{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Xyxjqlt4Vvv2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3b8b1243-5380-45fd-d336-ed25087075d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================================================\n",
            "COMPREHENSIVE MODEL ANALYSIS\n",
            "================================================================================\n",
            "\n",
            "1. OVERALL PERFORMANCE RANKING:\n",
            "----------------------------------------\n",
            "1. vgg16_transfer: 0.9297 accuracy\n",
            "2. baseline_cnn: 0.8594 accuracy\n",
            "3. resnet50_transfer: 0.6953 accuracy\n",
            "\n",
            "2. DETAILED METRICS COMPARISON:\n",
            "----------------------------------------\n",
            "Test Accuracy: vgg16_transfer (0.9297)\n",
            "Macro Avg F1-Score: vgg16_transfer (0.9301)\n",
            "Weighted Avg F1-Score: vgg16_transfer (0.9298)\n",
            "\n",
            "3. PER-CLASS PERFORMANCE:\n",
            "----------------------------------------\n",
            "angular_leaf_spot: vgg16_transfer (0.9176 F1-score)\n",
            "bean_rust: vgg16_transfer (0.8966 F1-score)\n",
            "healthy: vgg16_transfer (0.9762 F1-score)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1200 with 4 Axes>"
            ],
            "image/png": 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Zs2bVwoULVa1aNbu1pWDBgurRo0e0chcXF0mSv7+/vvvuO2XOnFkFChR4qYuW+vXra/PmzWratKnat2+vsLAwnTlzRhs2bFCpUqWUK1euVz2MN+rixYuqXLmyrl69qoYNG+qLL76Qi4uLTp48qTlz5mj16tX6559/7N3M1+rXX3+1dxMAAMB/iMzSe/bsUd26dS3lDx8+1J9//iknJyft3btXFStWtCy7du2arl27piZNmsRqX7Nnz5bZbI6bhtsQGBiooUOHSpLd74gzDEOLFy9W1qxZtX79ej169EhJkya1W3umT58ebTR/iRIlrJb7+vqqWLFiunv3bqy3v3z5cjVu3FgFCxZUly5dlCJFCl26dEl//PGHZs+erWbNmr3yMbxJhmGoTZs2+umnn1SoUCF1795d6dOn182bN7V69Wp9/PHH2rt371v5ZVJcadGihZo0aRLvr62BhIhOdAAvrHr16lq+fLkmTZokJ6f//fpYtGiRihQpIn9//zfaHsMwFBwcLHd39zjZ3oIFC1S4cGG1atVK3377rZ48eaLEiRPHybZjK2PGjPrss89sLs+QIYNu3ryp9OnT68iRIypWrFistn/48GFt2LBBI0aM0Lfffmu1bMqUKQoICHiZZr+U4OBgubi4yMHh5W+OCg8PV7169eTn56edO3dG+6JnxIgRGjVq1Ks29a0VGBioRIkSWb5kAQAAby9PT095eXlFu8Nz