\n",
+        "for a in range(rows * cols):\n",
+        "    c = np.random.randint(100)\n",
+        "    img = plt.imread(data[0][c])\n",
+        "    axes.append(fig.add_subplot(rows, cols, a + 1))\n",
+        "    subplot_title = \"Subplot\" + str(a)\n",
+        "    axes[-1].set_title(subplot_title)\n",
+        "    plt.imshow(img)\n",
+        "\n",
+        "# Adjust layout\n",
+        "fig.tight_layout()\n",
+        "\n",
+        "# Save the plot\n",
+        "plt.savefig('plot.png')\n",
+        "\n",
+        "# Show the plot (optional)\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:07:03.50641Z",
+          "iopub.execute_input": "2021-08-07T05:07:03.506748Z",
+          "iopub.status.idle": "2021-08-07T05:07:04.657711Z",
+          "shell.execute_reply.started": "2021-08-07T05:07:03.506716Z",
+          "shell.execute_reply": "2021-08-07T05:07:04.656819Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 507
+        },
+        "id": "1FR0IXb1Js8_",
+        "outputId": "24695c57-8916-4031-b02a-609f443913ff"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x500 with 9 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "x_min samples"
+      ],
+      "metadata": {
+        "id": "IS8w1e_WJs8_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.histplot(data[1])\n",
+        "\n",
+        "\n",
+        "# Save the plot\n",
+        "plt.savefig('histogram-xmin.png')\n",
+        "\n",
+        "# Show the plot (optional)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:07:07.219508Z",
+          "iopub.execute_input": "2021-08-07T05:07:07.219849Z",
+          "iopub.status.idle": "2021-08-07T05:07:07.414364Z",
+          "shell.execute_reply.started": "2021-08-07T05:07:07.219818Z",
+          "shell.execute_reply": "2021-08-07T05:07:07.413523Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "rwuugywgJs9A",
+        "outputId": "0f4844a3-cc5a-476a-d047-e8e9cadea9c2"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "y_min samples"
+      ],
+      "metadata": {
+        "id": "NL2nW1GIJs9A"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.histplot(data[2])\n",
+        "# Save the plot\n",
+        "plt.savefig('histogram-ymin.png')\n",
+        "\n",
+        "# Show the plot (optional)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:07:09.436247Z",
+          "iopub.execute_input": "2021-08-07T05:07:09.43657Z",
+          "iopub.status.idle": "2021-08-07T05:07:09.6159Z",
+          "shell.execute_reply.started": "2021-08-07T05:07:09.436541Z",
+          "shell.execute_reply": "2021-08-07T05:07:09.615163Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "un2OQNIcJs9A",
+        "outputId": "b0f424f5-b4af-44cc-b8fb-b7e734dcca48"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "x_max samples"
+      ],
+      "metadata": {
+        "id": "voT_zXduJs9A"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.histplot(data[3])\n",
+        "# Save the plot\n",
+        "plt.savefig('histogram-xmax.png')\n",
+        "\n",
+        "# Show the plot (optional)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:07:12.131263Z",
+          "iopub.execute_input": "2021-08-07T05:07:12.131604Z",
+          "iopub.status.idle": "2021-08-07T05:07:12.305893Z",
+          "shell.execute_reply.started": "2021-08-07T05:07:12.131565Z",
+          "shell.execute_reply": "2021-08-07T05:07:12.305157Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "MHyaHPMLJs9B",
+        "outputId": "b7bdd050-8fcb-4abe-a22d-c7215d6d2014"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "y_max samples"
+      ],
+      "metadata": {
+        "id": "6mOe3TCkJs9B"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.histplot(data[4])\n",
+        "# Save the plot\n",
+        "plt.savefig('histogram-ymax.png')\n",
+        "\n",
+        "# Show the plot (optional)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:07:14.608464Z",
+          "iopub.execute_input": "2021-08-07T05:07:14.608791Z",
+          "iopub.status.idle": "2021-08-07T05:07:14.782839Z",
+          "shell.execute_reply.started": "2021-08-07T05:07:14.608758Z",
+          "shell.execute_reply": "2021-08-07T05:07:14.781803Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "UOJPoMUMJs9B",
+        "outputId": "aa9d0d2a-e825-43fa-d24f-4bb9552a40a3"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "def cm(xmin, xmax):\n",
+        "    return (xmin + xmax) / 2\n",
+        "\n",
+        "# Apply the cm function to calculate the center x and y coordinates\n",
+        "data['cx'] = data.apply(lambda r: cm(r[1], r[3]), axis=1)\n",
+        "data['cy'] = data.apply(lambda r: cm(r[2], r[4]), axis=1)\n"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:07:18.381399Z",
+          "iopub.execute_input": "2021-08-07T05:07:18.381733Z",
+          "iopub.status.idle": "2021-08-07T05:07:18.39918Z",
+          "shell.execute_reply.started": "2021-08-07T05:07:18.381702Z",
+          "shell.execute_reply": "2021-08-07T05:07:18.398397Z"
+        },
+        "trusted": true,
+        "id": "2cyyRhOKJs9C"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.scatterplot(data=data, x=\"cx\", y=\"cy\")\n",
+        "# Save the plot\n",
+        "plt.savefig('Distribution.png')\n",
+        "\n",
+        "# Show the plot (optional)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:07:22.709299Z",
+          "iopub.execute_input": "2021-08-07T05:07:22.709623Z",
+          "iopub.status.idle": "2021-08-07T05:07:22.922336Z",
+          "shell.execute_reply.started": "2021-08-07T05:07:22.709592Z",
+          "shell.execute_reply": "2021-08-07T05:07:22.921292Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "5WoB_7_2Js9C",
+        "outputId": "6a7137df-2f78-467f-b16a-b75aced2eb49"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "here we can observer that samples have covered most of the detection region to establish the generalised approaximations in model training. it could have been better we apply some sort of augmentation to enahnce the diversity and varience in data"
+      ],
+      "metadata": {
+        "id": "Xm0p3XtOJs9C"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**box size distribution**"
+      ],
+      "metadata": {
+        "id": "ymB3VSnwJs9C"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "def cm(xmin, ymin, xmax, ymax):\n",
+        "    w = xmax - xmin\n",
+        "    h = ymax - ymin\n",
+        "    return (h+w)/2\n",
+        "\n",
+        "# Assuming 'data' is a DataFrame with columns [1, 2, 3, 4]\n",
+        "data['size'] = data.apply(lambda r: cm(r[1], r[2], r[3], r[4]), axis=1)\n"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:07:28.842987Z",
+          "iopub.execute_input": "2021-08-07T05:07:28.843431Z",
+          "iopub.status.idle": "2021-08-07T05:07:28.860265Z",
+          "shell.execute_reply.started": "2021-08-07T05:07:28.84339Z",
+          "shell.execute_reply": "2021-08-07T05:07:28.859454Z"
+        },
+        "trusted": true,
+        "id": "Upew-U1qJs9D"
+      },
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.histplot(data['size'])\n",
+        "# Save the plot\n",
+        "plt.savefig('box-size-distribution.png')\n",
+        "\n",
+        "# Show the plot (optional)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:07:31.435591Z",
+          "iopub.execute_input": "2021-08-07T05:07:31.435924Z",
+          "iopub.status.idle": "2021-08-07T05:07:31.633036Z",
+          "shell.execute_reply.started": "2021-08-07T05:07:31.435893Z",
+          "shell.execute_reply": "2021-08-07T05:07:31.632245Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "aTZ29MRSJs9D",
+        "outputId": "adb827c4-e65c-466d-9134-f956995d0928"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.scatterplot(data=data, x=\"cx\", y=\"cy\", size = 'size', sizes=(0, 224))\n",
+        "# Save the plot\n",
+        "plt.savefig('size-location.png')\n",
+        "\n",
+        "# Show the plot (optional)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:07:34.546748Z",
+          "iopub.execute_input": "2021-08-07T05:07:34.5471Z",
+          "iopub.status.idle": "2021-08-07T05:07:34.975657Z",
+          "shell.execute_reply.started": "2021-08-07T05:07:34.547069Z",
+          "shell.execute_reply": "2021-08-07T05:07:34.974722Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "RVZVnWxZJs9D",
+        "outputId": "c1bcd7d0-bcb1-493f-e71c-649ca3c76d0b"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Pre Processing"
+      ],
+      "metadata": {
+        "id": "S5N9LEYeJs9E"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def proc_image(path, xmin, ymin, xmax, ymax):\n",
+        "    image = tf.io.read_file(path)\n",
+        "    image = tf.image.decode_jpeg(image, channels=3)\n",
+        "    image = tf.cast(image, tf.float64)\n",
+        "    return image/255.0, (xmin, ymin, xmax, ymax)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:07:38.381495Z",
+          "iopub.execute_input": "2021-08-07T05:07:38.381876Z",
+          "iopub.status.idle": "2021-08-07T05:07:38.387168Z",
+          "shell.execute_reply.started": "2021-08-07T05:07:38.381839Z",
+          "shell.execute_reply": "2021-08-07T05:07:38.385983Z"
+        },
+        "trusted": true,
+        "id": "DsFCCP-oJs9E"
+      },
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.random.seed(127)\n",
+        "bs = 16 #batch size\n",
+        "valid_mask = np.random.rand(len(data)) < 0.2\n",
+        "val = data[valid_mask]\n",
+        "train = data[~valid_mask]"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:22:59.305596Z",
+          "iopub.execute_input": "2021-08-07T05:22:59.305921Z",
+          "iopub.status.idle": "2021-08-07T05:22:59.311769Z",
+          "shell.execute_reply.started": "2021-08-07T05:22:59.30589Z",
+          "shell.execute_reply": "2021-08-07T05:22:59.310983Z"
+        },
+        "trusted": true,
+        "id": "To4PMmexJs9E"
+      },
+      "execution_count": 23,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dataset = tf.data.Dataset.from_tensor_slices((train[0].values, train[1].values, train[2].values ,train[3].values, train[4].values))\n",
+        "dataset = dataset.map(proc_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
+        "dataset = dataset.shuffle(241).cache().repeat().batch(bs).prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n",
+        "\n",
+        "dataset_valid = tf.data.Dataset.from_tensor_slices((val[0].values, val[1].values, val[2].values, val[3].values, val[4].values))\n",
+        "dataset_valid = dataset_valid.map(proc_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
+        "dataset_valid = dataset_valid.shuffle(241).cache().batch(bs).repeat().prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n",
+        "\n",
+        "steps_per_epoch_train = len(train) // bs\n",
+        "validation_steps = len(val) // bs"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:23:01.615538Z",
+          "iopub.execute_input": "2021-08-07T05:23:01.615876Z",
+          "iopub.status.idle": "2021-08-07T05:23:01.648821Z",
+          "shell.execute_reply.started": "2021-08-07T05:23:01.615844Z",
+          "shell.execute_reply": "2021-08-07T05:23:01.648087Z"
+        },
+        "trusted": true,
+        "id": "OWaNUZ3KJs9F"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "visulise data loader"
+      ],
+      "metadata": {
+        "id": "U4Nv9ccaJs9F"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tf.keras.backend.clear_session()\n",
+        "datasetGen = iter(dataset)\n",
+        "batch2 = next(datasetGen)\n",
