Faster_RCNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyORZERo0MXBJ61QJr0ykM3p",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abiraaaaaaf/Cattle-Detection/blob/main/Faster_RCNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cow detection (standing or sitting)\n",
        "\n",
        "Fariba Lotfi.\n",
        "Date: 29/10/2022\n",
        "\n",
        "Cattle Dataset (Cattle images standing or sitting): \n",
        "* 245 training \n",
        "* 35 validation"
      ],
      "metadata": {
        "id": "meTbIWlY3ydm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Main Problem:** Cow Detection (Standing or Sitting)\n",
        "\n",
        "**YOLO vs. Faster RCNN**"
      ],
      "metadata": {
        "id": "xEvVDuX44JdH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Faster RCNN:"
      ],
      "metadata": {
        "id": "NKNgsh554Kzx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "ud4YyFVfA5du"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Faster RCNN pipeline:"
      ],
      "metadata": {
        "id": "lMGL0ghy4OrN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "Xb6G1-gI3tbx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check if GPU is available:"
      ],
      "metadata": {
        "id": "kRRc2brL4V0E"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0z8dXrs2pcNz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8c73f9c-d339-421a-afa7-1fd6eabae21d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 1 GPU(s) available.\n",
            "We will use the GPU: Tesla T4\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "\n",
        "if torch.cuda.is_available():    \n",
        "    device = torch.device(\"cuda\")\n",
        "    print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
        "    print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
        "\n",
        "else:\n",
        "    print('No GPU available, using the CPU instead.')\n",
        "    device = torch.device(\"cpu\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import random\n",
        "import numpy as np\n",
        "import shutil"
      ],
      "metadata": {
        "id": "xDhtTypsFGv9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Information"
      ],
      "metadata": {
        "id": "wwTrtVwF_DJl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        " \n",
        "x = np.arange(2)\n",
        "plt.bar(x, height=[245,35])\n",
        "plt.xticks(x, ['train','test']);\n",
        "plt.style.use('ggplot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "7wWoP9BUaDVR",