//79MgxDDRs2jLYs8vWzGee/ODs7v1pj45mdO3fq33//1fbt2+Xt7a1Vq1apVatWdmtPgwYNlDp1apvL58+fr4wZM8rBwUH58uWL9faHDBmiPHny6MCBA9Fy4O3bt2O9vZcVV9dlY8eO1U8//aSuXbtq3LhxMplMlmX9+/fX/Pnzra5DE5LIa09HR0c5OjrauzkAYsB0LgBeWNOmTXX37l399ttvlrLQ0FCtWLHC5iiHMWPGqFSpUkqVKpXc3d1VpEgRrVixIsa6CxYsUPHixZUoUSKlSJFC5cqVsxpZmzVrVtWsWVNbt25V0aJF5e7urpkzZ0p6Ogq5YcOGSpkypRIlSqSPPvpIGzdufOFjCwoK0urVq9WkSRM1atRIQUFBWrt2rdVxmEwmXblyJdq6/fr1k4uLi+7fv28pmzp1qrJlyyZ3d3cVL15cu3fvjtP5ql1dXZU+ffqXXv/ChQuSpNKlS0db5ujoqFSpUlmVXb9+XW3btpWnp6dcXV3l5eWljh07KjQ01FLnRT6DnTt3ymQyacmSJRowYIAyZsyoRIkS6eHDh5KkgwcPqmrVqkqWLJkSJUqk8uXLa+/evf95PCtXrtSJEyfUv3//GC8uPTw8NGLECKuy5cuXq0iRInJ3d1fq1Kn12Wef6fr161Z1IucCvXr1qmrWrKkkSZIoY8aMmjp1qiTp1KlTqlSpkhInTqwsWbJEu202ciqkP/74Q19++aVSpUolDw8PtWzZ0urnRZLWrl2rGjVqWN7j7Nmza9iwYYqIiLCqV6FCBeXLl0++vr4qV66cEiVKZPkiJKafscmTJytv3ryW86po0aLR2nns2DFVq1ZNHh4eSpIkiT7++GMdOHAgxmPZu3evunfvrjRp0ihx4sSqW7eu7ty5E9PHAgAAbChTpoyOHTumoKAgS9nevXuVN29eVatWTQcOHLAaQb53716ZTCar7LZgwQJLlkmZMqWaNGmia9euWe0npjnR7969qxYtWlim82vVqpVOnDhhc3rA69evq06dOkqSJInSpEmjnj17WvLJ5cuXlSZNGknS0KFDLVOWDBkyxLL+mTNn1KBBA6VMmVJubm4qWrSo1q1bF20/p0+fVqVKleTu7q733ntPw4cPj/Uo+oULFypPnjyqWLGiKleurIULF1qW+fn5ycnJyTJqPqqzZ8/KZDJpypQplrKTJ0+qfPnyVu2ZN29enM5XnSlTplcaSHLhwgUVK1YsxoEUadOmtXptNps1ceJE5c+fX25ubkqTJo2qVq2qI0eOWOqEh4dr2LBhyp49u1xdXZU1a1Z9++23CgkJsdrW867LAgIC1LVrV2XKlEmurq56//33NWrUqP/8LIOCguTj46NcuXJZrr2e1aJFCxUvXtzyOjbXH8uWLdPQoUOVMWNGJU2aVA0aNNCDBw8UEhKirl27Km3atEqSJIlat24d7XhNJpO+/vprLVy4UDlz5pSbm5uKFCmiP/74w6relStX9NVXXylnzpxyd3dXqlSp1LBhw2g/L5G5eteuXfrqq6+UNm1avffee1bLoq5z5MgReXt7K3Xq1HJ3d5eXl5fatGljtc0nT56oR48elvc9Z86cGjNmjAzDiPFY1qxZo3z58snV1VV58+bVli1bnvv5AGAkOoBYyJo1q0qWLKnFixdbpjrZvHmzHjx4oCZNmmjSpEnR1pk4caJq166t5s2bKzQ0VEuWLFHDhg21YcMG1ahRw1Jv6NChGjJkiEqVKqXvvvtOLi4uOnjwoLZv364qVapY6p09e1ZNmzbVl19+qfbt2ytnzpzy8/NTqVKlFBgYqG+++UapUqXSzz//rNq1a2vFihVWt8nasm7dOj1+/FhNmjRR+vTpVaFCBS1cuNDy5UCjRo3Uu3dvLVu2TL169bJad9myZapSpYpSpEgh6eltmV9//bXKli2rbt266fLly6pTp45SpEhhCUf/JSwsLNrI/kSJEilRokQvtP5/yZIli6SnFxqlS5d+7oiOGzduqHjx4goICNAXX3yhXLly6fr161qxYoUCAwPl4uIS689g2LBhcnFxUc+ePRUSEiIXFxdt375d1apVU5EiRTR48GA5ODho3rx5qlSpknbv3m0VmJ8VeSHWokWLFzr+n376Sa1bt1axYsXk4+MjPz8/TZw4UXv37tWxY8eUPHlyS92IiAhVq1ZN5cqV0w8//