+        "img = np.array(batch2[0][0],dtype='float32')\n",
+        "# print(np.array(batch2[1],dtype='float32'))\n",
+        "plt.figure(figsize=(6,6))\n",
+        "plt.imshow(img[:,:,0],cmap='gray')"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:23:03.192647Z",
+          "iopub.execute_input": "2021-08-07T05:23:03.193026Z",
+          "iopub.status.idle": "2021-08-07T05:23:03.498987Z",
+          "shell.execute_reply.started": "2021-08-07T05:23:03.192989Z",
+          "shell.execute_reply": "2021-08-07T05:23:03.498212Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 544
+        },
+        "id": "O30KdyFfJs9G",
+        "outputId": "229fa25f-67ef-4988-a234-97bc9946146b"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x78796358b400>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Models"
+      ],
+      "metadata": {
+        "id": "iwBqNGfcNnFC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tensorflow as tf\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Function to create simple ANN model\n",
+        "def create_ann_model():\n",
+        "    model = tf.keras.Sequential([\n",
+        "        tf.keras.layers.Flatten(input_shape=(224, 224, 3)),\n",
+        "        tf.keras.layers.Dense(128, activation='relu'),\n",
+        "        tf.keras.layers.Dense(64, activation='relu'),\n",
+        "        tf.keras.layers.Dense(1, activation=\"linear\")\n",
+        "    ])\n",
+        "    return model\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "vhWvVRnLNEzr"
+      },
+      "execution_count": 26,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def create_cnn_model():\n",
+        "    model = tf.keras.Sequential([\n",
+        "        tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu', input_shape=(224, 224, 3)),\n",
+        "        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
+        "        tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu'),\n",
+        "        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
+        "        tf.keras.layers.Flatten(),\n",
+        "        tf.keras.layers.Dense(128, activation='relu'),\n",
+        "        tf.keras.layers.Dense(64, activation='relu'),\n",
+        "        tf.keras.layers.Dense(1, activation=\"linear\")\n",
+        "    ])\n",
+        "    return model\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "XN2ve_j6NQ-s"
+      },
+      "execution_count": 27,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Function to create simple DNN model\n",
+        "def create_dnn_model():\n",
+        "    model = tf.keras.Sequential([\n",
+        "        tf.keras.layers.Flatten(input_shape=(224, 224, 3)),\n",
+        "        tf.keras.layers.Dense(256, activation='relu'),\n",
+        "        tf.keras.layers.Dense(128, activation='relu'),\n",
+        "        tf.keras.layers.Dense(64, activation='relu'),\n",
+        "        tf.keras.layers.Dense(1, activation=\"linear\")\n",
+        "    ])\n",
+        "    return model\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "U6b2-UIMNTfU"
+      },
+      "execution_count": 28,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Function to create simple LSTM model\n",
+        "def create_lstm_model():\n",
+        "    model = tf.keras.Sequential([\n",
+        "        tf.keras.layers.Reshape((224, 224*3), input_shape=(224, 224, 3)),  # Reshape to (224, 224*3) assuming 3 channels\n",
+        "        tf.keras.layers.LSTM(128),\n",
+        "        tf.keras.layers.Dense(64, activation='relu'),\n",
+        "        tf.keras.layers.Dense(1, activation=\"linear\")\n",
+        "    ])\n",
+        "    return model\n"
+      ],
+      "metadata": {
+        "id": "QozAqmyHNVxQ"
+      },
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Function to create simple RNN model\n",
+        "def create_rnn_model():\n",
+        "    model = tf.keras.Sequential([\n",
+        "        tf.keras.layers.Reshape((224, 224*3), input_shape=(224, 224, 3)),  # Reshape to (224, 224*3) assuming 3 channels\n",
+        "        tf.keras.layers.SimpleRNN(128),\n",
+        "        tf.keras.layers.Dense(64, activation='relu'),\n",
+        "        tf.keras.layers.Dense(1, activation=\"linear\")\n",
+        "    ])\n",
+        "    return model\n"
+      ],
+      "metadata": {
+        "id": "uo-3wM_9NZw4"
+      },
+      "execution_count": 42,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Training and Testing"
+      ],
+      "metadata": {
+        "id": "H2HIElu4P4fE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Compile and train model\n",
+        "def train_model(model, train_dataset, val_dataset, epochs, steps_per_epoch, validation_steps):\n",
+        "    model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n",
+        "    history = model.fit(train_dataset, epochs=epochs, steps_per_epoch=steps_per_epoch, validation_data=val_dataset, validation_steps=validation_steps)\n",
+        "    return history\n",
+        "\n",
+        "\n",
+        "# Plot and save loss and accuracy\n",
+        "def plot_and_save_metrics(history, model_type):\n",
+        "    # Plot loss\n",
+        "    plt.plot(history.history['loss'], label='train_loss')\n",
+        "    plt.plot(history.history['val_loss'], label='val_loss')\n",
+        "    plt.xlabel('Epochs')\n",
+        "    plt.ylabel('Loss')\n",
+        "    plt.title('Loss - ' + model_type)\n",
+        "    plt.legend()\n",
+        "    plt.savefig(model_type + '_loss.png')\n",
+        "    plt.show()\n",
+        "\n",
+        "    # Plot accuracy\n",
+        "    plt.plot(history.history['mae'], label='train_mae')\n",
+        "    plt.plot(history.history['val_mae'], label='val_mae')\n",
+        "    plt.xlabel('Epochs')\n",
+        "    plt.ylabel('Mean Absolute Error')\n",
+        "    plt.title('Mean Absolute Error - ' + model_type)\n",
+        "    plt.legend()\n",
+        "    plt.savefig(model_type + '_mae.png')\n",
+        "    plt.show()\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "6rNMx_jYNbpm"
+      },
+      "execution_count": 33,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Train and evaluate ANN model\n",
+        "ann_model = create_ann_model()\n",
+        "ann_history = train_model(ann_model, dataset, dataset_valid, epochs=50, steps_per_epoch=steps_per_epoch_train, validation_steps=validation_steps)\n",
+        "plot_and_save_metrics(ann_history, 'ANN')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "uDiXejO3Ni8V",
+        "outputId": "1ec35478-1ff3-495f-a9b4-3e31d52cd838"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "5/5 [==============================] - 3s 435ms/step - loss: 49733.9453 - mae: 149.9720 - val_loss: 1644.0210 - val_mae: 35.0331\n",
+            "Epoch 2/50\n",
+            "5/5 [==============================] - 3s 514ms/step - loss: 2130.8188 - mae: 39.1653 - val_loss: 805.5683 - val_mae: 24.1443\n",
+            "Epoch 3/50\n",
+            "5/5 [==============================] - 2s 342ms/step - loss: 1782.4730 - mae: 33.9855 - val_loss: 1527.4417 - val_mae: 33.5884\n",
+            "Epoch 4/50\n",
+            "5/5 [==============================] - 2s 333ms/step - loss: 1836.5602 - mae: 38.6157 - val_loss: 723.5010 - val_mae: 22.6761\n",
+            "Epoch 5/50\n",
+            "5/5 [==============================] - 2s 329ms/step - loss: 1652.0192 - mae: 32.7315 - val_loss: 818.2024 - val_mae: 23.0894\n",
+            "Epoch 6/50\n",
+            "5/5 [==============================] - 2s 328ms/step - loss: 1462.5037 - mae: 33.1374 - val_loss: 687.0244 - val_mae: 21.4304\n",
+            "Epoch 7/50\n",
+            "5/5 [==============================] - 2s 320ms/step - loss: 1252.9652 - mae: 29.2508 - val_loss: 831.6190 - val_mae: 22.9807\n",
+            "Epoch 8/50\n",
+            "5/5 [==============================] - 2s 322ms/step - loss: 1221.2776 - mae: 29.7596 - val_loss: 606.7404 - val_mae: 19.9162\n",
+            "Epoch 9/50\n",
+            "5/5 [==============================] - 2s 446ms/step - loss: 1200.6565 - mae: 28.3178 - val_loss: 778.4974 - val_mae: 21.5073\n",
+            "Epoch 10/50\n",
+            "5/5 [==============================] - 3s 511ms/step - loss: 919.7405 - mae: 25.2328 - val_loss: 527.0009 - val_mae: 17.5617\n",
+            "Epoch 11/50\n",
+            "5/5 [==============================] - 2s 331ms/step - loss: 902.6151 - mae: 23.3869 - val_loss: 844.7025 - val_mae: 20.8250\n",
+            "Epoch 12/50\n",
+            "5/5 [==============================] - 2s 328ms/step - loss: 673.0599 - mae: 20.4240 - val_loss: 641.9578 - val_mae: 17.6215\n",
+            "Epoch 13/50\n",
+            "5/5 [==============================] - 2s 331ms/step - loss: 558.1884 - mae: 17.4978 - val_loss: 621.3369 - val_mae: 17.6370\n",
+            "Epoch 14/50\n",
+            "5/5 [==============================] - 2s 429ms/step - loss: 530.8125 - mae: 16.5773 - val_loss: 676.5546 - val_mae: 18.4058\n",
+            "Epoch 15/50\n",
+            "5/5 [==============================] - 2s 362ms/step - loss: 475.5847 - mae: 15.4212 - val_loss: 781.1066 - val_mae: 20.2803\n",
+            "Epoch 16/50\n",
+            "5/5 [==============================] - 2s 377ms/step - loss: 461.8831 - mae: 15.6749 - val_loss: 799.2012 - val_mae: 20.9121\n",
+            "Epoch 17/50\n",
+            "5/5 [==============================] - 4s 686ms/step - loss: 407.1411 - mae: 14.6511 - val_loss: 869.3254 - val_mae: 22.4833\n",
+            "Epoch 18/50\n",
+            "5/5 [==============================] - 2s 445ms/step - loss: 326.8132 - mae: 13.5956 - val_loss: 841.4714 - val_mae: 22.0537\n",
+            "Epoch 19/50\n",
+            "5/5 [==============================] - 2s 479ms/step - loss: 417.8572 - mae: 15.8596 - val_loss: 716.9517 - val_mae: 19.6479\n",
+            "Epoch 20/50\n",
+            "5/5 [==============================] - 2s 340ms/step - loss: 399.5799 - mae: 15.2955 - val_loss: 630.7036 - val_mae: 18.6546\n",
+            "Epoch 21/50\n",
+            "5/5 [==============================] - 2s 391ms/step - loss: 350.4021 - mae: 15.2284 - val_loss: 668.1797 - val_mae: 20.7376\n",
+            "Epoch 22/50\n",
+            "5/5 [==============================] - 3s 685ms/step - loss: 430.6325 - mae: 16.2323 - val_loss: 621.6353 - val_mae: 19.8809\n",
+            "Epoch 23/50\n",
+            "5/5 [==============================] - 3s 618ms/step - loss: 395.9397 - mae: 15.8769 - val_loss: 568.5025 - val_mae: 17.8943\n",
+            "Epoch 24/50\n",
+            "5/5 [==============================] - 2s 420ms/step - loss: 378.0072 - mae: 15.0780 - val_loss: 620.6595 - val_mae: 18.0298\n",
+            "Epoch 25/50\n",
+            "5/5 [==============================] - 2s 458ms/step - loss: 350.2229 - mae: 14.5456 - val_loss: 733.9224 - val_mae: 20.3367\n",
+            "Epoch 26/50\n",
+            "5/5 [==============================] - 2s 464ms/step - loss: 322.0377 - mae: 13.2827 - val_loss: 827.4551 - val_mae: 22.1111\n",
+            "Epoch 27/50\n",
+            "5/5 [==============================] - 3s 677ms/step - loss: 314.3983 - mae: 13.4618 - val_loss: 681.7850 - val_mae: 19.2353\n",
+            "Epoch 28/50\n",
+            "5/5 [==============================] - 4s 687ms/step - loss: 272.5880 - mae: 12.0147 - val_loss: 612.0491 - val_mae: 17.9925\n",
+            "Epoch 29/50\n",
+            "5/5 [==============================] - 2s 477ms/step - loss: 201.2415 - mae: 10.1171 - val_loss: 566.2783 - val_mae: 17.3153\n",
