{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# 📊 Training Visualization for Faster R-CNN\n",
                "This notebook helps to visualize the training progress, loss curves, and mAP (Mean Average Precision) scores.\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [
                {
                    "ename": "FileNotFoundError",
                    "evalue": "[Errno 2] No such file or directory: 'config.yaml'",
                    "output_type": "error",
                    "traceback": [
                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
                        "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
                        "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mjson\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01myaml\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mconfig.yaml\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f: \n\u001b[1;32m      6\u001b[0m     config \u001b[38;5;241m=\u001b[39m yaml\u001b[38;5;241m.\u001b[39msafe_load(f)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Load training log file\u001b[39;00m\n",
                        "File \u001b[0;32m~/studies/northEastern/Computer Vision/Assigments/objectlocalization/venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py:324\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m    317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m    318\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    319\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    320\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    321\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    322\u001b[0m     )\n\u001b[0;32m--> 324\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
                        "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'config.yaml'"
                    ]
                }
            ],
            "source": [
                "import matplotlib.pyplot as plt\n",
                "import json\n",
                "import yaml\n",
                "\n",
                "with open(\"config.yaml\", \"r\") as f: \n",
                "    config = yaml.safe_load(f)\n",
                "\n",
                "# Load training log file\n",
                "log_file = config[\"notebooks\"][\"visualization\"]\n",
                "with open(log_file, 'r') as f:\n",
                "    log_data = json.load(f)\n",
                "\n",
                "# Extract loss and mAP values\n",
                "epochs = list(range(1, len(log_data['loss']) + 1))\n",
                "loss_values = log_data['loss']\n",
                "map_values = log_data['mAP']\n",
                "\n",
                "# Plot loss curve\n",
                "plt.figure(figsize=(10, 5))\n",
                "plt.plot(epochs, loss_values, marker='o', label='Loss')\n",
                "plt.xlabel('Epochs')\n",
                "plt.ylabel('Loss')\n",
                "plt.title('Training Loss Curve')\n",
                "plt.legend()\n",
                "plt.show()\n",
                "\n",
                "# Plot mAP curve\n",
                "plt.figure(figsize=(10, 5))\n",
                "plt.plot(epochs, map_values, marker='o', label='mAP')\n",
                "plt.xlabel('Epochs')\n",
                "plt.ylabel('Mean Average Precision (mAP)')\n",
                "plt.title('mAP Progression Over Epochs')\n",
                "plt.legend()\n",
                "plt.show()"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.12.2"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}