KCFCxfq66+/VuLEidW/f381b95c9erV04wZM9SyZUuVLFlSXl5eVvv7+uuvlTx5cg0ZMkRnz57V9OnTdeXKFUuoj2xTkiRJ1L17dyVJkkTbt2/XoEGD9PDhQ40ePdpqe3fv3lW1atXUpEkTffbZZ0qXLl2Mxzl79mx98803atCggbp06aLg4GCdPHlSBw8etPxsnz59WmXLlpWHh4d69+4tZ2dnzZw5UxUqVNCuXbusbjmWpM6dOytFihQaPHiwLl++rAkTJujrr7/W0qVLX+i9BwAATzvR58+fr4MHD1q+AI+cpqJUqVJ68OCB/vzzT3344YeWZbly5bIMdBgxYoQGDhyoRo0aqV27drpz544mT56scuXKRcsyUZnNZtWqVUuHDh1Sx44dlStXLq1du9bmaO2IiAh5e3urRIkSGjNmjH7//XeNHTtW2bNnV8eOHZUmTRpNnz5dHTt2VN26dVWvXj1JsrT79OnTKl26tDJmzKi+ffsqceLEWrZsmerUqaOVK1daMuKtW7dUsWJFhYeHW+rNmjUrViObQ0JCtHLlSsuUiE2bNlXr1q1169YtpU+fXunSpVP58uW1bNkyDR482GrdpUuXytHRUQ0bNpT09IuDihUrymQyqV+/fkqcOLF+/PHHWE+xce/ePavXjo6OlmuGuJAlSxZt27ZN//77739eZ7Rt21Y//fSTqlWrpnbt2ik8PFy7d+/WgQMHVLRoUUlSu3bt9PPPP6tBgwbq0aOHDh48KB8fH/3999/R5vCP6bosMDBQ5cuX1/Xr1/Xll18qc+bM2rdvn/r166ebN29qwoQJNtu3Z88e3bt3T127dn2hkdixvf7w8fGRu7u7+vbtq/Pnz2vy5MlydnaWg4OD7t+/ryFDhujAgQP66aef5OXlpUGDBlmtv2vXLi1dulTffPONXF1dNW3aNFWtWlWHDh2y3EVw+PBh7du3T02aNNF7772ny5cva/r06apQoYL++uuvaNdyX331ldKkSaNBgwbpyZMnMR7n7du3VaVKFaVJk0Z9+/ZV8uTJdfnyZa1atcpSxzAM1a5dWzt27FDbtm1VsGBBbd26Vb169dL169c1fvz4aO/1qlWr9NVXXylp0qSaNGmS6tevr6tXr0YbTAUgCgMA/sO8efMMScbhw4eNKVOmGEmTJjUCAwMNwzCMhg0bGhUrVjQMwzCyZMli1KhRw2rdyHqRQkNDjXz58hmVKlWylJ07d85wcHAw6tata0RERFjVN5vNlv/PkiWLIcnYsmWLVZ2uXbsakozdu3dbyh49emR4eXkZWbNmjbbNmNSsWdMoXbq05fWsWbMMJycn4/bt25aykiVLGkWKFLFa79ChQ4Yk45dffjEMwzBCQkKMVKlSGcWKFTPCwsIs9X766SdDklG+fPn/bEvkcT77b/DgwTHWP3z4sCHJmDdv3n9uO5LZbDbKly9vSDLSpUtnNG3a1Jg6dapx5cqVaHVbtmxpODg4GIcPH45xO4bx4p/Bjh07DElGtmzZrH42zGazkSNHDsPb29vqMw8MDDS8vLyMTz755LnHU6hQISNZsmQvdOyhoaFG2rRpjXz58hlBQUGW8g0bNhiSjEGDBlnKWrVqZUgyRo4caSm7f/++4e7ubphMJmPJkiWW8jNnzkT7nCLPnSJFihihoaGW8h9++MGQZKxdu9bqWJ/15ZdfGokSJTKCg4MtZZGf24wZM6LVL1++vNXP2KeffmrkzZv3ue9HnTp1DBcXF+PChQuWshs3bhhJkyY1ypUrF+1YKleubPUZdevWzXB0dDQCAgKeux8AAPA/p0+fNiQZw4YNMwzDMMLCwozEiRMbP//8s2EYhpEuXTpj6tSphmEYxsOHDw1HR0ejffv2hmEYxuXLlw1HR0djxIgRVts8deqU4eTkZFXeqlUrI0uWLJbXK1euNCQZEyZMsJRFREQYlSpVipYnI3PQd999Z7WfQoUKWWXiO3fu2MyqH3/8sZE/f36rLGM2m41SpUoZOXLksJRFZsmDBw9aym7fvm0kS5bMkGRcunQpxvcxqhUrVhiSjHPnzhmG8fR9c3NzM8aPH2+pM3PmTEOScerUKat18+TJY3V90rlzZ8NkMhnHjh2zlN29e9dImTLlC7Vn8ODBMeb5qJ/Fs/LmzftC1wpRzZkzx5BkuLi4GBUrVjQGDhxo7N69O9r1z/bt2w1JxjfffBNtG5G57vjx44Yko127dlbLe/bsaUgytm/fbimzdV02bNgwI3HixMY///xjVd63b1/D0dHRuHr1qs1jmThxoiHJWL169Qsde2yvP/Lly2eVx5s2bWqYTCajWrVqVtstWbJktM8p8vM7cuSIpezKlSuGm5ubUbduXUtZTHl+//79VteLhvG/XF2mTBkjPDzcqn7kssifsdWrV1uuxW1Zs2aNIckYPny4VXmDBg0Mk8lknD9/3upYXFxcrMpOnDhhSDImT55scx8ADIPpXADESuRUJxs2bNCjR4+0YcOG5z6wJurokfv37+vBgwcqW7asjh49ailfs2aNzGazBg0aFO12xmdv4/Py8pK3t7dV2aZNm1S8eHGraTySJEmiL774QpcvX9Zff/313GO6e/eutm7dqqZNm1rK6tevb7ntL1Ljxo3l6+trmQpFejpqxdXVVZ9++qmkp7fa3b17V+3bt7ca3d28efNYjTopUaKEfvvtN6t/LVu2fOH1/4vJZNLWrVs1fPhwpUiRQosXL1anTp2UJUsWNW7c2DInutls1po1a1SrVi3LCJVntyPF/jNo1aqV1c/G8ePHde7cOTVr1kx3796Vv7+//P399eTJE3388cf6448/nnsL6MOHD1/4oVFHjhzR7du39dVXX8nNzc1SXqNGDeXKlSvGaYDatWtn+f/kyZMrZ86cSpw4sRo1amQpz5kzp5InT66LFy9GW/+LL76wmpO0Y8eOcnJy0qZNmyxlUd+PR48eyd/fX2XLllVgYKDOnDljtT1XV1e1bt36P481efLk+vfff3X48OEYl0dEROjXX39VnTp1lC1bNkt5hgwZ1KxZM+3Zs8cy1U7UY4l6XpYtW1YRERExTnUEAABiljt3bqVKlcoy1/mJEyf05MkTywMTS5UqZZnSbv/+/YqIiLDkrFWrVslsNqtRo0aWzOTv76/06dMrR44c2rFjh839btmyRc7Ozmrfvr2lzMHBQZ06dbK5TocOHaxely1bNsa886x79+5p+/btatSokSXb+Pv76+7du/L29ta5c+csU+lt2rRJH330kdWdh2nSpFHz5s3/cz+RFi5cqKJFi+r999+XJCVNmlQ1atSwmtKlXr16cnJysrqD7s8//9Rff/2lxo0bW8q2bNmikiVLqmDBgpaylClTxqo90tMpB6Pm+ahtiQtt2rTRli1bVKFCBe3Zs0fDhg1T2bJllSNHDu3bt8+qHSaTKdoIfMk6z0tS9+7drZZHjux/NiPHdF22fPlylS1bVilSpLD62axcubIiIiKiTX8SVWTmfNFMH9vrj5YtW1rl8RIlSlgeZBpViRIldO3aNYWHh1uVlyxZUkWKFLG8zpw5sz799FNt3brVMr1R1DwfFhamu3fv6v3331fy5Mmtrn8jtW/f/j9H3UfeVbJhwwaFhYXFWGfTpk1ydHTUN998Y1Xeo0cPGYahzZs3W5VXrlxZ2bNnt7z+8MMP5eHh8ULnNfAuoxMdQKykSZNGlStX1qJFi7Rq1SpFRESoQYMGNutv2LBBH330kdzc3JQyZUrLLZ8PHjyw1Llw4YIcHByUJ0+e/9z/s9NkSE/nnsuZM2e08ty5c1uWP8/SpUsVFhamQoUK6fz58zp//rzu3bunEiVKWAXdhg0bysHBwRK6DcPQ8uXLLXNJR91XZHiP5OTkFG0+yudJnTq1KleubPUvaifni3rw4IFu3bpl+Rf1llJXV1f1799ff//9t27cuKHFixfro48+0rJly/T1119Lku7cuaOHDx/+54OOYvsZPPs5njt3TtLTzvU0adJY/fvxxx8VEhJi9TPzLA8PDz169Oi5bYzaVkkxtjdXrlzR2ho5Z2RUyZIl03vvvRftS55kyZJFm+tcknLkyGH1OkmSJMqQIYPVXIenT59W3bp1lSxZMnl4eChNmjSWh8s+e+wZM2Z8oYeI9unTR0mSJFHx4sWVI0cOderUyWqO+Tt37igwMNDmZ2c2m6PNrZo5c2ar15FfDsV03AAAIGYmk0mlSpWyzH2+d+9epU2b1pIho3aiR/43srPw3LlzMgxDOXLkiJab/v777+c+UPLKlSvKkCFDtGklns2ukWLKQSlSpHihv/vnz5+XYRgaOHBgtHZGduZGtvXKlSvR8pIUc16LSUBAgDZt2qTy5ctb8vz58+dVunRpHTlyRP/884+kpxn7448/thoos3TpUjk5OVmmoolsT0zvia33yZZy5cpZ5fmYnkf0X0JDQ63y/K1bt6yemePt7a2tW7cqICBAf/zxhzp16qQrV66oZs2alvf3woUL8vT0VMqUKW3u58qVK3JwcIh2jOnTp1fy5Mn/M89LT382t2zZEu3zrly5sqTnP+w08noqNpk+Ntcfz2bYZMmSSXo6L/2z5WazOVr+junn84MPPlBgYKDl+UBBQUEaNGiQZV7y1KlTK02aNAoICIjxWiam9/BZ5cuXV/369TV06FClTp1an376qebNm2c1b/uVK1fk6ekZ7QuIF30vpBc/r4F3GXOiA4i1Zs2aqX379rp165aqVatmc87F3bt3q3bt2ipXrpymTZumDBkyyNnZWfPmzYv2YMMX9apPfI9JZEe5rVB78eJFZcuWTZ6enipbtqyWLVumb7/9VgcOHNDVq1c1atSoOG9TXOnSpYt+/vlny+vy5ctr586d0eplyJBBTZo0Uf369ZU3b14tW7YsxgdLxZVnP8fIUeajR4+2GvETVZIkSWxuL1euXDp27JiuXbsWLQi/KlujQ2yVG888vOdFBAQEqHz58vLw8NB3332n7Nmzy83NTUePHlWfPn2ijcJ/0fMgd+7cOnv2rDZs2KAtW7Zo5cqVmjZtmgYNGhTjQ7VeRFweNwAA77IyZcpo/fr1OnXqlGU+9EilSpWyzGe8Z88eeXp6WgZUmM1mmUwmbd68Oca/y8/LTLH1InNT2xKZX3r27BltxHKk2HZK27J8+XKFhIRo7NixGjt2bLTlCxcutGSfJk2aqHXr1jp+/LgKFiyoZcuW6eOPP1bq1KnjpC1xbd++fapYsaJV2aVLl6IN0EmUKJHKli2rsmXLKnXq1Bo6dKg2b95sc757W2J6oGdMYsqjZrNZn3zyiXr37h3jOh988IHN7eXKlUuSdOrUKdWpU+eF2hAbbyLTd+7cWfPmzVPXrl1VsmRJJUuWTCaTSU2aNInxrtoXyfQmk0krVqzQgQMHtH79em3dulVt2rTR2LFjdeDAgZc638nzwMuhEx1ArNWtW1dffvmlDhw48NyHCa5cuVJubm7aunWr1UN45s2bZ1Uve/bsMpvN+uuvv2x2oD5PlixZdPbs2WjlkVNgRD5EMyaXLl3Svn379PXXX6t8+fJWy8xms1q0aKFFixZpwIABkp5O6fLVV1/p7NmzWrp0qRIlSqRatWpZtUV6OvImatgNDw/X5cuXLQ9ZelN69+5tGc0s6T+nlHF2dtaHH36oc+fOyd/fX2nTppWHh4f+/PPP5673Kp+BJMvthB4eHpaRKrFRq1YtLV68WAsWLFC/fv3+s63S04chVapUyWrZ2bNn/7OtL+PcuXNWPw+PHz/WzZs3Vb16dUnSzp07dffuXa1atUrlypWz1Lt06dIr7ztx4sRq3LixGjdurNDQUNWrV08jRoxQv379lCZNGiVKlMjmZ+fg4BDnX0oAAICnIkeW79mzR3v37lXXrl0ty4oUKSJXV1ft3LlTBw8etGQG6WluMgxDXl5ez+2UjEmWLFm0Y8cOBQYGWo1GP3/+/Esfh61O18hOf2dn5//Md1myZLHcmRhVTBklJgsXLlS+fPlinK5k5syZWrRokaUTvU6dOvryyy8t1zH//PNPtPyYJUuWGN+TV3mfXlaBAgX022+/WZWlT5/+uetETsN48+ZNSU9/ZrZu3ap79+7ZHI2eJUsWmc1mnTt3zjKCWXr6AM+AgIAXysjZs2fX48ePXyrPlylTxjLN5LfffvufX+C86vVHbMX08/nPP/8oUaJElrs1VqxYoVatWll9kRMcHGyZKvNVfPTRR/roo480YsQILVq0SM2bN9eSJUvUrl07ZcmSRb///rsePXpkNRr9db0XwLuK6VwAxFqSJEk0ffp0DRkyxKoD+VmOjo4ymUxWtxtevnxZa9assapXp04dOTg46Lvvvov2Df2LfBtevXp1HTp0SPv377eUPXnyRLNmzVLWrFmfO01M5Cj03r17q0GDBlb/GjVqpPLly1tN6VK/fn05Ojpq8eLFWr58uWrWrKnEiRNblhctWlSpUqXS7NmzrebRW7hwoV1uj8uTJ4/VLaSR8/idO3dOV69ejVY/ICBA+/fvV4oUKZQmTRo5ODioTp06Wr9+vY4cORKtfuTn8yqfgfT