+            "Epoch 30/50\n",
+            "5/5 [==============================] - 2s 487ms/step - loss: 228.0708 - mae: 10.7270 - val_loss: 553.1390 - val_mae: 17.2852\n",
+            "Epoch 31/50\n",
+            "5/5 [==============================] - 2s 396ms/step - loss: 210.8780 - mae: 10.2630 - val_loss: 547.5500 - val_mae: 17.2159\n",
+            "Epoch 32/50\n",
+            "5/5 [==============================] - 2s 440ms/step - loss: 156.1565 - mae: 9.5620 - val_loss: 568.1254 - val_mae: 17.3241\n",
+            "Epoch 33/50\n",
+            "5/5 [==============================] - 2s 457ms/step - loss: 195.5018 - mae: 9.9278 - val_loss: 597.3405 - val_mae: 17.8379\n",
+            "Epoch 34/50\n",
+            "5/5 [==============================] - 3s 524ms/step - loss: 183.8141 - mae: 10.0336 - val_loss: 582.2071 - val_mae: 17.6260\n",
+            "Epoch 35/50\n",
+            "5/5 [==============================] - 2s 336ms/step - loss: 172.3213 - mae: 9.5079 - val_loss: 537.2581 - val_mae: 16.7425\n",
+            "Epoch 36/50\n",
+            "5/5 [==============================] - 2s 332ms/step - loss: 177.9137 - mae: 9.8947 - val_loss: 507.5401 - val_mae: 16.5049\n",
+            "Epoch 37/50\n",
+            "5/5 [==============================] - 2s 332ms/step - loss: 165.2160 - mae: 9.5010 - val_loss: 488.5917 - val_mae: 16.3954\n",
+            "Epoch 38/50\n",
+            "5/5 [==============================] - 2s 330ms/step - loss: 167.7363 - mae: 9.5951 - val_loss: 476.9801 - val_mae: 16.4465\n",
+            "Epoch 39/50\n",
+            "5/5 [==============================] - 2s 329ms/step - loss: 162.6322 - mae: 9.7117 - val_loss: 475.3996 - val_mae: 16.2496\n",
+            "Epoch 40/50\n",
+            "5/5 [==============================] - 2s 326ms/step - loss: 129.1140 - mae: 8.3616 - val_loss: 471.6900 - val_mae: 16.0644\n",
+            "Epoch 41/50\n",
+            "5/5 [==============================] - 2s 510ms/step - loss: 150.7701 - mae: 9.3012 - val_loss: 491.2726 - val_mae: 15.9742\n",
+            "Epoch 42/50\n",
+            "5/5 [==============================] - 2s 416ms/step - loss: 143.2189 - mae: 8.9321 - val_loss: 530.2983 - val_mae: 16.9008\n",
+            "Epoch 43/50\n",
+            "5/5 [==============================] - 2s 333ms/step - loss: 98.3714 - mae: 7.8944 - val_loss: 594.7503 - val_mae: 18.2696\n",
+            "Epoch 44/50\n",
+            "5/5 [==============================] - 2s 340ms/step - loss: 128.6397 - mae: 8.3022 - val_loss: 675.4655 - val_mae: 19.7030\n",
+            "Epoch 45/50\n",
+            "5/5 [==============================] - 2s 327ms/step - loss: 129.3479 - mae: 8.4699 - val_loss: 640.9165 - val_mae: 19.0775\n",
+            "Epoch 46/50\n",
+            "5/5 [==============================] - 2s 337ms/step - loss: 121.1822 - mae: 8.2727 - val_loss: 565.5638 - val_mae: 17.8511\n",
+            "Epoch 47/50\n",
+            "5/5 [==============================] - 2s 333ms/step - loss: 120.8863 - mae: 8.0062 - val_loss: 491.3216 - val_mae: 16.0355\n",
+            "Epoch 48/50\n",
+            "5/5 [==============================] - 2s 439ms/step - loss: 108.1134 - mae: 7.7316 - val_loss: 458.3440 - val_mae: 15.5364\n",
+            "Epoch 49/50\n",
+            "5/5 [==============================] - 3s 516ms/step - loss: 111.6204 - mae: 7.6542 - val_loss: 444.9803 - val_mae: 15.7053\n",
+            "Epoch 50/50\n",
+            "5/5 [==============================] - 2s 324ms/step - loss: 102.5596 - mae: 7.5352 - val_loss: 445.3107 - val_mae: 15.7126\n"
+          ]
+        },
+        {
+          "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": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cnn_model = create_cnn_model()\n",
+        "cnn_history = train_model(cnn_model, dataset, dataset_valid, epochs=50, steps_per_epoch=steps_per_epoch_train, validation_steps=validation_steps)\n",
+        "plot_and_save_metrics(cnn_history, 'CNN')\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "qWaMBYL-O5vg",
+        "outputId": "b758e12c-b125-48a8-e518-6763e81c3a82"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "5/5 [==============================] - 18s 4s/step - loss: 2448.3848 - mae: 39.6567 - val_loss: 787.5180 - val_mae: 24.0266\n",
+            "Epoch 2/50\n",
+            "5/5 [==============================] - 9s 2s/step - loss: 1778.2379 - mae: 33.4434 - val_loss: 1374.8867 - val_mae: 31.7186\n",
+            "Epoch 3/50\n",
+            "5/5 [==============================] - 9s 2s/step - loss: 1495.2468 - mae: 33.9589 - val_loss: 510.8860 - val_mae: 18.5485\n",
+            "Epoch 4/50\n",
+            "5/5 [==============================] - 10s 2s/step - loss: 683.1439 - mae: 19.5388 - val_loss: 454.5175 - val_mae: 14.5004\n",
+            "Epoch 5/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 322.7522 - mae: 13.6326 - val_loss: 352.9762 - val_mae: 13.3998\n",
+            "Epoch 6/50\n",
+            "5/5 [==============================] - 9s 2s/step - loss: 352.1055 - mae: 14.3442 - val_loss: 288.2514 - val_mae: 12.4462\n",
+            "Epoch 7/50\n",
+            "5/5 [==============================] - 13s 3s/step - loss: 206.0597 - mae: 11.1530 - val_loss: 228.3736 - val_mae: 12.7063\n",
+            "Epoch 8/50\n",
+            "5/5 [==============================] - 12s 3s/step - loss: 146.8421 - mae: 9.0235 - val_loss: 182.6780 - val_mae: 9.8677\n",
+            "Epoch 9/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 98.2313 - mae: 6.9291 - val_loss: 157.2470 - val_mae: 9.7908\n",
+            "Epoch 10/50\n",
+            "5/5 [==============================] - 12s 2s/step - loss: 37.9354 - mae: 4.8636 - val_loss: 144.9210 - val_mae: 9.0366\n",
+            "Epoch 11/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 50.4917 - mae: 4.8356 - val_loss: 125.0602 - val_mae: 8.5905\n",
+            "Epoch 12/50\n",
+            "5/5 [==============================] - 10s 2s/step - loss: 35.0886 - mae: 4.2313 - val_loss: 106.4498 - val_mae: 8.2692\n",
+            "Epoch 13/50\n",
+            "5/5 [==============================] - 10s 2s/step - loss: 18.4557 - mae: 2.5679 - val_loss: 94.8975 - val_mae: 7.7683\n",
+            "Epoch 14/50\n",
+            "5/5 [==============================] - 9s 2s/step - loss: 11.8244 - mae: 1.8254 - val_loss: 90.8303 - val_mae: 7.4734\n",
+            "Epoch 15/50\n",
+            "5/5 [==============================] - 9s 2s/step - loss: 7.6281 - mae: 1.7722 - val_loss: 86.5871 - val_mae: 7.3519\n",
+            "Epoch 16/50\n",
+            "5/5 [==============================] - 10s 2s/step - loss: 5.8258 - mae: 1.4926 - val_loss: 86.4070 - val_mae: 7.5541\n",
+            "Epoch 17/50\n",
+            "5/5 [==============================] - 8s 2s/step - loss: 3.8989 - mae: 1.4375 - val_loss: 87.6760 - val_mae: 7.1795\n",
+            "Epoch 18/50\n",
+            "5/5 [==============================] - 10s 2s/step - loss: 4.0573 - mae: 1.5140 - val_loss: 85.1685 - val_mae: 7.0518\n",
+            "Epoch 19/50\n",
+            "5/5 [==============================] - 10s 2s/step - loss: 3.2291 - mae: 1.4619 - val_loss: 83.9709 - val_mae: 7.5350\n",
+            "Epoch 20/50\n",
+            "5/5 [==============================] - 8s 2s/step - loss: 4.4700 - mae: 1.7346 - val_loss: 79.1320 - val_mae: 6.9336\n",
+            "Epoch 21/50\n",
+            "5/5 [==============================] - 10s 2s/step - loss: 5.4626 - mae: 1.7687 - val_loss: 94.8905 - val_mae: 7.7076\n",
+            "Epoch 22/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 12.0304 - mae: 2.8608 - val_loss: 85.8319 - val_mae: 7.3702\n",
+            "Epoch 23/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 12.4760 - mae: 2.7214 - val_loss: 109.2993 - val_mae: 8.5464\n",
+            "Epoch 24/50\n",
+            "5/5 [==============================] - 10s 2s/step - loss: 18.7005 - mae: 3.6258 - val_loss: 106.7962 - val_mae: 8.3060\n",
+            "Epoch 25/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 20.9737 - mae: 4.0027 - val_loss: 89.0940 - val_mae: 7.5724\n",
+            "Epoch 26/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 30.2016 - mae: 4.0905 - val_loss: 106.7192 - val_mae: 8.3115\n",
+            "Epoch 27/50\n",
+            "5/5 [==============================] - 9s 2s/step - loss: 18.1351 - mae: 3.5947 - val_loss: 101.4223 - val_mae: 8.0495\n",
+            "Epoch 28/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 8.9535 - mae: 2.1251 - val_loss: 93.6882 - val_mae: 7.5555\n",
+            "Epoch 29/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 4.7131 - mae: 1.6872 - val_loss: 90.8591 - val_mae: 7.8070\n",
+            "Epoch 30/50\n",
+            "5/5 [==============================] - 10s 2s/step - loss: 2.3513 - mae: 1.2627 - val_loss: 91.1407 - val_mae: 7.3537\n",
+            "Epoch 31/50\n",
+            "5/5 [==============================] - 9s 2s/step - loss: 2.3569 - mae: 1.2709 - val_loss: 87.8974 - val_mae: 7.2587\n",
+            "Epoch 32/50\n",
+            "5/5 [==============================] - 12s 3s/step - loss: 1.7213 - mae: 1.0106 - val_loss: 86.6196 - val_mae: 7.4363\n",
+            "Epoch 33/50\n",
+            "5/5 [==============================] - 12s 2s/step - loss: 1.6080 - mae: 1.0963 - val_loss: 89.3779 - val_mae: 7.3986\n",
+            "Epoch 34/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 2.7483 - mae: 1.3155 - val_loss: 84.9973 - val_mae: 7.1117\n",
+            "Epoch 35/50\n",
+            "5/5 [==============================] - 9s 2s/step - loss: 1.6764 - mae: 0.9750 - val_loss: 86.6467 - val_mae: 7.4805\n",
+            "Epoch 36/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 3.8172 - mae: 1.5655 - val_loss: 91.5689 - val_mae: 7.4531\n",
+            "Epoch 37/50\n",
+            "5/5 [==============================] - 10s 2s/step - loss: 3.3545 - mae: 1.3476 - val_loss: 91.4353 - val_mae: 7.5127\n",
+            "Epoch 38/50\n",
+            "5/5 [==============================] - 10s 2s/step - loss: 4.3732 - mae: 1.6930 - val_loss: 91.7347 - val_mae: 7.9155\n",
+            "Epoch 39/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 9.6120 - mae: 2.3673 - val_loss: 85.5010 - val_mae: 7.2092\n",
+            "Epoch 40/50\n",
+            "5/5 [==============================] - 12s 2s/step - loss: 5.8106 - mae: 1.8159 - val_loss: 106.7557 - val_mae: 8.2423\n",
+            "Epoch 41/50\n",
+            "5/5 [==============================] - 14s 3s/step - loss: 13.1453 - mae: 2.8612 - val_loss: 93.2959 - val_mae: 7.8950\n",
+            "Epoch 42/50\n",
+            "5/5 [==============================] - 13s 3s/step - loss: 12.6418 - mae: 2.7094 - val_loss: 91.3726 - val_mae: 7.9518\n",
+            "Epoch 43/50\n",
+            "5/5 [==============================] - 9s 2s/step - loss: 14.2801 - mae: 2.9557 - val_loss: 108.9846 - val_mae: 8.4152\n",
+            "Epoch 44/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 24.6748 - mae: 3.9189 - val_loss: 86.5125 - val_mae: 7.1840\n",
+            "Epoch 45/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 11.6572 - mae: 2.6950 - val_loss: 97.8820 - val_mae: 8.0638\n",
+            "Epoch 46/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 22.0294 - mae: 3.4978 - val_loss: 109.2332 - val_mae: 8.4767\n",
+            "Epoch 47/50\n",
+            "5/5 [==============================] - 9s 2s/step - loss: 14.4446 - mae: 3.2331 - val_loss: 90.1137 - val_mae: 7.6207\n",