0QjF79uwaM2aMHj9+HG155C2StjRo0ED58+fXiBEjrNoQ6dGjR+rfv7+kp59R2rRpNWPGDKvbIDdv3qy///5bNWrUeO6+XsasWbOs5jCcPn26wsPDVa1aNUn/Gw0S9ec9NDRU06ZNe6X93r171+q1i4uL8uTJI8MwFBYWJkdHR1WpUkVr1661mlrGz89PixYtUpkyZSy31gIAgLhVtGhRubm5aeHChbp+/brVSHRXV1cVLlxYU6dO1ZMnT6zmfa5Xr54cHR01dOjQaFnZMIxof/+j8vb2VlhYmGbPnm0pM5vNmjp16ksfR2Rn/LMdhWnTplWFChU0c+ZMS2duVFHzXfXq1XXgwAEdOnTIavmLzCF+7do1/fHHH2rUqFG0PN+gQQO1bt1a58+f18GDByU9nWPa29tby5Yt05IlS+Ti4hJt5LO3t7f279+v48ePW8ru3bsX53Oav4gUKVJEm+Yx8rk+27Zti3GdyPnNI6c7qV+/vgzDiPFOxKh5XpImTJhgtXz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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "4. PERFORMANCE SUMMARY:\n",
            "----------------------------------------\n",
            "Best overall model: vgg16_transfer\n",
            "Accuracy range: 0.6953 - 0.9297\n",
            "Average accuracy: 0.8281\n",
            "Improvement from worst to best: 33.71%\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "def analyze_model_performance():\n",
        "    try:\n",
        "        df = pd.read_csv('model_comparison.csv')\n",
        "    except FileNotFoundError:\n",
        "        print(\"model_comparison.csv not found. Run transfer_learning_bean_classifier.py first.\")\n",
        "        return\n",
        "\n",
        "    print(\"=\"*80)\n",
        "    print(\"COMPREHENSIVE MODEL ANALYSIS\")\n",
        "    print(\"=\"*80)\n",
        "\n",
        "    print(\"\\n1. OVERALL PERFORMANCE RANKING:\")\n",
        "    print(\"-\" * 40)\n",
        "    ranking = df.sort_values('Test Accuracy', ascending=False)\n",
        "    for i, (_, row) in enumerate(ranking.iterrows(), 1):\n",
        "        print(f\"{i}. {row['Model']}: {row['Test Accuracy']:.4f} accuracy\")\n",
        "\n",
        "    print(\"\\n2. DETAILED METRICS COMPARISON:\")\n",
        "    print(\"-\" * 40)\n",
        "    metrics = ['Test Accuracy', 'Macro Avg F1-Score', 'Weighted Avg F1-Score']\n",
        "    for metric in metrics:\n",
        "        best_model = df.loc[df[metric].idxmax(), 'Model']\n",
        "        best_value = df[metric].max()\n",
        "        print(f\"{metric}: {best_model} ({best_value:.4f})\")\n",