+            "Epoch 48/50\n",
+            "5/5 [==============================] - 10s 2s/step - loss: 15.2106 - mae: 2.7983 - val_loss: 98.2597 - val_mae: 8.1154\n",
+            "Epoch 49/50\n",
+            "5/5 [==============================] - 11s 2s/step - loss: 10.8783 - mae: 2.6914 - val_loss: 96.5248 - val_mae: 7.7117\n",
+            "Epoch 50/50\n",
+            "5/5 [==============================] - 10s 2s/step - loss: 6.4726 - mae: 1.7975 - val_loss: 97.9523 - val_mae: 7.8286\n"
+          ]
+        },
+        {
+          "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": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Train and evaluate DNN model\n",
+        "dnn_model = create_dnn_model()\n",
+        "dnn_history = train_model(dnn_model, dataset, dataset_valid, epochs=50, steps_per_epoch=steps_per_epoch_train, validation_steps=validation_steps)\n",
+        "plot_and_save_metrics(dnn_history, 'DNN')\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "Nrwef7KXPGWd",
+        "outputId": "5e9187cb-cda9-4687-ba9d-8afe5156f85a"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "5/5 [==============================] - 6s 1s/step - loss: 34221.2539 - mae: 118.7619 - val_loss: 1306.6008 - val_mae: 31.0101\n",
+            "Epoch 2/50\n",
+            "5/5 [==============================] - 4s 687ms/step - loss: 1980.2308 - mae: 34.8069 - val_loss: 915.0782 - val_mae: 24.8136\n",
+            "Epoch 3/50\n",
+            "5/5 [==============================] - 3s 660ms/step - loss: 1819.7764 - mae: 33.5458 - val_loss: 1189.2520 - val_mae: 28.8629\n",
+            "Epoch 4/50\n",
+            "5/5 [==============================] - 3s 658ms/step - loss: 1549.6250 - mae: 29.8217 - val_loss: 2111.7422 - val_mae: 38.9661\n",
+            "Epoch 5/50\n",
+            "5/5 [==============================] - 5s 1s/step - loss: 1574.2644 - mae: 30.6639 - val_loss: 1956.7495 - val_mae: 37.3138\n",
+            "Epoch 6/50\n",
+            "5/5 [==============================] - 3s 655ms/step - loss: 1233.2859 - mae: 26.4004 - val_loss: 1583.3079 - val_mae: 32.1900\n",
+            "Epoch 7/50\n",
+            "5/5 [==============================] - 3s 662ms/step - loss: 1168.4636 - mae: 26.7761 - val_loss: 953.2608 - val_mae: 22.3754\n",
+            "Epoch 8/50\n",
+            "5/5 [==============================] - 4s 769ms/step - loss: 1005.8885 - mae: 24.8235 - val_loss: 538.0089 - val_mae: 19.2407\n",
+            "Epoch 9/50\n",
+            "5/5 [==============================] - 5s 906ms/step - loss: 854.3976 - mae: 23.2784 - val_loss: 737.7866 - val_mae: 24.3717\n",
+            "Epoch 10/50\n",
+            "5/5 [==============================] - 3s 689ms/step - loss: 734.9697 - mae: 21.7606 - val_loss: 715.2418 - val_mae: 24.1107\n",
+            "Epoch 11/50\n",
+            "5/5 [==============================] - 4s 723ms/step - loss: 668.0310 - mae: 19.9716 - val_loss: 509.6779 - val_mae: 19.6636\n",
+            "Epoch 12/50\n",
+            "5/5 [==============================] - 4s 922ms/step - loss: 540.1501 - mae: 17.8076 - val_loss: 471.9535 - val_mae: 16.4223\n",
+            "Epoch 13/50\n",
+            "5/5 [==============================] - 4s 856ms/step - loss: 442.6076 - mae: 15.5489 - val_loss: 621.7040 - val_mae: 16.2588\n",
+            "Epoch 14/50\n",
+            "5/5 [==============================] - 12s 2s/step - loss: 424.1061 - mae: 15.6648 - val_loss: 654.6244 - val_mae: 17.1851\n",
+            "Epoch 15/50\n",
+            "5/5 [==============================] - 8s 1s/step - loss: 342.3528 - mae: 13.4017 - val_loss: 658.9028 - val_mae: 16.8600\n",
+            "Epoch 16/50\n",
+            "5/5 [==============================] - 3s 688ms/step - loss: 328.5901 - mae: 13.8447 - val_loss: 609.7384 - val_mae: 16.4330\n",
+            "Epoch 17/50\n",
+            "5/5 [==============================] - 4s 714ms/step - loss: 265.9065 - mae: 12.0055 - val_loss: 520.8968 - val_mae: 14.6030\n",
+            "Epoch 18/50\n",
+            "5/5 [==============================] - 4s 926ms/step - loss: 214.7503 - mae: 11.1439 - val_loss: 391.1872 - val_mae: 13.5118\n",
+            "Epoch 19/50\n",
+            "5/5 [==============================] - 4s 725ms/step - loss: 249.4223 - mae: 12.1031 - val_loss: 465.7410 - val_mae: 15.4405\n",
+            "Epoch 20/50\n",
+            "5/5 [==============================] - 4s 710ms/step - loss: 222.0821 - mae: 11.2021 - val_loss: 367.0096 - val_mae: 15.1850\n",
+            "Epoch 21/50\n",
+            "5/5 [==============================] - 3s 684ms/step - loss: 184.7638 - mae: 11.0670 - val_loss: 461.8394 - val_mae: 14.9281\n",
+            "Epoch 22/50\n",
+            "5/5 [==============================] - 5s 1s/step - loss: 195.5953 - mae: 10.4413 - val_loss: 415.5745 - val_mae: 13.2729\n",
+            "Epoch 23/50\n",
+            "5/5 [==============================] - 3s 690ms/step - loss: 198.6302 - mae: 10.8716 - val_loss: 550.2747 - val_mae: 15.9961\n",
+            "Epoch 24/50\n",
+            "5/5 [==============================] - 3s 696ms/step - loss: 177.8475 - mae: 10.1505 - val_loss: 421.1919 - val_mae: 13.0965\n",
+            "Epoch 25/50\n",
+            "5/5 [==============================] - 3s 678ms/step - loss: 169.9967 - mae: 9.9946 - val_loss: 438.6275 - val_mae: 13.9904\n",
+            "Epoch 26/50\n",
+            "5/5 [==============================] - 5s 963ms/step - loss: 162.4330 - mae: 9.9170 - val_loss: 446.4919 - val_mae: 13.3283\n",
+            "Epoch 27/50\n",
+            "5/5 [==============================] - 3s 678ms/step - loss: 154.9008 - mae: 9.5087 - val_loss: 458.6077 - val_mae: 14.1533\n",
+            "Epoch 28/50\n",
+            "5/5 [==============================] - 3s 660ms/step - loss: 153.6548 - mae: 9.5455 - val_loss: 328.8624 - val_mae: 12.9283\n",
+            "Epoch 29/50\n",
+            "5/5 [==============================] - 4s 762ms/step - loss: 131.0786 - mae: 8.6591 - val_loss: 337.8116 - val_mae: 12.7422\n",
+            "Epoch 30/50\n",
+            "5/5 [==============================] - 4s 842ms/step - loss: 128.1673 - mae: 8.5932 - val_loss: 315.1667 - val_mae: 12.6618\n",
+            "Epoch 31/50\n",
+            "5/5 [==============================] - 3s 654ms/step - loss: 122.2710 - mae: 8.3092 - val_loss: 323.5367 - val_mae: 13.2391\n",
+            "Epoch 32/50\n",
+            "5/5 [==============================] - 3s 657ms/step - loss: 82.7287 - mae: 7.4134 - val_loss: 337.0270 - val_mae: 12.0144\n",
+            "Epoch 33/50\n",
+            "5/5 [==============================] - 4s 850ms/step - loss: 105.1055 - mae: 7.5413 - val_loss: 361.9127 - val_mae: 12.2035\n",
+            "Epoch 34/50\n",
+            "5/5 [==============================] - 4s 834ms/step - loss: 91.9080 - mae: 6.8544 - val_loss: 426.9525 - val_mae: 13.7854\n",
+            "Epoch 35/50\n",
+            "5/5 [==============================] - 3s 679ms/step - loss: 94.3894 - mae: 6.9053 - val_loss: 325.5836 - val_mae: 11.4461\n",
+            "Epoch 36/50\n",
+            "5/5 [==============================] - 4s 708ms/step - loss: 90.2638 - mae: 6.9356 - val_loss: 448.1050 - val_mae: 14.3262\n",
+            "Epoch 37/50\n",
+            "5/5 [==============================] - 5s 1s/step - loss: 71.3625 - mae: 6.0231 - val_loss: 370.3507 - val_mae: 12.2849\n",
+            "Epoch 38/50\n",
+            "5/5 [==============================] - 4s 695ms/step - loss: 76.0647 - mae: 6.2192 - val_loss: 350.2196 - val_mae: 11.6220\n",
+            "Epoch 39/50\n",
+            "5/5 [==============================] - 3s 678ms/step - loss: 62.0981 - mae: 5.3712 - val_loss: 278.4550 - val_mae: 11.1720\n",
+            "Epoch 40/50\n",
+            "5/5 [==============================] - 3s 683ms/step - loss: 57.0431 - mae: 5.2467 - val_loss: 266.1952 - val_mae: 11.2247\n",
+            "Epoch 41/50\n",
+            "5/5 [==============================] - 5s 1s/step - loss: 56.3099 - mae: 5.1059 - val_loss: 270.0618 - val_mae: 10.9841\n",
+            "Epoch 42/50\n",
+            "5/5 [==============================] - 4s 710ms/step - loss: 53.8969 - mae: 5.2606 - val_loss: 280.7704 - val_mae: 11.2519\n",
+            "Epoch 43/50\n",
+            "5/5 [==============================] - 3s 674ms/step - loss: 34.5241 - mae: 4.5023 - val_loss: 270.2934 - val_mae: 10.7483\n",
+            "Epoch 44/50\n",
+            "5/5 [==============================] - 4s 776ms/step - loss: 51.5367 - mae: 4.8850 - val_loss: 278.6537 - val_mae: 10.6289\n",
+            "Epoch 45/50\n",
+            "5/5 [==============================] - 4s 814ms/step - loss: 43.0714 - mae: 4.6239 - val_loss: 341.4629 - val_mae: 12.1919\n",
+            "Epoch 46/50\n",
+            "5/5 [==============================] - 3s 655ms/step - loss: 44.7374 - mae: 4.5530 - val_loss: 432.0350 - val_mae: 14.7507\n",
+            "Epoch 47/50\n",
+            "5/5 [==============================] - 3s 650ms/step - loss: 42.8791 - mae: 4.7717 - val_loss: 296.9916 - val_mae: 11.0726\n",
+            "Epoch 48/50\n",
+            "5/5 [==============================] - 4s 861ms/step - loss: 55.6782 - mae: 5.8027 - val_loss: 259.1925 - val_mae: 10.2077\n",
+            "Epoch 49/50\n",
+            "5/5 [==============================] - 4s 747ms/step - loss: 66.5341 - mae: 6.5107 - val_loss: 575.6592 - val_mae: 17.7368\n",
+            "Epoch 50/50\n",
+            "5/5 [==============================] - 3s 657ms/step - loss: 148.3877 - mae: 10.4360 - val_loss: 351.3958 - val_mae: 12.5298\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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icqciixVt5253+fv+sCAePrqqfV0HBARAp9PBx8dHmTbHPlHzggUL8PDDDyttg4OD0alTJ+XxK6+8go0bN2LTpk13HK4yYcIEjBo1CgDwj3/8A2+//TYOHTqEAQMGVOtzvfnmm+jXrx9efvllAEDLli3xww8/YPHixZgwYQIyMzPh6+uLRx99FP7+/oiOjsZ9990HQA5NpaWlGD58OKKjowHI8zk6Gw/P1XF6e2hyw183RERUf3Tr1s3hcUFBAWbOnIk2bdogMDAQfn5+OHPmDDIzM++4HfsZ7ADg6+sLg8HgMPVPVZ05cwY9e/Z0eK5nz544d+4crFYrHn74YURHR6NZs2YYO3YsVq9ejZs3bwIAOnXqhH79+qFDhw7485//jH/+85+4ceNGtftQXaw01XH2SlMJK01ERG7lrVXjhwXxv9/QCe9bG357FtzMmTORkpKC119/Hc2bN4e3tzcef/xxlJSU3HE7Wq3W4bEkSbDZav87yt/fH0eOHMHu3buxY8cOzJ07F8nJyTh8+DACAwORkpKC/fv3Y8eOHXjnnXfw0ksv4eDBg2jatGmt98WOlaY6rrzSxNBEROROkiTBR6dx+VLd2ax1Oh2s1t8/OrFv3z5MmDABw4YNQ4cOHRAeHo6LFy/WcO9UX5s2bbBv375b+tSyZUvlUicajQZxcXFYtGgRTpw4gYsXL2Lnzp0A5J9Hz549MX/+fBw9ehQ6nQ4bN250ap9ZaarjWGkiIqLqaNKkCQ4ePIiLFy/Cz8/vtlWgFi1a4Msvv8TgwYMhSRJefvllp1SMbuf5559H9+7d8corr2DkyJFIS0vDsmXL8O677wIANm/ejJ9//hm9e/dGUFAQvv76a9hsNrRq1QoHDx5Eamoq+vfvj9DQUBw8eBBXr15FmzZtnNpnVprqOL1GTtsc00RERFUxc+ZMqNVqtG3bFg0bNrztGKU333wTQUFB+NOf/oTBgwcjPj4eXbp0cVk/u3TpgvXr12Pt2rVo37495s6diwULFmDChAkAgMDAQHz55Zd46KGH0KZNG6xcuRKff/452rVrB4PBgL179+KRRx5By5YtMWfOHLzxxhsYOHCgU/ssCWefn/cHYTKZEBAQgLy8PBgMhlrb7uXrN9Fr0S54aVU4+4pzfxmIiEhWXFyMCxcuoGnTpvDy4sTCnu5OP8/qfH+z0lTHKWOaSm1On3+CiIiIbo+hqY6zj2kSAii1MTQREVHdNGXKFPj5+VW6TJkyxd3dqxUcCF7H2UMTIM8KrlUz5xIRUd2zYMECzJw5s9J1tTlsxZ0Ymuo4XYWQZC61wVfvxs4QERHdRmhoKEJDQ93dDadi2aKO06hVUKvkOTp4/TkiIiL3YWjyAOWDwTntABERkbswNHkAZYJLVpqIiIjchqHJA1ScdoCIiIjcg6HJA+gYmoiIiNyOockD2M+g4+E5IiJytiZNmmDJkiVVaitJEr766iun9qcuYWjyAMr15zgQnIiIyG0YmjwAB4ITERG5H0OTB+BAcCIiqor3338fkZGRsNkcvy+GDBmCJ598EufPn8eQIUMQFhYGPz8/dO/eHd98802tvf/Jkyfx0EMPwdvbGw0aNMDkyZNRUFCgrN+9ezd69OgBX19fBAYGomfPnrh06RIA4Pjx4+jbty/8/f1hMBjQtWtXfP/997XWt9rA0OQBWGkiIqoDhABKCl2/VONi7X/+859x7do17Nq1S3nu+vXr2LZtG8aMGYOCggI88sgjSE1NxdGjRzFgwAAMHjwYmZmZd717CgsLER8fj6CgIBw+fBgbNmzAN998g2nTpgEASktLMXToUDz44IM4ceIE0tLSMHnyZEiSPIHzmDFj0KhRIxw+fBjp6el48cUXodVq77pftYmXUfEA5WOaGJqIiNzGchP4R6Tr3/f/uwLofKvUNCgoCAMHDsSaNWvQr18/AMAXX3yBkJAQ9O3bFyqVCp06dVLav/LKK9i4cSM2bdqkhJuaWrNmDYqLi/Hpp5/C11fu77JlyzB48GC89tpr0Gq1yMvLw6OPPop7770XANCmTRvl9ZmZmZg1axZat24NAGjRosVd9ccZWGnyAHql0sSB4EREdGdjxozBv//9b5jNZgDA6tWr8cQTT0ClUqGgoAAzZ85EmzZtEBgYCD8/P5w5c6ZWKk1nzpxBp06dlMAEAD179oTNZkNGRgaCg4MxYcIExMfHY/DgwVi6dCmysrKUtomJiXjqqacQFxeHV199FefPn7/rPtU2Vpo8gBKarKw0ERG5jdZHrvq4432rYfDgwRBCYMuWLejevTu+/fZbvPXWWwCAmTNnIiUlBa+//jqaN28Ob29vPP744ygpKXFGz2/x8ccf469//Su2bduGdevWYc6cOUhJScH999+P5ORkjB49Glu2bMHWrVsxb948rF27FsOGDXNJ36qCockDKJNbWhiaiIjcRpKqfJjMnby8vDB8+HCsXr0aP/30E1q1aoUuXboAAPbt24cJEyYoQaSgoAAXL16slfdt06YNVq1ahcLCQqXatG/fPqhUKrRq1Uppd9999+G+++5DUlISYmNjsWbNGtx///0AgJYtW6Jly5aYMWMGRo0ahY8//rhOhSYenvMAOlaaiIioGsaMGYMtW7bgo48+wpgxY5TnW7RogS+//BLHjh3D8ePHMXr06FvOtLub9/Ty8sL48eNx6tQp7Nq1C88++yzGjh2LsLAwXLhwAUlJSUhLS8OlS5ewY8cOnDt3Dm3atEFRURGmTZuG3bt349KlS9i3bx8OHz7sMOapLmClyQNwygEiIqqOhx56CMHBwcjIyMDo0aOV59988008+eST+NOf/oSQkBDMnj0bJpOpVt7Tx8cH27dvx3PPPYfu3bvDx8cHI0aMwJtvvqmsP3v2LD755BNcu3YNERERSEhIwP/93/+htLQU165dw7hx45CdnY2QkBAMHz4c8+fPr5W+1RZJiGqcy0i3ZTKZEBAQgLy8PBgMhlrd9uLtZ7F813lM+FMTJD/Wrla3TUREtyouLsaFCxfQtGlTeHl5ubs7dJfu9POszvc3D895AF5GhYiIyP0YmjyAjofniIjIxVavXg0/P79Kl3bt/phHPTimyQPoOSM4ERG52GOPPYaYmJhK19W1mbpdhaHJA7DSRERErubv7w9/f393d6NO4eE5D6BTs9JERETkbgxNHkCv5UBwIiJ3qK05jMi9auvnyMNzHoCVJiIi19LpdFCpVLhy5QoaNmwInU4HSZLc3S2qJiEESkpKcPXqVahUKuh0urvaHkOTB9BrOaaJiMiVVCoVmjZtiqysLFy54obrzVGt8vHxQePGjaFS3d0BNreGphUrVmDFihXKdW/atWuHuXPnYuDAgQDkyaief/55rF27FmazGfHx8Xj33XcRFhambCMzMxNTp07Frl274Ofnh/Hjx2PhwoXQaMo/2u7du5GYmIjTp08jKioKc+bMwYQJExz6snz5cixevBhGoxGdOnXCO++8gx49ejh9H1SFnpUmIiKX0+l0aNy4MUpLS2G1cniEp1Kr1dBoNLVSKXRraGrUqBFeffVVtGjRAkIIfPLJJxgyZAiOHj2Kdu3aYcaMGdiyZQs2bNiAgIAATJs2DcOHD8e+ffsAAFarFYMGDUJ4eDj279+PrKwsjBs3DlqtFv/4xz8AABcuXMCgQYMwZcoUrF69GqmpqXjqqacQERGB+Ph4AMC6deuQmJiIlStXIiYmBkuWLEF8fDwyMjIQGhrqtv1jx0oTEZF7SJIErVb7hz3Fnn5D1DFBQUHigw8+ELm5uUKr1YoNGzYo686cOSMAiLS0NCGEEF9//bVQqVTCaDQqbVasWCEMBoMwm81CCCFeeOEF0a5dO4f3GDlypIiPj1ce9+jRQyQkJCiPrVariIyMFAsXLqxyv/Py8gQAkZeXV70PXAUnLueK6NmbRczfv6n1bRMREf2RVef7u86cPWe1WrF27VoUFhYiNjYW6enpsFgsiIuLU9q0bt0ajRs3RlpaGgAgLS0NHTp0cDhcFx8fD5PJhNOnTyttKm7D3sa+jZKSEqSnpzu0UalUiIuLU9pUxmw2w2QyOSzOYp+nqcTKShMREZG7uD00nTx5En5+ftDr9ZgyZQo2btyItm3bwmg0QqfTITAw0KF9WFgYjEYjAMBoNDoEJvt6+7o7tTGZTCgqKsKvv/4Kq9VaaRv7NiqzcOFCBAQEKEtUVFSNPn9V2GcEN1t4TJ2IiMhd3B6aWrVqhWPHjuHgwYOYOnUqxo8fjx9++MHd3fpdSUlJyMvLU5bLly877b1YaSIiInI/t085oNPp0Lx5cwBA165dcfjwYSxduhQjR45ESUkJcnNzHapN2dnZCA8PBwCEh4fj0KFDDtvLzs5W1tlv7c9VbGMwGODt7Q21Wg21Wl1pG/s2KqPX66HX62v2oavJXmmyWAVsNgGVinOFEBERuZrbK02/ZbPZYDab0bVrV2i1WqSmpirrMjIykJmZidjYWABAbGwsTp48iZycHKVNSkoKDAYD2rZtq7SpuA17G/s2dDodunbt6tDGZrMhNTVVaeNu9koTwGoTERGRu7i10pSUlISBAweicePGyM/Px5o1a7B7925s374dAQEBmDRpEhITExEcHAyDwYBnn30WsbGxuP/++wEA/fv3R9u2bTF27FgsWrQIRqMRc+bMQUJCglIFmjJlCpYtW4YXXngBTz75JHbu3In169djy5YtSj8SExMxfvx4dOvWDT169MCSJUtQWFiIiRMnumW//JZeo1bumy02eGnVd2hNREREzuDW0JSTk4Nx48YhKysLAQEB6NixI7Zv346HH34YAPDWW29BpVJhxIgRDpNb2qnVamzevBlTp05FbGwsfH19MX78eCxYsEBp07RpU2zZsgUzZszA0qVL0ahRI3zwwQfKHE0AMHLkSFy9ehVz586F0WhE586dsW3btlsGh7uLVl1+OM5stQLgfCFERESuJgkhhLs7UR+YTCYEBAQgLy8PBoOh1rffas5WmEtt+G52XzQK8qn17RMREf0RVef7u86NaaLK2cc1cVZwIiIi92Bo8hD2M+h4/TkiIiL3YGjyEPbB4Kw0ERERuQdDk4fQsdJERETkVgxNHkK5lEopL6VCRETkDgxNHoKVJiIiIvdiaPIQHAhORETkXgxNHoJTDhAREbkXQ5OH0KlZaSIiInInhiYPUT7lAAeCExERuQNDk4fg4TkiIiL3YmjyEHqGJiIiIrdiaPIQnHKAiIjIvRiaPIR9TFOJlaGJiIjIHRiaPIQypsnC0EREROQODE0eQpnc0sqz54iIiNyBoclDsNJERETkXgxNHqK80sTQRERE5A4MTR5Cz0oTERGRWzE0eQgdK01ERERuxdDkIXgZFSIiIvdiaPIQnNySiIjIvRiaPISeoYmIiMitGJo8BC/YS0RE5F4MTR5Cp2aliYiIyJ0YmjyEXmsfCM7QRERE5A4MTR7CXmliaCIiInIPhiYPodfaQxOnHCAiInIHhiYPwTFNRERE7sXQ5CHslaYSqw1CCDf3hoiI6I+HoclD6NXyQHAhAIuVoYmIiMjVGJo8hH2eJoDXnyMiInIHhiYPUTE0mS0cDE5ERORqDE0eQq2SoFFJAFhpIiIicgeGJg9iv/6c2cLQRERE5GoMTR7EfoiOlSYiIiLXY2jyIHqNfAYd52oiIiJyPYYmD2KvNHFWcCIiItdza2hauHAhunfvDn9/f4SGhmLo0KHIyMhwaNOnTx9IkuSwTJkyxaFNZmYmBg0aBB8fH4SGhmLWrFkoLS11aLN792506dIFer0ezZs3x6pVq27pz/Lly9GkSRN4eXkhJiYGhw4dqvXPfDeUMU2sNBEREbmcW0PTnj17kJCQgAMHDiAlJQUWiwX9+/dHYWGhQ7unn34aWVlZyrJo0SJlndVqxaBBg1BSUoL9+/fjk08+wapVqzB37lylzYULFzBo0CD07dsXx44dw/Tp0/HUU09h+/btSpt169YhMTER8+bNw5EjR9CpUyfEx8cjJyfH+TuiinQMTURERG4jiTp0TY6rV68iNDQUe/bsQe/evQHIlabOnTtjyZIllb5m69atePTRR3HlyhWEhYUBAFauXInZs2fj6tWr0Ol0mD17NrZs2YJTp04pr3viiSeQm5uLbdu2AQBiYmLQvXt3LFu2DABgs9kQFRWFZ599Fi+++OLv9t1kMiEgIAB5eXkwGAx3sxtua9i7+3A0Mxfvje2K+HbhTnkPIiKiP5LqfH/XqTFNeXl5AIDg4GCH51evXo2QkBC0b98eSUlJuHnzprIuLS0NHTp0UAITAMTHx8NkMuH06dNKm7i4OIdtxsfHIy0tDQBQUlKC9PR0hzYqlQpxcXFKm7qAh+eIiIjcR+PuDtjZbDZMnz4dPXv2RPv27ZXnR48ejejoaERGRuLEiROYPXs2MjIy8OWXXwIAjEajQ2ACoDw2Go13bGMymVBUVIQbN27AarVW2ubs2bOV9tdsNsNsNiuPTSZTDT951el49hwREZHb1JnQlJCQgFOnTuG7775zeH7y5MnK/Q4dOiAiIgL9+vXD+fPnce+997q6m4qFCxdi/vz5Ln1PPc+eIyIicps6cXhu2rRp2Lx5M3bt2oVGjRrdsW1MTAwA4KeffgIAhIeHIzs726GN/XF4ePgd2xgMBnh7eyMkJARqtbrSNvZt/FZSUhLy8vKU5fLly1X8tDWnTG7JShMREZHLuTU0CSEwbdo0bNy4ETt37kTTpk1/9zXHjh0DAERERAAAYmNjcfLkSYez3FJSUmAwGNC2bVulTWpqqsN2UlJSEBsbCwDQ6XTo2rWrQxubzYbU1FSlzW/p9XoYDAaHxdn0DE1ERERu49bDcwkJCVizZg3+85//wN/fXxmDFBAQAG9vb5w/fx5r1qzBI488ggYNGuDEiROYMWMGevfujY4dOwIA+vfvj7Zt22Ls2LFYtGgRjEYj5syZg4SEBOj1egDAlClTsGzZMrzwwgt48sknsXPnTqxfvx5btmxR+pKYmIjx48ejW7du6NGjB5YsWYLCwkJMnDjR9TvmNjgQnIiIyI2EGwGodPn444+FEEJkZmaK3r17i+DgYKHX60Xz5s3FrFmzRF5ensN2Ll68KAYOHCi8vb1FSEiIeP7554XFYnFos2vXLtG5c2eh0+lEs2bNlPeo6J133hGNGzcWOp1O9OjRQxw4cKDKnyUvL08AuKVvtWnuVydF9OzNYvG2s057DyIioj+S6nx/16l5mjyZK+Zp+sfXZ/D+3p/xdK+meGlQW6e8BxER0R+Jx87TRHemU3NMExERkbswNHkQjmkiIiJyH4YmD8IpB4iIiNyHocmDKJUmK0MTERGRqzE0eRD7ZVTMFoYmIiIiV2No8iDK4TlWmoiIiFyOocmDKIfnLLz2HBERkasxNHkQVpqIiIjch6HJg5RXmhiaiIiIXI2hyYOw0kREROQ+DE0eRF929hznaSIiInI9hiYPUj4jOAeCExERuRpDkwfRc0ZwIiIit2Fo8iA6XnuOiIjIbRiaPAivPUdEROQ+DE0exD4QvNQmYLUJN/eGiIjoj4WhyYPYK00Aq01ERESuxtDkQfQMTURERG7D0ORBNCoJkiTf57QDRERErsXQ5EEkSaowVxMrTURERK7E0ORhdGqGJiIiIndgaPIwOl5KhYiIyC0YmjwML6VCRETkHgxNHoaXUiEiInIPhiYPw0upEBERuQdDk4dhpYmIiMg9GJo8jP1SKiVWhiYiIiJXYmjyMDoOBCciInILhiYPw8NzRERE7sHQ5GE4EJyIiMg9GJo8jI6VJiIiIrdgaPIwvPYcERGRezA0eRgeniMiInIPhiYPo+e154iIiNyCocnDcMoBIiIi92Bo8jCccoCIiMg9GJo8DMc0ERERuQdDk4fRqVlpIiIicge3hqaFCxeie/fu8Pf3R2hoKIYOHYqMjAyHNsXFxUhISECDBg3g5+eHESNGIDs726FNZmYmBg0aBB8fH4SGhmLWrFkoLS11aLN792506dIFer0ezZs3x6pVq27pz/Lly9GkSRN4eXkhJiYGhw4dqvXPfLf0WnkgOMc0ERERuZZbQ9OePXuQkJCAAwcOICUlBRaLBf3790dhYaHSZsaMGfjvf/+LDRs2YM+ePbhy5QqGDx+urLdarRg0aBBKSkqwf/9+fPLJJ1i1ahXmzp2rtLlw4QIGDRqEvn374tixY5g+fTqeeuopbN++XWmzbt06JCYmYt68eThy5Ag6deqE+Ph45OTkuGZnVJGelSYiIiL3EHVITk6OACD27NkjhBAiNzdXaLVasWHDBqXNmTNnBACRlpYmhBDi66+/FiqVShiNRqXNihUrhMFgEGazWQghxAsvvCDatWvn8F4jR44U8fHxyuMePXqIhIQE5bHVahWRkZFi4cKFVep7Xl6eACDy8vKq+amr56ujv4jo2ZvF6H+mOfV9iIiI/giq8/1dp8Y05eXlAQCCg4MBAOnp6bBYLIiLi1PatG7dGo0bN0ZaWhoAIC0tDR06dEBYWJjSJj4+HiaTCadPn1baVNyGvY19GyUlJUhPT3doo1KpEBcXp7SpK+xjmswWVpqIiIhcSePuDtjZbDZMnz4dPXv2RPv27QEARqMROp0OgYGBDm3DwsJgNBqVNhUDk329fd2d2phMJhQVFeHGjRuwWq2Vtjl79myl/TWbzTCbzcpjk8lUzU9cM3pt2eE5K0MTERGRK9WZSlNCQgJOnTqFtWvXursrVbJw4UIEBAQoS1RUlEveV6cuGwjOShMREZFL1YnQNG3aNGzevBm7du1Co0aNlOfDw8NRUlKC3Nxch/bZ2dkIDw9X2vz2bDr7499rYzAY4O3tjZCQEKjV6krb2LfxW0lJScjLy1OWy5cvV/+D14B9niZWmoiIiFzLraFJCIFp06Zh48aN2LlzJ5o2beqwvmvXrtBqtUhNTVWey8jIQGZmJmJjYwEAsbGxOHnypMNZbikpKTAYDGjbtq3SpuI27G3s29DpdOjatatDG5vNhtTUVKXNb+n1ehgMBofFFewzgpstnHKAiIjIldw6pikhIQFr1qzBf/7zH/j7+ytjkAICAuDt7Y2AgABMmjQJiYmJCA4OhsFgwLPPPovY2Fjcf//9AID+/fujbdu2GDt2LBYtWgSj0Yg5c+YgISEBer0eADBlyhQsW7YML7zwAp588kns3LkT69evx5YtW5S+JCYmYvz48ejWrRt69OiBJUuWoLCwEBMnTnT9jrkDVpqIiIjcxPkn890egEqXjz/+WGlTVFQknnnmGREUFCR8fHzEsGHDRFZWlsN2Ll68KAYOHCi8vb1FSEiIeP7554XFYnFos2vXLtG5c2eh0+lEs2bNHN7D7p133hGNGzcWOp1O9OjRQxw4cKDKn8VVUw6cz8kX0bM3i/Zztzn1fYiIiP4IqvP9LQkhhPsiW/1hMpkQEBCAvLw8px6q++XGTTzw2i7oNCr8+LeBTnsfIiKiP4LqfH/XiYHgVHV6jXz2XEmpDcy7RERErsPQ5GHsY5oAjmsiIiJyJYYmD6OvGJp4/TkiIiKXYWjyMPbLqACAmaGJiIjIZRiaPIxKJUGrlgCw0kRERORKDE0eyD4YnJUmIiIi12Fo8kDKBJcMTURERC7D0OSB9AxNRERELsfQ5IHslSZzKa8/R0RE5CoMTR6IlSYiIiLXq1Founz5Mn755Rfl8aFDhzB9+nS8//77tdYxur3yShNDExERkavUKDSNHj0au3btAgAYjUY8/PDDOHToEF566SUsWLCgVjtIt7LP1cTQRERE5Do1Ck2nTp1Cjx49AADr169H+/btsX//fqxevRqrVq2qzf5RJcqnHOCYJiIiIlepUWiyWCzQ6/UAgG+++QaPPfYYAKB169bIysqqvd5RpTjlABERkevVKDS1a9cOK1euxLfffouUlBQMGDAAAHDlyhU0aNCgVjtIt1IGgvOCvURERC5To9D02muv4b333kOfPn0watQodOrUCQCwadMm5bAdOY8yENzC0EREROQqmpq8qE+fPvj1119hMpkQFBSkPD958mT4+PjUWueocvYxTaw0ERERuU6NKk1FRUUwm81KYLp06RKWLFmCjIwMhIaG1moH6VasNBEREblejULTkCFD8OmnnwIAcnNzERMTgzfeeANDhw7FihUrarWDdKvyMU08e46IiMhVahSajhw5gl69egEAvvjiC4SFheHSpUv49NNP8fbbb9dqB+lWelaaiIiIXK5GoenmzZvw9/cHAOzYsQPDhw+HSqXC/fffj0uXLtVqB+lWOp49R0RE5HI1Ck3NmzfHV199hcuXL2P79u3o378/ACAnJwcGg6FWO0i34rXniIiIXK9GoWnu3LmYOXMmmjRpgh49eiA2NhaAXHW67777arWDdCtee46IiMj1ajTlwOOPP44HHngAWVlZyhxNANCvXz8MGzas1jpHlVOmHGBoIiIicpkahSYACA8PR3h4OH755RcAQKNGjTixpYuUV5p49hwREZGr1OjwnM1mw4IFCxAQEIDo6GhER0cjMDAQr7zyCmw2Vj+cTc/Dc0RERC5Xo0rTSy+9hA8//BCvvvoqevbsCQD47rvvkJycjOLiYvz973+v1U6SI45pIiIicr0ahaZPPvkEH3zwAR577DHluY4dO+Kee+7BM888w9DkZDo1z54jIiJytRodnrt+/Tpat259y/OtW7fG9evX77pTdGd6rTwQnJUmIiIi16lRaOrUqROWLVt2y/PLli1Dx44d77pTdGfllSYOBCciInKVGh2eW7RoEQYNGoRvvvlGmaMpLS0Nly9fxtdff12rHaRb6bWcEZyIiMjValRpevDBB/Hjjz9i2LBhyM3NRW5uLoYPH47Tp0/js88+q+0+0m/YK0289hwREZHr1HiepsjIyFsGfB8/fhwffvgh3n///bvuGN2eFytNRERELlejShO5l05dNhCclSYiIiKXYWjyQPZ5mlhpIiIich2GJg9knxHcahMoZXAiIiJyiWqNaRo+fPgd1+fm5t5NX6iK7JUmQK42adTMvkRERM5WrdAUEBDwu+vHjRt3Vx2i36evGJpKbfDRubEzREREfxDVCk0ff/yxs/pB1aBRq6CSAJvgrOBERESu4tbjOnv37sXgwYMRGRkJSZLw1VdfOayfMGECJElyWAYMGODQ5vr16xgzZgwMBgMCAwMxadIkFBQUOLQ5ceIEevXqBS8vL0RFRWHRokW39GXDhg1o3bo1vLy80KFDhzo/SadeI59Bx+vPERERuYZbQ1NhYSE6deqE5cuX37bNgAEDkJWVpSyff/65w/oxY8bg9OnTSElJwebNm7F3715MnjxZWW8ymdC/f39ER0cjPT0dixcvRnJyssNcUvv378eoUaMwadIkHD16FEOHDsXQoUNx6tSp2v/QtcQ+rsnMS6kQERG5RI0nt6wNAwcOxMCBA+/YRq/XIzw8vNJ1Z86cwbZt23D48GF069YNAPDOO+/gkUceweuvv47IyEisXr0aJSUl+Oijj6DT6dCuXTscO3YMb775phKuli5digEDBmDWrFkAgFdeeQUpKSlYtmwZVq5cWYufuPboldDEShMREZEr1PnTrnbv3o3Q0FC0atUKU6dOxbVr15R1aWlpCAwMVAITAMTFxUGlUuHgwYNKm969e0OnKx8tHR8fj4yMDNy4cUNpExcX5/C+8fHxSEtLu22/zGYzTCaTw+JKOoYmIiIil6rToWnAgAH49NNPkZqaitdeew179uzBwIEDYbXKh6SMRiNCQ0MdXqPRaBAcHAyj0ai0CQsLc2hjf/x7bezrK7Nw4UIEBAQoS1RU1N192GpSJrhkaCIiInIJtx6e+z1PPPGEcr9Dhw7o2LEj7r33XuzevRv9+vVzY8+ApKQkJCYmKo9NJpNLgxMHghMREblWna40/VazZs0QEhKCn376CQAQHh6OnJwchzalpaW4fv26Mg4qPDwc2dnZDm3sj3+vze3GUgHyWCuDweCwuBIPzxEREbmWR4WmX375BdeuXUNERAQAIDY2Frm5uUhPT1fa7Ny5EzabDTExMUqbvXv3wmKxKG1SUlLQqlUrBAUFKW1SU1Md3islJQWxsbHO/kg1pufhOSIiIpdya2gqKCjAsWPHcOzYMQDAhQsXcOzYMWRmZqKgoACzZs3CgQMHcPHiRaSmpmLIkCFo3rw54uPjAQBt2rTBgAED8PTTT+PQoUPYt28fpk2bhieeeAKRkZEAgNGjR0On02HSpEk4ffo01q1bh6VLlzocWnvuueewbds2vPHGGzh79iySk5Px/fffY9q0aS7fJ1Wl55QDREREriXcaNeuXQLALcv48ePFzZs3Rf/+/UXDhg2FVqsV0dHR4umnnxZGo9FhG9euXROjRo0Sfn5+wmAwiIkTJ4r8/HyHNsePHxcPPPCA0Ov14p577hGvvvrqLX1Zv369aNmypdDpdKJdu3Ziy5Yt1foseXl5AoDIy8ur/o6ogUmrDono2ZvF5wcvueT9iIiI6qPqfH9LQgjhxsxWb5hMJgQEBCAvL88l45ueWZ2Or08aMf+xdhj/pyZOfz8iIqL6qDrf3x41ponK6dQc00RERORKDE0eSplywMrQRERE5AoMTR5KmXLAwoHgRERErsDQ5KGUs+dYaSIiInIJhiYPVV5pYmgiIiJyBYYmD8UxTURERK7F0OShWGkiIiJyLYYmD6VcRoWVJiIiIpdgaPJQPHuOiIjItRiaPJSOlSYiIiKXYmjyUMrhOc4ITkRE5BIMTR5KmaeJoYmIiMglGJo8lDLlAEMTERGRSzA0eShlIHgpB4ITERG5AkOTh+KYJiIiItdiaPJQOo5pIiIicimGJg+lY6WJiIjIpRiaPBQHghMREbkWQ5OH4uE5IiIi12Jo8lAVrz0nhHBzb4iIiOo/hiYPZa80Aaw2ERERuQJDk4fSVwhNvP4cERGR8zE0eSidukKlycLQRERE5GwMTR5KkiQlOLHSRERE5HwMTR6Ms4ITERG5DkOTB+P154iIiFyHocmDsdJERETkOgxNHowTXBIREbkOQ5MH46VUiIiIXIehyYNxTBMREZHrMDR5MI5pIiIich2GJg/GMU1ERESuw9DkwRiaiIiIXIehyYPx8BwREZHrMDR5MF3Z2XOsNBERETkfQ5MHY6WJiIjIdRiaPBinHCAiInIdhiYPxkoTERGR67g1NO3duxeDBw9GZGQkJEnCV1995bBeCIG5c+ciIiIC3t7eiIuLw7lz5xzaXL9+HWPGjIHBYEBgYCAmTZqEgoIChzYnTpxAr1694OXlhaioKCxatOiWvmzYsAGtW7eGl5cXOnTogK+//rrWP29t49lzREREruPW0FRYWIhOnTph+fLlla5ftGgR3n77baxcuRIHDx6Er68v4uPjUVxcrLQZM2YMTp8+jZSUFGzevBl79+7F5MmTlfUmkwn9+/dHdHQ00tPTsXjxYiQnJ+P9999X2uzfvx+jRo3CpEmTcPToUQwdOhRDhw7FqVOnnPfha4FezUoTERGRy4g6AoDYuHGj8thms4nw8HCxePFi5bnc3Fyh1+vF559/LoQQ4ocffhAAxOHDh5U2W7duFZIkif/9739CCCHeffddERQUJMxms9Jm9uzZolWrVsrjv/zlL2LQoEEO/YmJiRH/93//V+X+5+XlCQAiLy+vyq+5W8t2nhPRszeLFzYcd9l7EhER1SfV+f6us2OaLly4AKPRiLi4OOW5gIAAxMTEIC0tDQCQlpaGwMBAdOvWTWkTFxcHlUqFgwcPKm169+4NnU6ntImPj0dGRgZu3LihtKn4PvY29vepjNlshslkclhcTafmQHAiIiJXqbOhyWg0AgDCwsIcng8LC1PWGY1GhIaGOqzXaDQIDg52aFPZNiq+x+3a2NdXZuHChQgICFCWqKio6n7Eu6bXlh2es/LwHBERkbPV2dBU1yUlJSEvL09ZLl++7PI+KJUmC0MTERGRs9XZ0BQeHg4AyM7Odng+OztbWRceHo6cnByH9aWlpbh+/bpDm8q2UfE9btfGvr4yer0eBoPBYXE1VpqIiIhcp86GpqZNmyI8PBypqanKcyaTCQcPHkRsbCwAIDY2Frm5uUhPT1fa7Ny5EzabDTExMUqbvXv3wmKxKG1SUlLQqlUrBAUFKW0qvo+9jf196iqduuwyKqw0EREROZ1bQ1NBQQGOHTuGY8eOAZAHfx87dgyZmZmQJAnTp0/H3/72N2zatAknT57EuHHjEBkZiaFDhwIA2rRpgwEDBuDpp5/GoUOHsG/fPkybNg1PPPEEIiMjAQCjR4+GTqfDpEmTcPr0aaxbtw5Lly5FYmKi0o/nnnsO27ZtwxtvvIGzZ88iOTkZ33//PaZNm+bqXVIt9sktzaw0EREROZ8Lzua7rV27dgkAtyzjx48XQsjTDrz88ssiLCxM6PV60a9fP5GRkeGwjWvXrolRo0YJPz8/YTAYxMSJE0V+fr5Dm+PHj4sHHnhA6PV6cc8994hXX331lr6sX79etGzZUuh0OtGuXTuxZcuWan0Wd0w5sCcjR0TP3iwGLNnrsvckIiKqT6rz/S0JIYQbM1u9YTKZEBAQgLy8PJeNbzrw8zU88f4BNGvoi53P93HJexIREdUn1fn+rrNjmuj38dpzRERErsPQ5MF47TkiIiLXYWjyYHqNfPYcK01ERETOx9DkwZSz53gZFSIiIqdjaPJgHNNERETkOgxNHsw+pskmgFLO1URERORUDE0ezB6aAA4GJyIicjaGJg9mv2AvwEN0REREzsbQ5ME0ahXUKgkAK01ERETOxtDk4TgYnIiIyDUYmjycjtMOEBERuQRDk4fTc1ZwIiIil2Bo8nC8lAoREZFrMDR5OPsZdBzTRERE5FwMTR5Ouf4cJ7ckIiJyKoYmD6ccnrNwIDgREZEzMTR5OGXKAVaaiIiInIqhycOVV5oYmoiIiJyJocnDcUwTERGRazA0eTg9xzQRERG5BEOTh+OYJiIiItdgaPJwOl57joiIyCUYmjwcZwQnIiJyDYYmD6dnpYmIiMglGJo8HCtNRERErsHQ5OHsUw4wNBERETkXQ5OHK680ccoBIiIiZ2Jo8nAc00REROQaDE0ejlMOEBERuQZDk4fTqTkQnIiIyBUYmjycXlt27TmGJiIiIqdiaPJw5ZUmDgQnIiJyJoYmD6fX8tpzRERErsDQ5OH09kqThaGJiIjImRiaPBwrTURERK7B0OThdGoOBCciInIFhiYPZ680ccoBIiIi52Jo8nD2s+dYaSIiInKuOh2akpOTIUmSw9K6dWtlfXFxMRISEtCgQQP4+flhxIgRyM7OdthGZmYmBg0aBB8fH4SGhmLWrFkoLS11aLN792506dIFer0ezZs3x6pVq1zx8WoFrz1HRETkGnU6NAFAu3btkJWVpSzfffedsm7GjBn473//iw0bNmDPnj24cuUKhg8frqy3Wq0YNGgQSkpKsH//fnzyySdYtWoV5s6dq7S5cOECBg0ahL59++LYsWOYPn06nnrqKWzfvt2ln7Om7Nees1gFbDbh5t4QERHVXxp3d+D3aDQahIeH3/J8Xl4ePvzwQ6xZswYPPfQQAODjjz9GmzZtcODAAdx///3YsWMHfvjhB3zzzTcICwtD586d8corr2D27NlITk6GTqfDypUr0bRpU7zxxhsAgDZt2uC7777DW2+9hfj4eJd+1pqwV5oA+Qw6L5Xajb0hIiKqv+p8pencuXOIjIxEs2bNMGbMGGRmZgIA0tPTYbFYEBcXp7Rt3bo1GjdujLS0NABAWloaOnTogLCwMKVNfHw8TCYTTp8+rbSpuA17G/s2bsdsNsNkMjks7qDXlIckDgYnIiJynjodmmJiYrBq1Sps27YNK1aswIULF9CrVy/k5+fDaDRCp9MhMDDQ4TVhYWEwGo0AAKPR6BCY7Ovt6+7UxmQyoaio6LZ9W7hwIQICApQlKirqbj9ujWjVknKf45qIiIicp04fnhs4cKByv2PHjoiJiUF0dDTWr18Pb29vN/YMSEpKQmJiovLYZDK5JThJkgS9RgVzqY1n0BERETlRna40/VZgYCBatmyJn376CeHh4SgpKUFubq5Dm+zsbGUMVHh4+C1n09kf/14bg8Fwx2Cm1+thMBgcFnexj2tiaCIiInIejwpNBQUFOH/+PCIiItC1a1dotVqkpqYq6zMyMpCZmYnY2FgAQGxsLE6ePImcnBylTUpKCgwGA9q2bau0qbgNexv7NjyBXsMJLomIiJytToemmTNnYs+ePbh48SL279+PYcOGQa1WY9SoUQgICMCkSZOQmJiIXbt2IT09HRMnTkRsbCzuv/9+AED//v3Rtm1bjB07FsePH8f27dsxZ84cJCQkQK/XAwCmTJmCn3/+GS+88ALOnj2Ld999F+vXr8eMGTPc+dGrxT4YnJUmIiIi56nTY5p++eUXjBo1CteuXUPDhg3xwAMP4MCBA2jYsCEA4K233oJKpcKIESNgNpsRHx+Pd999V3m9Wq3G5s2bMXXqVMTGxsLX1xfjx4/HggULlDZNmzbFli1bMGPGDCxduhSNGjXCBx984BHTDdjpWGkiIiJyOkkIwRkRa4HJZEJAQADy8vJcPr5pwJK9OGvMx78mxeCBFiEufW8iIiJPVp3v7zp9eI6qhpdSISIicj6GpnpAz7PniIiInI6hqR5QphywMjQRERE5C0NTPaBTlx2eszA0EREROQtDUz1gn3LAzEoTERGR0zA01QPKQHALB4ITERE5C0NTPaDnmCYiIiKnY2iqB8orTQxNREREzsLQVA8ol1FhpYmIiMhpGJrqAR3naSIiInI6hqZ6QM8ZwYmIiJyOoakeYKWJiIjI+Ria6oHyShNDExERkbMwNNUDvPYcERGR8zE01QM6VpqIiIicjqGpHlCmHGBoIiIichqGpnpAx7PniIiInI6hqR7gmCYiIiLnY2iqBzimiYiIyPkYmuoBnZqVJiIiImfTuLsDdPf0WnkgeK1UmowngbNfA8HNgIatgJAWgNb77rdLRETk4Ria6gF7panYYkWp1QaNuoYFxMwDwL8eB0ryy5+TVEBQE6Bh6/IltDXQoAWg87n7zhMREXkIhqZ6wEsrh6RrhSVoO3c77g31Q6swP7QI80erMH+0CvfHPYHeUKmk22/k4j5g9Z8BSyEQ3gHQ+QNXzwBFN4DrP8tLxtfl7SU10OJhoOtEoHkcoOavEhER1W/8pqsHmjTwxcD24djz41XcLLHiTJYJZ7JMDm18dGq0CPVD+3sC8GDLhujZPAS++rIf/4VvgTV/ASw3gWZ9gSfWyFUkIYDCq0DOGeBqBnD1rLzknAGKrgM/bpMXwz3AfWOBLmOBgEZu2ANERETOJwkhhLs7UR+YTCYEBAQgLy8PBoPBLX2w2QT+l1uEDGM+MrLz8WN2Pn7MLsD5nAKUWB3HO+nUKvRoGozRDX/GgJPToSotlitGI/9VtTFMV38EjnwCHFsjByhAPpTX/GGg20T5ltUnIiKq46rz/c3QVEvqQmi6nVKrDRev3USGMR+HLlzDzowcXL5ehF6qE/in9g14SRakqboipcNi9GoThe5NgyFBPhuvxGpzvC21wVIWwNrfEwAvqRQ4818gfRVw8dvyN/WPlCtP3Z8C/ELd8rmJiIh+D0OTG9Tl0PRbQghkpW9G6NdPQmMrQaqtK6aW/BUl0FZrOwYvDQZ1jMTwLvegW3QQpGvny6pPq4Gb1+RGXoHAI68DHR4HpDuMqSIiInIDhiY38KTQhB+3A+v+H2AtAVo/isLH/on9F/OxKyMHu8/m4EpesdJUkuRDeTqNCnqNCjq1ClqNCoXmUvxaUKK0iwr2xrD7GmHYffegaaAGOLsF+O4twHhCbtD6UeDRJYBfQxd/WCIiottjaHIDp4WmwmvA2tFAl3FA++F3P2fS2a+B9eMAmwVo8xjw+EeAurzCJISAqbgUWrUEnVp12+kLrDaBAz9fw5dH/odtp7JQWFJ+3bv7Ggdi+H334NF2DRF0dDmw5zXAVgr4NAAGvQG0G3Z3n4GIiKiWMDS5gdNC03dvAd8ky/e9g+Sz1LpPkudOqo6iXODUv4GtL8gBpt1wYPj7DoGppm6WlCLlh2x8eeR/+PbcVdjKfqO0agmNgnzQSZuJGQVvIdryMwAgo0EcDrd7Cd4BoQj00SIy0BvNGvpCr1HfdV+IiIiqg6HJDZxXafoVOPIp8P3HQF5m2ZMS0KI/0ONp4N5+gKqSapAQ8vQAP24Hzu2QJ64UZdWgDn8Ghq50ytltOaZibDp+BV8e+R9+qDDtgRalmKbZiAT1f6CRbLgqDJhjmYTttu4AALVKQrMQX7QM90frMH+0DJfnmGoc7HPn+aWIiIjuAkOTGzh9TJPNKgegw/8Ezu8sfz6oqVx56jxGPnR34Vvg3Hbgxx0VQlaZkFZyYOqVCKicX9XJvHYTRlMxcm+WIK/IgrwiC/Q5J9D/XDLCii8AAHbp+mBOyTj8r9ir0m14a9VoEeaHlmH+aBHqh+ZlS6MgH6gZpoiI6C4xNLmBSweC//oT8P2HwNHVgDlPfk7jBUACSovK26n1QNPeQMt4efbu6h7Sc5ZSM7B7IbBvKSBsEHoDCtuPxol7RuFkvj8ysvORYczHuZyC216EWK9RoVlDP4cgdW9DP0QFe8NHx/mhiIioahia3MAtZ8+VFAInNwCHPgCyT8rPGRoBLfsDLeLlwFSXrw/3y/fAf6bJl2sB5EuztH0MiJ0GNOqGUqsNl67L80udyy7AT1cLcC47Hz//WlhpmNKiFKVQoYGfF+4J8kFUkDeign3QKMgbUUE+iAr2QWSgF8dOERGRgqHJDdw65YAQQPYpOXSEtvGs+ZBsNuCnFCBtOXBhT/nzjXoAsQnyVAW/GXtltQn8YryKqz8ehOXyYfjkHENk4Q9oaLsKALAINSzQwAI1SqBBCbSwCA0s0KAYWpyXmuCcrjUyfdohz785Any9EeitRZCPFgE+OgR6y4PTGzfwQYTBq/bGVFlL5UqgpdjxtrQEgACETf5ZQjjeCpv8M9X6Anp/QO8n32p9Kx/PRkREVcbQ5AYeNU9TXWU8CRxYAZxYL0+JAAABjYH7pwDRPYGs48D/vgf+dwTI+UEOE3epUOhx3HYvjormOGprgaO25riGAGW9Tq1Co2BvRAf7ILqBLxoH+yC6gbzcE+gDb11Z1aq0BLh+Xr5G368/lt1mAPnG8nBkK73r/t5CVyFE6fwA3xB5Bna/cMAvrOx+GOAfJt/qfGu/D3WBzQbkZwGWIiAwCtDo3d0jIqoKIeT5/LKOA2HtgPBOLr8EF0OTGzA01aJ8I3D4A+Dwh+XXtauM4R7gni7APd2Ae7oCoW3l560llSwWiFIzCvKuw/JLOjRX0uGTcxSa0sJbNpujDodRBCGvVItCoUcRdCgSOhTBq+y+HsXQoYFkQmvNFbRQZSHSlgU1qh7ihFoHSeMNaL3ksWeSJF+7T5IASBVuy54TQr6gstkEmAvKz4SsLp2fvN+CmwHBTeXboKby/cDGt5+CQgj5vW9ek+cOu3lN7o9fKOAfARgiqzeHmM0qXww6PwsoyJE/p85PDnV6PzkM6nzlbdorp0LIvxvXzwPXzle4/Rm4fqHCeD5JvnB0cNPyz1bxc+r9b/1stlLAapFvlcVaVv27w2Kzym2FVQ5uyv0Kr/cKkN/bN8SzqsB1iaUIyPtF/v30CZF/N7gvnctmrfBvUi0TAsg+DZz+Eji9Uf5/2E7rC0R1l/9QbhwLNOp29/MT/g6GJjdgaHICSxFwfC1w6H35H8yITvL/QPd0lYOSIeLutm+zyhWhXw6XL1fP1nhz+cIb50UkfhL34LwtEj+JSPxPhKAIehQLHYqhRTF0MEMHG1RQSYBGLc+03sBXhxA/PRr42W/1CLHf99WhgZ8OOrUaarUEjQSorGZoSwugthZCYymA2lIIdUk+1EXXgIJsID9bvi3IKbvNlkPOnUhquUoT3Ez+or95Dbh5XZ724ua18urf7XgFyuHJP0L+2fhHAr4NgaIbcjjKN5bfFuZUrVIoqcoDVHEeYLk15Dr0X6P//c+pDygLPBVCkivo/IHgJo4Bzn7fP9wxiImywGWzBzSr/EWj1gEanXyr0t794Vl7YLSHP1tp2fuWlv/BUVoCWM1ltxXvm4GSm0BxrvwzLrohzwen3L8hr7MUyeHacE+F34+y+/bFJ0T+nbhxEbhxSb7NvVT+uMDo2G+Nl/y75dNADqM+IfKtb4gcilVaeR+ptfKi0jre13oDPsHy63V+f5wAZrPK/x8V3Sj7o8VY9u9FxdsceX8X/groDUCDZmV/ZN0LNLi3/NYnuPrvn3NGDkmnN8oVeTuNFxDZBcg5LfevIpVW/uO4cawcpKJ6AN6Bd7Ubfouh6S4sX74cixcvhtFoRKdOnfDOO++gR48ev/s6hqZ6oihXLhPb/7EvKZRvLUXyF7alCLDchCi5iRKtP/J8myJbF42LUiNkWgJgNJlhNBUjx1QMo6kYeUUWlFoFSm2u+d/MS6tCgLfWYTF4axHgpUGIzoIIVR5CbTkILvkFAcW/wK/wMrzzM6E1XYJU8czL27BpfWD1CoZFHwSrWg9d0a/Q3jRCVVr8u6+9haQuP4QIIVfQSgrkfV5ScPvXBDYu+8e74j/kzeTnVRr5H/0bF+Tqk/32+s/yffs1EavaP5W67K/tioskr5Mk+f0qtlNpyu6ry9ZJ8hdU3i/yZ6xtKk1ZONCVhwT7ODglbNnviwrVM2t5QPIUOj+5zzX5XbsTtQ7wLgtQPsHlYcorUN6f9p9vxZ9rxce/HYMIVHhsq/DzsJYHYttvH1srVJsrvpfqN8+pflMBtTr+vG1W+Y+BotyyMPub22ITau330CtQ/n/PPwLQ+shBVOsjn3xkv6/1litHNy7KQcl+0g8gV9hbPCxfIaLlALnCbLPJbS7tl5fMNPkPrYqiHwAmbqmdz1CGoamG1q1bh3HjxmHlypWIiYnBkiVLsGHDBmRkZCA0NPSOr2VoojsRQg5OpVYBi80mBymrDRabQFGJFdcLS/BrgRnXCsz4tcB+vwTXCuXH1wtLUGq1odQmYLU5I4QJhCIX96qz0VJ7Ff6qYly1+iG71A9XbX64Lgy4Dn+Yoav0tQYUIly6gXDpOsKkGwjHdYRLNxCiykeByh831A2QpwlBviYEBfqGKNI3hMUrGF46Hby0KkiQYBUCNpuQb61WaGxF0JTehM4qL0WSN65qwlAKrfLPvv2fL/u/YpIEaNUqaNUSNGr5WokalQRt2XUTfcRNBFqvQVLJX35CpYVQaSCptIBaA6HSAGotJJUaKkmCJElQS4BKJd9XSYCq7FZ+XP6cpKwrX69WSZAgVwa9Ci/Dp+AyfAouwbsgE94Fl+BTeBleBb9AJe5c7RKQKyGSM4LX7dirNRqd/AWn3OrLqjZ6+UvRO+g2S6B8q9HLlc/8K4DJvvwPMGXJ9/Oz5C9/lQYIiJKnRgmKBgKjy+8HNZW3Bcih+uavciWk8NcK96/KobikQD7pwloiBwirfSkpPwxbUigf+q/tAOYptD5ypc6/bOyjw2142VjIUDnw//ZQ+LXz8s+yJlRaoHmcHJRaDQS8fue7UgiIGxdR/NO3sFzYB93/DqCg+RCEPLagZu9/GwxNNRQTE4Pu3btj2bJlAACbzYaoqCg8++yzePHFF+/4WoYmciUhBGxCPpNQDlFyECswlyoTif52yb1pganIgnxzKQqKLSgwl6KguFR+bC7F7/1LIEmAl0YNb50aXhr5ws2WUhvMZUtJqQ0lVg+qXNQRaljhiyIIqGCFCjZIsP3mFmWhSQUbtCiFDqXQli06yVL22AotSmGDBFHhtVaoyh5LEJArF2qNBlqtDlqtBnqtDlqdHl5aLXR6LXQ6Hby1WqhUKlis8s/UYrXBYhXy49Lyx6U2AY1KDofKIknQqMvvq1RShd8tUV6MsT9lK4WfTQ7XUGnkTysBgD2IAlLZfbVKgrYsCMuhWL7VqCVoVSpo1SplCKAoey8hBATKCm1l9yXI29ILM/ysefC15sHXaoJ3aS58SnPhbcmD3poPlSiFStggCRtUsJbf2sp+UsrYQnkcorD/rCQJwj4usWxsopBUEJJcQRKSCqKsmiQg31cBUEk2qCEvqgq39kUSQv45SmpYhQQhqWCFBJtQwSapYBMSbJIaVp0BVn0ArLoAWL0CYdMHwKYPhM0rAEJvgKTxcvwZoMIfILf5PbUfwJQkCSrLTejzL0GfdwGq4uuQLDchWYqA0iKoLDfl27L7qtIilKq9cSWiH66E90Wx2qD8cWS1CdjK/lgqsdpwrbAEv+bLfyxeq/DHY8V/Vx6/LwKvj+xSzf/L7qw639+cBbBMSUkJ0tPTkZSUpDynUqkQFxeHtLS0W9qbzWaYzWblsclkuqUNkbPYKyDls6LLZ/EF+eoQVYPt2WwCRRYrCsylyC8uhcVqg7dWDS+tGt5aNfRaeeyV9DtjP+z/+NlDVLHFCnOpFUUlNhRZrPJSUlp2Kz9XbLFCCAFV2ZdsxS9glVT+5Wt/a0mSKnyxlh3VKPtStQmhfLlbSuXKXInVBkupHCzt9+1fqDblH257EJXv22wV7ovytvbnRNnrBOD4Wlt5e/uXQkUOjyqkVKUKWRZSKgYT+2MAsEEFc9m4uMo3WgUCQEnZorjlCTe4w0kfTicBCChbot3YD2cpAZBTttQm+z6rggvKf2rET69BiJ8Ogb6VXz3CVRiayvz666+wWq0ICwtzeD4sLAxnz946OHjhwoWYP3++q7pH5FQqlQRfvQa+eg3C7qJQqlJJ8FLJYYtqT8WQVl6pEQ7VwYrVFXtFRdjK79tDnxCA2WLDTUspbpZYUVRilW/LAu3NsseAfNFt+XCnfHhTp1Ephz+1GhXUknxY1VpWdbKVHYa2lR1CttpssNrswVZmD7nyg/IbIX+o8gpRhc9kK3vw20PcSsXLWh6QhSh/P/thUzlkS8p7CwiHakeptfywd8XPA1QM1PY+ifJ+VVJYFb9JsQ6VtUp+dvZtWgXkz2H/XJV8RqsQ0KrkyppGqbjJVTZ1WfVNrbJX1SqG//I/AOzh3q7i30HKHyIof9L+eRyGa/2GWiVBo5KripqyP3Y06rLbsuftfxDJ9+HwR5FKJUGnViG47KSXEF89Qvx1aOCrR4i/fDJMXfk3haGphpKSkpCYmKg8NplMiIqqyd/4RER3JpV9CRGRezE0lQkJCYFarUZ2drbD89nZ2QgPD7+lvV6vh17PCfSIiIj+KHgNhjI6nQ5du3ZFamqq8pzNZkNqaipiY2Pd2DMiIiKqC1hpqiAxMRHjx49Ht27d0KNHDyxZsgSFhYWYOHGiu7tGREREbsbQVMHIkSNx9epVzJ07F0ajEZ07d8a2bdtuGRxOREREfzycp6mWcJ4mIiIiz1Od72+OaSIiIiKqAoYmIiIioipgaCIiIiKqAoYmIiIioipgaCIiIiKqAoYmIiIioipgaCIiIiKqAoYmIiIioipgaCIiIiKqAl5GpZbYJ1Y3mUxu7gkRERFVlf17uyoXSGFoqiX5+fkAgKioKDf3hIiIiKorPz8fAQEBd2zDa8/VEpvNhitXrsDf3x+SJNXqtk0mE6KionD58mVe184FuL9di/vbtbi/XYv727Vqsr+FEMjPz0dkZCRUqjuPWmKlqZaoVCo0atTIqe9hMBj4P50LcX+7Fve3a3F/uxb3t2tVd3//XoXJjgPBiYiIiKqAoYmIiIioChiaPIBer8e8efOg1+vd3ZU/BO5v1+L+di3ub9fi/nYtZ+9vDgQnIiIiqgJWmoiIiIiqgKGJiIiIqAoYmoiIiIiqgKGJiIiIqAoYmuq45cuXo0mTJvDy8kJMTAwOHTrk7i7VC3v37sXgwYMRGRkJSZLw1VdfOawXQmDu3LmIiIiAt7c34uLicO7cOfd0th5YuHAhunfvDn9/f4SGhmLo0KHIyMhwaFNcXIyEhAQ0aNAAfn5+GDFiBLKzs93UY8+2YsUKdOzYUZngLzY2Flu3blXWc18716uvvgpJkjB9+nTlOe7z2pOcnAxJkhyW1q1bK+udua8ZmuqwdevWITExEfPmzcORI0fQqVMnxMfHIycnx91d83iFhYXo1KkTli9fXun6RYsW4e2338bKlStx8OBB+Pr6Ij4+HsXFxS7uaf2wZ88eJCQk4MCBA0hJSYHFYkH//v1RWFiotJkxYwb++9//YsOGDdizZw+uXLmC4cOHu7HXnqtRo0Z49dVXkZ6eju+//x4PPfQQhgwZgtOnTwPgvnamw4cP47333kPHjh0dnuc+r13t2rVDVlaWsnz33XfKOqfua0F1Vo8ePURCQoLy2Gq1isjISLFw4UI39qr+ASA2btyoPLbZbCI8PFwsXrxYeS43N1fo9Xrx+eefu6GH9U9OTo4AIPbs2SOEkPevVqsVGzZsUNqcOXNGABBpaWnu6ma9EhQUJD744APuayfKz88XLVq0ECkpKeLBBx8Uzz33nBCCv9+1bd68eaJTp06VrnP2vmalqY4qKSlBeno64uLilOdUKhXi4uKQlpbmxp7VfxcuXIDRaHTY9wEBAYiJieG+ryV5eXkAgODgYABAeno6LBaLwz5v3bo1GjduzH1+l6xWK9auXYvCwkLExsZyXztRQkICBg0a5LBvAf5+O8O5c+cQGRmJZs2aYcyYMcjMzATg/H3NC/bWUb/++iusVivCwsIcng8LC8PZs2fd1Ks/BqPRCACV7nv7Oqo5m82G6dOno2fPnmjfvj0AeZ/rdDoEBgY6tOU+r7mTJ08iNjYWxcXF8PPzw8aNG9G2bVscO3aM+9oJ1q5diyNHjuDw4cO3rOPvd+2KiYnBqlWr0KpVK2RlZWH+/Pno1asXTp065fR9zdBERC6VkJCAU6dOOYxBoNrXqlUrHDt2DHl5efjiiy8wfvx47Nmzx93dqpcuX76M5557DikpKfDy8nJ3d+q9gQMHKvc7duyImJgYREdHY/369fD29nbqe/PwXB0VEhICtVp9y4j/7OxshIeHu6lXfwz2/ct9X/umTZuGzZs3Y9euXWjUqJHyfHh4OEpKSpCbm+vQnvu85nQ6HZo3b46uXbti4cKF6NSpE5YuXcp97QTp6enIyclBly5doNFooNFosGfPHrz99tvQaDQICwvjPneiwMBAtGzZEj/99JPTf78ZmuoonU6Hrl27IjU1VXnOZrMhNTUVsbGxbuxZ/de0aVOEh4c77HuTyYSDBw9y39eQEALTpk3Dxo0bsXPnTjRt2tRhfdeuXaHVah32eUZGBjIzM7nPa4nNZoPZbOa+doJ+/frh5MmTOHbsmLJ069YNY8aMUe5znztPQUEBzp8/j4iICOf/ft/1UHJymrVr1wq9Xi9WrVolfvjhBzF58mQRGBgojEaju7vm8fLz88XRo0fF0aNHBQDx5ptviqNHj4pLly4JIYR49dVXRWBgoPjPf/4jTpw4IYYMGSKaNm0qioqK3NxzzzR16lQREBAgdu/eLbKyspTl5s2bSpspU6aIxo0bi507d4rvv/9exMbGitjYWDf22nO9+OKLYs+ePeLChQvixIkT4sUXXxSSJIkdO3YIIbivXaHi2XNCcJ/Xpueff17s3r1bXLhwQezbt0/ExcWJkJAQkZOTI4Rw7r5maKrj3nnnHdG4cWOh0+lEjx49xIEDB9zdpXph165dAsAty/jx44UQ8rQDL7/8sggLCxN6vV7069dPZGRkuLfTHqyyfQ1AfPzxx0qboqIi8cwzz4igoCDh4+Mjhg0bJrKystzXaQ/25JNPiujoaKHT6UTDhg1Fv379lMAkBPe1K/w2NHGf156RI0eKiIgIodPpxD333CNGjhwpfvrpJ2W9M/e1JIQQd1+vIiIiIqrfOKaJiIiIqAoYmoiIiIiqgKGJiIiIqAoYmoiIiIiqgKGJiIiIqAoYmoiIiIiqgKGJiIiIqAoYmoiIapEkSfjqq6/c3Q0icgKGJiKqNyZMmABJkm5ZBgwY4O6uEVE9oHF3B4iIatOAAQPw8ccfOzyn1+vd1Bsiqk9YaSKiekWv1yM8PNxhCQoKAiAfOluxYgUGDhwIb29vNGvWDF988YXD60+ePImHHnoI3t7eaNCgASZPnoyCggKHNh999BHatWsHvV6PiIgITJs2zWH9r7/+imHDhsHHxwctWrTApk2blHU3btzAmDFj0LBhQ3h7e6NFixa3hDwiqpsYmojoD+Xll1/GiBEjcPz4cYwZMwZPPPEEzpw5AwAoLCxEfHw8goKCcPjwYWzYsAHffPONQyhasWIFEhISMHnyZJw8eRKbNm1C8+bNHd5j/vz5+Mtf/oITJ07gkUcewZgxY3D9+nXl/X/44Qds3boVZ86cwYoVKxASEuK6HUBENVcrl/0lIqoDxo8fL9RqtfD19XVY/v73vwshhAAgpkyZ4vCamJgYMXXqVCGEEO+//74ICgoSBQUFyvotW7YIlUoljEajEEKIyMhI8dJLL922DwDEnDlzlMcFBQUCgNi6dasQQojBgweLiRMn1s4HJiKX4pgmIqpX+vbtixUrVjg8FxwcrNyPjY11WBcbG4tjx44BAM6cOYNOnTrB19dXWd+zZ0/YbDZkZGRAkiRcuXIF/fr1u2MfOnbsqNz39fWFwWBATk4OAGDq1KkYMWIEjhw5gv79+2Po0KH405/+VKPPSkSuxdBERPWKr6/vLYfLaou3t3eV2mm1WofHkiTBZrMBAAYOHIhLly7h66+/RkpKCvr164eEhAS8/vrrtd5fIqpdHNNERH8oBw4cuOVxmzZtAABt2rTB8ePHUVhYqKzft28fVCoVWrVqBX9/fzRp0gSpqal31YeGDRti/Pjx+Ne//oUlS5bg/fffv6vtEZFrsNJERPWK2WyG0Wh0eE6j0SiDrTds2IBu3brhgQcewOrVq3Ho0CF8+OGHAIAxY8Zg3rx5GD9+PJKTk3H16lU8++yzGDt2LMLCwgAAycnJmDJlCkJDQzFw4EDk5+dj3759ePbZZ6vUv7lz56Jr165o164dzGYzNm/erIQ2IqrbGJqIqF7Ztm0bIiIiHJ5r1aoVzp49C0A+s23t2rV45plnEBERgc8//xxt27YFAPj4+GD79u147rnn0L17d/j4+GDEiBF48803lW2NHz8excXFeOuttzBz5kyEhITg8ccfr3L/dDodkpKScPHiRXh7e6NXr15Yu3ZtLXxyInI2SQgh3N0JIiJXkCQJGzduxNChQ93dFSLyQBzTRERERFQFDE1EREREVcAxTUT0h8HRCER0N1hpIiIiIqoChiYiIiKiKmBoIiIiIqoChiYiIiKiKmBoIiIiIqoChiYiIiKiKmBoIiIiIqoChiYiIiKiKmBoIiIiIqqC/x9GlOROoJlc6QAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Train and evaluate LSTM model\n",
+        "lstm_model = create_lstm_model()\n",
+        "lstm_history = train_model(lstm_model, dataset, dataset_valid, epochs=50, steps_per_epoch=steps_per_epoch_train, validation_steps=validation_steps)\n",
+        "plot_and_save_metrics(lstm_history, 'LSTM')\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "XfoXwdnwPKqg",
+        "outputId": "962b97d4-7ce9-42be-f113-9b8afe9716ff"
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "5/5 [==============================] - 6s 548ms/step - loss: 4332.6846 - mae: 50.8302 - val_loss: 2086.8181 - val_mae: 36.4607\n",
+            "Epoch 2/50\n",
+            "5/5 [==============================] - 3s 636ms/step - loss: 3878.1887 - mae: 45.8173 - val_loss: 1932.4192 - val_mae: 34.7994\n",
+            "Epoch 3/50\n",
+            "5/5 [==============================] - 2s 352ms/step - loss: 3932.9382 - mae: 46.7198 - val_loss: 1794.5331 - val_mae: 33.3172\n",
+            "Epoch 4/50\n",
+            "5/5 [==============================] - 2s 349ms/step - loss: 3450.6941 - mae: 42.2571 - val_loss: 1672.6119 - val_mae: 32.2968\n",
+            "Epoch 5/50\n",
+            "5/5 [==============================] - 2s 356ms/step - loss: 3426.4988 - mae: 41.8699 - val_loss: 1548.6584 - val_mae: 31.2123\n",
+            "Epoch 6/50\n",
+            "5/5 [==============================] - 2s 347ms/step - loss: 3149.0376 - mae: 40.1630 - val_loss: 1422.7568 - val_mae: 30.0150\n",
+            "Epoch 7/50\n",
+            "5/5 [==============================] - 2s 344ms/step - loss: 3040.2234 - mae: 40.6473 - val_loss: 1309.0945 - val_mae: 28.8262\n",
+            "Epoch 8/50\n",
+            "5/5 [==============================] - 2s 453ms/step - loss: 2725.6208 - mae: 37.5042 - val_loss: 1204.5574 - val_mae: 27.6107\n",
+            "Epoch 9/50\n",
+            "5/5 [==============================] - 3s 624ms/step - loss: 2767.2739 - mae: 38.7110 - val_loss: 1105.9377 - val_mae: 26.3120\n",
+            "Epoch 10/50\n",
+            "5/5 [==============================] - 2s 348ms/step - loss: 2500.0779 - mae: 37.2180 - val_loss: 1011.4269 - val_mae: 25.4814\n",
+            "Epoch 11/50\n",
+            "5/5 [==============================] - 2s 358ms/step - loss: 2526.9580 - mae: 37.4389 - val_loss: 929.5729 - val_mae: 25.0929\n",
+            "Epoch 12/50\n",
+            "5/5 [==============================] - 2s 350ms/step - loss: 2301.4519 - mae: 37.1122 - val_loss: 867.6036 - val_mae: 24.7116\n",
+            "Epoch 13/50\n",
+            "5/5 [==============================] - 2s 342ms/step - loss: 2134.1848 - mae: 35.6614 - val_loss: 824.0354 - val_mae: 24.3278\n",
+            "Epoch 14/50\n",
+            "5/5 [==============================] - 2s 339ms/step - loss: 2131.1726 - mae: 36.0531 - val_loss: 798.4954 - val_mae: 24.1874\n",
+            "Epoch 15/50\n",
+            "5/5 [==============================] - 2s 385ms/step - loss: 1909.7211 - mae: 34.4604 - val_loss: 793.4854 - val_mae: 24.4420\n",
+            "Epoch 16/50\n",
+            "5/5 [==============================] - 3s 580ms/step - loss: 1937.4248 - mae: 35.5715 - val_loss: 804.2097 - val_mae: 24.7834\n",
+            "Epoch 17/50\n",
+            "5/5 [==============================] - 2s 374ms/step - loss: 1805.8660 - mae: 34.7018 - val_loss: 826.6603 - val_mae: 25.0911\n",
+            "Epoch 18/50\n",
+            "5/5 [==============================] - 2s 340ms/step - loss: 1721.8943 - mae: 34.3680 - val_loss: 855.9180 - val_mae: 25.3577\n",
+            "Epoch 19/50\n",
+            "5/5 [==============================] - 2s 353ms/step - loss: 1717.6575 - mae: 35.0930 - val_loss: 885.2465 - val_mae: 25.5670\n",
+            "Epoch 20/50\n",
+            "5/5 [==============================] - 2s 334ms/step - loss: 1805.7438 - mae: 36.3200 - val_loss: 909.2367 - val_mae: 25.7141\n",
+            "Epoch 21/50\n",
+            "5/5 [==============================] - 2s 336ms/step - loss: 1698.4707 - mae: 35.6541 - val_loss: 929.1206 - val_mae: 25.8249\n",
+            "Epoch 22/50\n",
+            "5/5 [==============================] - 2s 360ms/step - loss: 1893.9508 - mae: 37.8788 - val_loss: 938.4974 - val_mae: 25.8743\n",
+            "Epoch 23/50\n",
+            "5/5 [==============================] - 3s 634ms/step - loss: 1755.9480 - mae: 36.1972 - val_loss: 944.9700 - val_mae: 25.9075\n",
+            "Epoch 24/50\n",
+            "5/5 [==============================] - 2s 352ms/step - loss: 1830.0111 - mae: 37.3053 - val_loss: 942.9779 - val_mae: 25.8974\n",
+            "Epoch 25/50\n",
+            "5/5 [==============================] - 2s 344ms/step - loss: 1845.3162 - mae: 36.8536 - val_loss: 940.9585 - val_mae: 25.8870\n",
+            "Epoch 26/50\n",
+            "5/5 [==============================] - 2s 353ms/step - loss: 1797.7043 - mae: 36.3067 - val_loss: 935.7726 - val_mae: 25.8601\n",
+            "Epoch 27/50\n",
+            "5/5 [==============================] - 2s 347ms/step - loss: 1855.5234 - mae: 37.2260 - val_loss: 931.1747 - val_mae: 25.8359\n",
+            "Epoch 28/50\n",
+            "5/5 [==============================] - 2s 348ms/step - loss: 1762.2191 - mae: 35.9646 - val_loss: 927.5026 - val_mae: 25.8162\n",
+            "Epoch 29/50\n",
+            "5/5 [==============================] - 2s 400ms/step - loss: 1686.0814 - mae: 35.0306 - val_loss: 924.6272 - val_mae: 25.8006\n",
+            "Epoch 30/50\n",
+            "5/5 [==============================] - 3s 611ms/step - loss: 1714.9467 - mae: 35.6333 - val_loss: 923.2318 - val_mae: 25.7930\n",
+            "Epoch 31/50\n",
+            "5/5 [==============================] - 2s 349ms/step - loss: 1801.4922 - mae: 36.5153 - val_loss: 920.9125 - val_mae: 25.7802\n",
+            "Epoch 32/50\n",
+            "5/5 [==============================] - 2s 334ms/step - loss: 1696.5212 - mae: 35.6769 - val_loss: 922.2623 - val_mae: 25.7877\n",
+            "Epoch 33/50\n",
+            "5/5 [==============================] - 2s 351ms/step - loss: 1894.1315 - mae: 37.7732 - val_loss: 920.6016 - val_mae: 25.7785\n",
+            "Epoch 34/50\n",
+            "5/5 [==============================] - 2s 346ms/step - loss: 1758.1683 - mae: 36.0733 - val_loss: 922.7652 - val_mae: 25.7905\n",
+            "Epoch 35/50\n",
+            "5/5 [==============================] - 2s 354ms/step - loss: 1827.2109 - mae: 37.0929 - val_loss: 921.1790 - val_mae: 25.7817\n",
+            "Epoch 36/50\n",
+            "5/5 [==============================] - 2s 420ms/step - loss: 1846.9633 - mae: 36.6932 - val_loss: 922.4278 - val_mae: 25.7886\n",
+            "Epoch 37/50\n",
+            "5/5 [==============================] - 3s 628ms/step - loss: 1794.7653 - mae: 36.1218 - val_loss: 921.6312 - val_mae: 25.7842\n",
+            "Epoch 38/50\n",
+            "5/5 [==============================] - 2s 353ms/step - loss: 1854.6641 - mae: 37.0945 - val_loss: 921.5575 - val_mae: 25.7838\n",
+            "Epoch 39/50\n",
+            "5/5 [==============================] - 2s 343ms/step - loss: 1761.9645 - mae: 35.8977 - val_loss: 921.8483 - val_mae: 25.7854\n",
+            "Epoch 40/50\n",
+            "5/5 [==============================] - 2s 359ms/step - loss: 1686.6094 - mae: 35.0027 - val_loss: 922.0842 - val_mae: 25.7867\n",
+            "Epoch 41/50\n",
+            "5/5 [==============================] - 2s 358ms/step - loss: 1714.9602 - mae: 35.6237 - val_loss: 922.9360 - val_mae: 25.7914\n",
+            "Epoch 42/50\n",
+            "5/5 [==============================] - 2s 355ms/step - loss: 1801.5586 - mae: 36.5185 - val_loss: 921.8321 - val_mae: 25.7853\n",
+            "Epoch 43/50\n",
+            "5/5 [==============================] - 2s 412ms/step - loss: 1696.5961 - mae: 35.6878 - val_loss: 924.0575 - val_mae: 25.7975\n",
+            "Epoch 44/50\n",
+            "5/5 [==============================] - 3s 646ms/step - loss: 1894.1000 - mae: 37.7909 - val_loss: 922.3746 - val_mae: 25.7883\n",
+            "Epoch 45/50\n",
+            "5/5 [==============================] - 2s 352ms/step - loss: 1757.9421 - mae: 36.0850 - val_loss: 924.5907 - val_mae: 25.8004\n",
+            "Epoch 46/50\n",
+            "5/5 [==============================] - 2s 353ms/step - loss: 1827.4265 - mae: 37.1097 - val_loss: 922.4617 - val_mae: 25.7888\n",
+            "Epoch 47/50\n",
+            "5/5 [==============================] - 2s 351ms/step - loss: 1846.8623 - mae: 36.7026 - val_loss: 923.4094 - val_mae: 25.7940\n",
+            "Epoch 48/50\n",
+            "5/5 [==============================] - 2s 344ms/step - loss: 1794.9392 - mae: 36.1320 - val_loss: 922.1340 - val_mae: 25.7870\n",
+            "Epoch 49/50\n",
+            "5/5 [==============================] - 2s 340ms/step - loss: 1854.6879 - mae: 37.0985 - val_loss: 921.7303 - val_mae: 25.7848\n",
+            "Epoch 50/50\n",
+            "5/5 [==============================] - 2s 404ms/step - loss: 1762.0026 - mae: 35.9001 - val_loss: 921.8163 - val_mae: 25.7852\n"
+          ]
+        },
+        {
+          "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": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Train and evaluate RNN model\n",
+        "rnn_model = create_rnn_model()\n",
+        "rnn_history = train_model(rnn_model, dataset, dataset_valid, epochs=50, steps_per_epoch=steps_per_epoch_train, validation_steps=validation_steps)\n",
+        "plot_and_save_metrics(rnn_history, 'RNN')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "fxe9lSRGPL4S",
+        "outputId": "469becb9-6823-4e27-9a72-bda66e8ce1d7"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "5/5 [==============================] - 4s 342ms/step - loss: 4042.7310 - mae: 48.3220 - val_loss: 1695.5544 - val_mae: 32.4890\n",
+            "Epoch 2/50\n",
+            "5/5 [==============================] - 1s 242ms/step - loss: 3324.6587 - mae: 41.1912 - val_loss: 1492.8391 - val_mae: 30.6948\n",
+            "Epoch 3/50\n",
+            "5/5 [==============================] - 1s 242ms/step - loss: 3283.0386 - mae: 42.0102 - val_loss: 1330.3291 - val_mae: 29.0576\n",
+            "Epoch 4/50\n",
+            "5/5 [==============================] - 1s 128ms/step - loss: 2799.4746 - mae: 37.8400 - val_loss: 1191.6454 - val_mae: 27.4503\n",
+            "Epoch 5/50\n",
+            "5/5 [==============================] - 1s 124ms/step - loss: 2721.4531 - mae: 37.7790 - val_loss: 1068.2734 - val_mae: 25.8688\n",
+            "Epoch 6/50\n",
+            "5/5 [==============================] - 1s 131ms/step - loss: 2437.0906 - mae: 36.1167 - val_loss: 964.5764 - val_mae: 25.2696\n",
+            "Epoch 7/50\n",
+            "5/5 [==============================] - 1s 122ms/step - loss: 2278.8877 - mae: 36.1504 - val_loss: 883.9755 - val_mae: 24.8243\n",
+            "Epoch 8/50\n",
+            "5/5 [==============================] - 1s 129ms/step - loss: 2047.2324 - mae: 34.6272 - val_loss: 828.8517 - val_mae: 24.3800\n",
+            "Epoch 9/50\n",
+            "5/5 [==============================] - 1s 134ms/step - loss: 2073.9636 - mae: 35.8929 - val_loss: 799.5880 - val_mae: 24.1875\n",
+            "Epoch 10/50\n",
+            "5/5 [==============================] - 1s 128ms/step - loss: 1869.7659 - mae: 34.8741 - val_loss: 793.7123 - val_mae: 24.4633\n",
+            "Epoch 11/50\n",
+            "5/5 [==============================] - 1s 133ms/step - loss: 1987.3824 - mae: 36.2953 - val_loss: 806.9340 - val_mae: 24.8318\n",
+            "Epoch 12/50\n",
+            "5/5 [==============================] - 1s 125ms/step - loss: 1820.6211 - mae: 35.3633 - val_loss: 833.9470 - val_mae: 25.1659\n",
+            "Epoch 13/50\n",
+            "5/5 [==============================] - 1s 136ms/step - loss: 1830.7771 - mae: 36.1497 - val_loss: 865.1969 - val_mae: 25.4284\n",
+            "Epoch 14/50\n",
+            "5/5 [==============================] - 1s 210ms/step - loss: 1854.7955 - mae: 36.2381 - val_loss: 897.0051 - val_mae: 25.6412\n",
+            "Epoch 15/50\n",
+            "5/5 [==============================] - 1s 239ms/step - loss: 1790.9226 - mae: 35.9118 - val_loss: 921.3814 - val_mae: 25.7828\n",
+            "Epoch 16/50\n",
+            "5/5 [==============================] - 1s 243ms/step - loss: 1855.0286 - mae: 37.1758 - val_loss: 938.0343 - val_mae: 25.8719\n",
+            "Epoch 17/50\n",
+            "5/5 [==============================] - 1s 164ms/step - loss: 1764.6254 - mae: 36.1108 - val_loss: 946.6630 - val_mae: 25.9161\n",
+            "Epoch 18/50\n",
+            "5/5 [==============================] - 1s 125ms/step - loss: 1685.0660 - mae: 35.1971 - val_loss: 948.5511 - val_mae: 25.9256\n",
+            "Epoch 19/50\n",
+            "5/5 [==============================] - 1s 129ms/step - loss: 1718.8802 - mae: 35.8591 - val_loss: 946.5695 - val_mae: 25.9156\n",
+            "Epoch 20/50\n",
+            "5/5 [==============================] - 1s 132ms/step - loss: 1801.4363 - mae: 36.6745 - val_loss: 940.3362 - val_mae: 25.8838\n",
+            "Epoch 21/50\n",
+            "5/5 [==============================] - 1s 130ms/step - loss: 1696.1315 - mae: 35.8057 - val_loss: 936.7662 - val_mae: 25.8653\n",
+            "Epoch 22/50\n",
+            "5/5 [==============================] - 1s 118ms/step - loss: 1894.1345 - mae: 37.8964 - val_loss: 929.8459 - val_mae: 25.8288\n",
+            "Epoch 23/50\n",
+            "5/5 [==============================] - 1s 128ms/step - loss: 1757.3346 - mae: 36.1258 - val_loss: 927.7477 - val_mae: 25.8175\n",
+            "Epoch 24/50\n",
+            "5/5 [==============================] - 1s 129ms/step - loss: 1827.9102 - mae: 37.1343 - val_loss: 922.7157 - val_mae: 25.7902\n",
+            "Epoch 25/50\n",
+            "5/5 [==============================] - 1s 126ms/step - loss: 1847.0925 - mae: 36.7025 - val_loss: 921.8129 - val_mae: 25.7852\n",
+            "Epoch 26/50\n",
+            "5/5 [==============================] - 1s 132ms/step - loss: 1794.7502 - mae: 36.1110 - val_loss: 919.7438 - val_mae: 25.7738\n",
+            "Epoch 27/50\n",
+            "5/5 [==============================] - 1s 128ms/step - loss: 1854.5808 - mae: 37.0710 - val_loss: 919.2271 - val_mae: 25.7709\n",
+            "Epoch 28/50\n",
+            "5/5 [==============================] - 1s 127ms/step - loss: 1761.8672 - mae: 35.8763 - val_loss: 919.6247 - val_mae: 25.7731\n",
+            "Epoch 29/50\n",
+            "5/5 [==============================] - 1s 122ms/step - loss: 1686.8649 - mae: 34.9874 - val_loss: 920.2872 - val_mae: 25.7768\n",
+            "Epoch 30/50\n",
+            "5/5 [==============================] - 1s 129ms/step - loss: 1714.8586 - mae: 35.6108 - val_loss: 921.7368 - val_mae: 25.7848\n",
+            "Epoch 31/50\n",
+            "5/5 [==============================] - 1s 125ms/step - loss: 1801.6277 - mae: 36.5112 - val_loss: 921.0551 - val_mae: 25.7810\n",
+            "Epoch 32/50\n",
+            "5/5 [==============================] - 1s 125ms/step - loss: 1696.7019 - mae: 35.6848 - val_loss: 923.9301 - val_mae: 25.7968\n",
+            "Epoch 33/50\n",
+            "5/5 [==============================] - 1s 178ms/step - loss: 1894.1155 - mae: 37.7900 - val_loss: 922.3719 - val_mae: 25.7883\n",
+            "Epoch 34/50\n",
+            "5/5 [==============================] - 1s 223ms/step - loss: 1757.9441 - mae: 36.0855 - val_loss: 925.0201 - val_mae: 25.8028\n",
+            "Epoch 35/50\n",
+            "5/5 [==============================] - 1s 225ms/step - loss: 1827.4957 - mae: 37.1134 - val_loss: 922.7068 - val_mae: 25.7901\n",
+            "Epoch 36/50\n",
+            "5/5 [==============================] - 1s 210ms/step - loss: 1846.8494 - mae: 36.7044 - val_loss: 923.7708 - val_mae: 25.7960\n",
+            "Epoch 37/50\n",
+            "5/5 [==============================] - 1s 123ms/step - loss: 1795.0381 - mae: 36.1366 - val_loss: 922.2770 - val_mae: 25.7878\n",
+            "Epoch 38/50\n",
+            "5/5 [==============================] - 1s 119ms/step - loss: 1854.7006 - mae: 37.0997 - val_loss: 921.7565 - val_mae: 25.7849\n",
+            "Epoch 39/50\n",
+            "5/5 [==============================] - 1s 126ms/step - loss: 1762.0801 - mae: 35.9032 - val_loss: 921.7803 - val_mae: 25.7850\n",
+            "Epoch 40/50\n",
+            "5/5 [==============================] - 1s 117ms/step - loss: 1686.6350 - mae: 35.0017 - val_loss: 921.8553 - val_mae: 25.7854\n",
+            "Epoch 41/50\n",
+            "5/5 [==============================] - 1s 119ms/step - loss: 1715.0566 - mae: 35.6255 - val_loss: 922.7961 - val_mae: 25.7906\n",
+            "Epoch 42/50\n",
+            "5/5 [==============================] - 1s 121ms/step - loss: 1801.5769 - mae: 36.5168 - val_loss: 921.3934 - val_mae: 25.7829\n",
+            "Epoch 43/50\n",
+            "5/5 [==============================] - 1s 129ms/step - loss: 1696.7273 - mae: 35.6878 - val_loss: 924.1769 - val_mae: 25.7982\n",
+            "Epoch 44/50\n",
+            "5/5 [==============================] - 1s 122ms/step - loss: 1894.1404 - mae: 37.7905 - val_loss: 922.1295 - val_mae: 25.7870\n",
+            "Epoch 45/50\n",
+            "5/5 [==============================] - 1s 125ms/step - loss: 1758.0101 - mae: 36.0839 - val_loss: 924.9550 - val_mae: 25.8024\n",
+            "Epoch 46/50\n",
+            "5/5 [==============================] - 1s 129ms/step - loss: 1827.5271 - mae: 37.1119 - val_loss: 922.3502 - val_mae: 25.7882\n",
+            "Epoch 47/50\n",
+            "5/5 [==============================] - 1s 116ms/step - loss: 1846.9082 - mae: 36.7014 - val_loss: 923.5861 - val_mae: 25.7950\n",
+            "Epoch 48/50\n",
+            "5/5 [==============================] - 1s 128ms/step - loss: 1795.0586 - mae: 36.1356 - val_loss: 922.0163 - val_mae: 25.7863\n",
+            "Epoch 49/50\n",
+            "5/5 [==============================] - 1s 116ms/step - loss: 1854.6956 - mae: 37.0972 - val_loss: 921.5366 - val_mae: 25.7837\n",
+            "Epoch 50/50\n",
+            "5/5 [==============================] - 1s 116ms/step - loss: 1762.1467 - mae: 35.9044 - val_loss: 921.6439 - val_mae: 25.7843\n"
+          ]
+        },
+        {
+          "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": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Comparision"
+      ],
+      "metadata": {
+        "id": "JJ2TijTwPzLJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def compare_models_metrics(models_history, metric):\n",
+        "    model_names = list(models_history.keys())\n",
+        "    metrics_values = [history.history[metric][-1] for history in models_history.values()]\n",
+        "\n",
+        "    plt.bar(model_names, metrics_values)\n",
+        "    plt.xlabel('Models')\n",
+        "    plt.ylabel(metric)\n",
+        "    plt.title('Comparison of Models based on ' + metric)\n",
+        "    plt.savefig('comparison_bar_graph_' + metric + '.png')\n",
+        "    plt.show()\n",
+        "\n",
+        "# Assume models_history is a dictionary containing histories of all five models\n",
+        "models_history = {'ANN': ann_history, 'CNN': cnn_history, 'DNN': dnn_history, 'LSTM': lstm_history, 'RNN': rnn_history}\n",
+        "\n",
+        "# Compare models based on validation loss\n",
+        "compare_models_metrics(models_history, 'val_loss')\n",
+        "\n",
+        "# Alternatively, you can compare based on other metrics like validation MAE\n",
+        "compare_models_metrics(models_history, 'val_mae')\n"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2021-08-07T05:40:32.81353Z",
+          "iopub.execute_input": "2021-08-07T05:40:32.813868Z",
+          "iopub.status.idle": "2021-08-07T05:40:33.005901Z",
+          "shell.execute_reply.started": "2021-08-07T05:40:32.813837Z",
+          "shell.execute_reply": "2021-08-07T05:40:33.004831Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 927
+        },
+        "id": "tu0YynbgJs9S",
+        "outputId": "98178b43-9597-4abd-8aaf-3c74bd5a6111"
+      },
+      "execution_count": 44,
+      "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": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Simple Object Detection/requirements.txt b/Simple Object Detection/requirements.txt
new file mode 100644
index 000000000..c3fce21cf
--- /dev/null
+++ b/Simple Object Detection/requirements.txt	
@@ -0,0 +1,7 @@
+TensorFlow
+Keras
+OpenCV
+Matplotlib
+NumPy
+Pandas
+Seaborn
\ No newline at end of file