        "\n",
        "    print(\"\\n3. PER-CLASS PERFORMANCE:\")\n",
        "    print(\"-\" * 40)\n",
        "    class_names = ['angular_leaf_spot', 'bean_rust', 'healthy']\n",
        "\n",
        "    for class_name in class_names:\n",
        "        f1_col = f'{class_name} F1-Score'\n",
        "        if f1_col in df.columns:\n",
        "            best_model = df.loc[df[f1_col].idxmax(), 'Model']\n",
        "            best_value = df[f1_col].max()\n",
        "            print(f\"{class_name}: {best_model} ({best_value:.4f} F1-score)\")\n",
        "\n",
        "    fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n",
        "\n",
        "    metrics_to_plot = ['Test Accuracy', 'Test Loss', 'Macro Avg F1-Score', 'Weighted Avg F1-Score']\n",
        "\n",
        "    for i, metric in enumerate(metrics_to_plot):\n",
        "        ax = axes[i//2, i%2]\n",
        "        bars = ax.bar(df['Model'], df[metric])\n",
        "        ax.set_title(f'{metric} Comparison')\n",
        "        ax.set_ylabel(metric)\n",
        "        ax.tick_params(axis='x', rotation=45)\n",
        "\n",
        "        for bar, value in zip(bars, df[metric]):\n",
        "            ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.001,\n",
        "                   f'{value:.3f}', ha='center', va='bottom')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.savefig('detailed_model_comparison.png', dpi=300, bbox_inches='tight')\n",
        "    plt.show()\n",
        "\n",
        "    print(f\"\\n4. PERFORMANCE SUMMARY:\")\n",
        "    print(\"-\" * 40)\n",
        "    print(f\"Best overall model: {ranking.iloc[0]['Model']}\")\n",
        "    print(f\"Accuracy range: {df['Test Accuracy'].min():.4f} - {df['Test Accuracy'].max():.4f}\")\n",
        "    print(f\"Average accuracy: {df['Test Accuracy'].mean():.4f}\")\n",
        "\n",
        "    improvement = (df['Test Accuracy'].max() - df['Test Accuracy'].min()) / df['Test Accuracy'].min() * 100\n",
        "    print(f\"Improvement from worst to best: {improvement:.2f}%\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    analyze_model_performance()"
      ]
    }
  ]
}