        "outputId": "24e967b0-e5da-4252-be4d-9126d05b17e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        " \n",
        "x = np.arange(3)\n",
        "plt.bar(x, height=[235,25,15])\n",
        "plt.xticks(x, ['standing','sitting','both']);\n",
        "plt.style.use('ggplot')\n",
        "plt.title('Training')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "rMzsdePzfQ1O",
        "outputId": "86a4a2dc-b540-42a9-a62a-f8c58db7f106"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        " \n",
        "x = np.arange(3)\n",
        "plt.bar(x, height=[32,6,3])\n",
        "plt.xticks(x, ['standing','sitting','both']);\n",
        "plt.style.use('ggplot')\n",
        "plt.title('Test')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Jz9l6gblgkDy",
        "outputId": "564ad890-7e68-4d42-ad7c-ef2420da5acd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEJCAYAAACNNHw2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWAElEQVR4nO3de2zV9f3H8dfpOdRSa2lPW8BTyqAwgSqEIAhuQhkcwAEhIIOEhJIxh+uKblzSSZqNjkxmHXZFlyKZI0uEZIqGsU0R3JFbhDmqnUGhiiCIDujh0FJLpUBPP78/+OVIR+G0pzc+nOcjIen5ntv7nE/67Jdvz+lxGGOMAADWienqAQAAkSHgAGApAg4AliLgAGApAg4AliLgAGApAg4AliLguK04HI6b/uvXr1+bbn/gwIH69a9/3S6zAm3l6uoBgPZ0+vTp0Nf79+/X7NmzVV5errvvvluS5HQ6u2o0oN2xB47bSu/evUP/3G63JCktLS207eTJk5o8ebISEhKUlpamRx55RJ9//nno+l9++aVmz56t1NRUxcXFKTMzU2vWrJEkjR8/XseOHdOqVatCe/QnTpzoiocJSCLgiCKHDx9Wdna2HnzwQb333nvauXOnnE6nJk2apPr6eklSXl6eampq5PP59PHHH2vDhg3q06ePJGnLli3q16+fli9frtOnT+v06dPKyMjoyoeEKMchFESN3/3ud5o+fbpWrVoV2rZp0yYlJydr+/btmjlzpj7//HPNmjVLw4cPl6Qmx8zdbrecTqcSEhLUu3fvzh4fuA4BR9QoKyvT0aNHlZCQ0GR7fX29Pv30U0nSkiVL9JOf/ERvvvmmxo8fr2nTpmncuHFdMS4QFgFH1GhsbFROTo5WrFhx3XkpKSmSpIULF+rhhx/W9u3btWvXLn3/+9/XrFmztGnTps4eFwiLgCNqjBw5UgcPHtSAAQPkcDhueLm7775bCxcu1MKFCzV16lTNmzdP69atU2JiomJjYxUMBjtxauDG+CUmokZBQYEqKio0f/58HThwQMePH9euXbv085//XJ999pkk6fHHH9e2bdt07NgxHTp0SFu2bFFGRobuuusuSVL//v21b98+nTx5UoFAQI2NjV35kBDlCDiixpAhQ7R//35duHBBU6ZMUVZWlhYtWqSLFy8qKSlJkmSM0ZIlS3Tfffdp3Lhxqqur05tvvhnaY1+1apXOnz+vQYMGKS0tTSdPnuzKh4Qo5+ATeQDATuyBA4ClCDgAWIqAA4ClCDgAWIqAA4ClOv2NPKdOnersu+wSqampCgQCXT0GWoj1sk80rZnH42l2O3vgAGApAg4AliLgAGApAg4AliLgAGApAg4AliLgAGApAg4AliLgAGApaz5SLbhoRleP0CqVXT1AKzhf/HtXjwAgAuyBA4ClCDgAWIqAA4ClCDgAWIqAA4ClCDgAWIqAA4ClCDgAWCrsG3kuX76swsJCNTQ0KBgMasyYMZo7d678fr/Wrl2r2tpaZWZm6oknnpDLZc37ggDAemGL261bNxUWFiouLk4NDQ1auXKlhg8frtdff13Tpk3Td7/7Xf3xj3/Uzp07NXny5M6YGQCgFhxCcTgciouLkyQFg0EFg0E5HA4dOnRIY8aMkSSNHz9eZWVlHTspAKCJFh3zaGxs1JNPPqkzZ85oypQp6tWrl+Lj4+V0OiVJbrdbVVVVHTooAKCpFgU8JiZGa9asUV1dnZ599lmdOnWqxXfg8/nk8/kkSUVFRUpNTY1oUJv+OJRtIl2T24nL5eJ5sAxr1sq/RnjnnXfq3nvv1ZEjR/T1118rGAzK6XSqqqpKbre72et4vV55vd7Q6UAg0LaJ0e5Yk6s/xHge7BJNa+bxeJrdHvYY+FdffaW6ujpJV1+RcvDgQaWnp+vee+/Vu+++K0navXu3Ro4c2Y7jAgDCCbsHXl1drdLSUjU2NsoYowcffFD333+/+vTpo7Vr1+rll19W//79NWHChM6YFwDw/xzGGNOZd9ia4+fXsu0DHWzCBzpE13/HbxfRtGYRH0IBANyaCDgAWIqAA4ClCDgAWIqAA4ClCDgAWIqAA4ClCDgAWIqAA4ClCDgAWIqAA4ClCDgAWIqAA4ClCDgAWIqAA4ClCDgAWIqAA4ClCDgAWIqAA4ClCDgAWIqAA4ClCDgAWIqAA4ClCDgAWMoV7gKBQEClpaU6f/68HA6HvF6vpk6dqs2bN+vtt99WYmKiJGnevHkaMWJEhw8MALgqbMCdTqdycnKUmZmpixcvasWKFRo2bJgkadq0aZoxY0aHDwkAuF7YgCcnJys5OVmS1L17d6Wnp6uqqqrDBwMA3FzYgF/L7/fr+PHjGjhwoD7++GPt2LFDe/fuVWZmphYsWKCEhITrruPz+eTz+SRJRUVFSk1NjWjQyoiuhZaIdE1uJy6Xi+fBMqyZ5DDGmJZcsL6+XoWFhXrkkUc0evRonT9/PnT8+5VXXlF1dbXy8vLC3s6pU6ciGjS4iEM1HcX54t+7eoQul5qaqkAg0NVjoBWiac08Hk+z21v0KpSGhgYVFxdr7NixGj16tCQpKSlJMTExiomJ0cSJE3Xs2LH2mxYAEFbYgBtjtH79eqWnp2v69Omh7dXV1aGvDxw4oIyMjI6ZEADQrLDHwD/55BPt3btXffv2VX5+vqSrLxnct2+fTpw4IYfDobS0ND322GMdPiwA4BthAz548GBt3rz5uu285hsAuhbvxAQASxFwALAUAQcASxFwALAUAQcASxFwALAUAQcASxFwALAUAQcASxFwALAUAQcASxFwALAUAQcASxFwALAUAQcASxFwALAUAQcASxFwALAUAQcASxFwALAUAQcASxFwALAUAQcAS7nCXSAQCKi0tFTnz5+Xw+GQ1+vV1KlTdeHCBZWUlOjs2bNKS0vT0qVLlZCQ0BkzAwDUgoA7nU7l5OQoMzNTFy9e1IoVKzRs2DDt3r1bQ4cO1cyZM7V161Zt3bpV8+fP74yZAQBqwSGU5ORkZWZmSpK6d++u9PR0VVVVqaysTNnZ2ZKk7OxslZWVdeykAIAmwu6BX8vv9+v48eMaOHCgampqlJycLElKSkpSTU1Ns9fx+Xzy+XySpKKiIqWmpkY0aGVE10JLRLomtxOXy8XzYBnWrBUBr6+vV3FxsX74wx8qPj6+yXkOh0MOh6PZ63m9Xnm93tDpQCAQ4ajoKKzJ1R9iPA92iaY183g8zW5v0atQGhoaVFxcrLFjx2r06NGSpB49eqi6ulqSVF1drcTExHYaFQDQEmEDbozR+vXrlZ6erunTp4e2jxw5Unv27JEk7dmzR6NGjeq4KQEA1wl7COWTTz7R3r171bdvX+Xn50uS5s2bp5kzZ6qkpEQ7d+4MvYwQANB5wgZ88ODB2rx5c7PnrVy5st0HAgC0DO/EBABLEXAAsBQBBwBLEXAAsBQBBwBLEXAAsBQBBwBLEXAAsBQBBwBLEXAAsBQBBwBLEXAAsBQBBwBLEXAAsBQBBwBLEXAAsBQBBwBLEXAAsBQBBwBLEXAAsBQBBwBLEXAAsBQBBwBLEXAAsJQr3AXWrVun8vJy9ejRQ8XFxZKkzZs36+2331ZiYqIkad68eRoxYkTHTgoAaCJswMePH6+HH35YpaWlTbZPmzZNM2bM6LDBAAA3F/YQSlZWlhISEjpjFgBAK4TdA7+RHTt2aO/evcrMzNSCBQtuGHmfzyefzydJKioqUmpqakT3VxnpoAgr0jW5nbhcLp4Hy7BmEQZ88uTJ+sEPfiBJeuWVV/TSSy8pLy+v2ct6vV55vd7Q6UAgEMldogOxJld/iPE82CWa1szj8TS7PaJXoSQlJSkmJkYxMTGaOHGijh071qbhAACtF1HAq6urQ18fOHBAGRkZ7TYQAKBlwh5CWbt2rQ4fPqza2lrl5uZq7ty5OnTokE6cOCGHw6G0tDQ99thjnTErAOAaYQO+ZMmS67ZNmDChQ4YBALQc78QEAEsRcACwFAEHAEsRcACwFAEHAEsRcACwFAEHAEsRcACwFAEHAEsRcACwFAEHAEsRcACwFAEHAEsRcACwFAEHAEsRcACwFAEHAEsRcACwFAEHAEsRcACwFAEHAEsRcACwFAEHAEu5wl1g3bp1Ki8vV48ePVRcXCxJunDhgkpKSnT27FmlpaVp6dKlSkhI6PBhAQDfCLsHPn78eBUUFDTZtnXrVg0dOlTPP/+8hg4dqq1bt3bYgACA5oUNeFZW1nV712VlZcrOzpYkZWdnq6ysrGOmAwDcUNhDKM2pqalRcnKyJCkpKUk1NTU3vKzP55PP55MkFRUVKTU1NZK7VGVE10JLRLomtxOXy8XzYBnWLMKAX8vhcMjhcNzwfK/XK6/XGzodCATaepdoZ6zJ1R9iPA92iaY183g8zW6P6FUoPXr0UHV1tSSpurpaiYmJkU8GAIhIRAEfOXKk9uzZI0nas2ePRo0a1a5DAQDCC3sIZe3atTp8+LBqa2uVm5uruXPnaubMmSopKdHOnTtDLyMEAHSusAFfsmRJs9tXrlzZ7sMAAFqOd2ICgKUIOABYioADgKUIOABYioADgKUIOABYioADgKUIOABYioADgKUIOABYioADgKUIOABYioADgKUIOABYioADgKUIOABYioADgKUIOABYioADgKUIOABYKuyHGgORCi6a0dUjtFhlVw/QSs4X/97VI+AWwB44AFiKgAOApdp0CGXx4sWKi4tTTEyMnE6nioqK2msuAEAYbT4GXlhYqMTExPaYBQDQChxCAQBLtXkPfPXq1ZKkSZMmyev1Xne+z+eTz+eTJBUVFSk1NTWi+7HtVQI2iXRNwmHNOk5HrZlNXC5X1D8PbQr4b37zG7ndbtXU1Oipp56Sx+NRVlZWk8t4vd4mYQ8EAm25S3QA1sQ+rNnVH2LR8jx4PJ5mt7fpEIrb7ZYk9ejRQ6NGjdLRo0fbcnMAgFaIOOD19fW6ePFi6OuDBw+qb9++7TYYAODmIj6EUlNTo2effVaSFAwG9dBDD2n48OHtNhgA4OYiDnivXr20Zs2a9pwFANAKvIwQACxFwAHAUgQcACxFwAHAUgQcACxFwAHAUgQcACzFR6oBCOFj8DpOR3wMHnvgAGApAg4AliLgAGApAg4AliLgAGApAg4AliLgAGApAg4AliLgAGApAg4AliLgAGApAg4AliLgAGApAg4AliLgAGApAg4AlmrTBzp88MEH+vOf/6zGxkZNnDhRM2fObK+5AABhRLwH3tjYqA0bNqigoEAlJSXat2+fvvzyy/acDQBwExEH/OjRo+rdu7d69eoll8ul73znOyorK2vP2QAANxHxIZSqqiqlpKSETqekpOjTTz+97nI+n08+n0+SVFRUJI/HE9kdvvFeZNdD12HN7MOaWaXDf4np9XpVVFSkoqKijr6rW8qKFSu6egS0AutlH9asDQF3u906d+5c6PS5c+fkdrvbZSgAQHgRB3zAgAE6ffq0/H6/GhoatH//fo0cObI9ZwMA3ETEx8CdTqd+9KMfafXq1WpsbNT3vvc9ZWRktOdsVvN6vV09AlqB9bIPayY5jDGmq4cAALQe78QEAEsRcACwFAFvxhtvvKFLly612+0tXrxYX331lSTpl7/8ZbvdLsJbv3596B3CW7ZsCW2vq6vTjh07QqerqqpUXFzc6fPhen6/X8uXL2/x5f/3+zUnJ6cjxrolEfBmbNu2rV0Dfq2nnnqqQ24XzcvNzVWfPn0kSX/9619D2+vq6vTWW2+FTrvd7lZFA7eOjvx+vdW16Y9Z3Q7q6+tVUlKiqqoqNTY2asyYMaqqqtKqVauUmJiowsJCvfjiizp27JguX76sMWPGaO7cuZKu7llnZ2fr/fffV0NDg5YtW6b09HTV1tbqueeeU1VVle655x5d+3vinJwcbdy4UYcOHdKrr76qu+66S1988YUyMzP1xBNPyOFwqLy8XC+99JLuuOMODRo0SH6/nzcttMD/ruXs2bP11ltvKScnR++++64uX76s/Px8ZWRkqLGxUWfOnFF+fr6GDRumKVOm6JlnnlFxcbF2796t9957T5cuXVJlZaUeeOABzZ8/X5K0c+dO/e1vf1N8fLy+9a1vqVu3bnr00Ue7+JHffoLBoJ5//nkdP35cffr00eOPP64jR45o48aNCgaDGjBggBYtWqR//vOf132/StJf/vIXlZeXKzY2Vvn5+UpKSuriR9RBTJT717/+ZV544YXQ6bq6OpOXl2dqampC22pra40xxgSDQVNYWGhOnDhhjDEmLy/PbNu2zRhjzPbt20O3s2HDBvPqq68aY4x5//33zZw5c0K3N3/+fGOMMR999JFZsGCBCQQCJhgMmoKCAlNRUWEuXbpkcnNzTWVlpTHGmJKSEvP000935FNw22huLQsLC83Ro0eNMd8898YYU1lZaZYtW9bs6V27dpnFixeburo6c+nSJfPTn/7UnD171pw7d87k5eWZ2tpac+XKFfOrX/3K/OlPf+qkRxc9KisrzZw5c0xFRYUxxpjS0lLz2muvmdzcXPPf//7XGGPMH/7wB/P6668bY8x1369z5swxZWVlxhhjNm7caF577bVOfgSdJ+oPofTt21cffvihNm3apIqKCsXHx193mf379+vJJ5/UL37xC33xxRdN/uri6NGjJUmZmZk6e/asJKmiokJjx46VJI0YMUJ33nlns/c9cOBApaSkKCYmRv369ZPf79epU6fUs2dP9ezZU5L00EMPtevjvZ21ZC1b6r777lN8fLxiY2PVp08fBQIBHT16VEOGDFFCQoJcLpfGjBnTjtPjWikpKRo8eLAkady4cfroo4/Us2fP0N9Sys7OVkVFRbPXdblcuv/++yU1/b68HUX9IRSPx6NnnnlG5eXlevnllzV06NAm5/v9fv3jH//Q008/rYSEBJWWlurKlSuh812uq09hTEyMgsFgq+67W7duoa9jYmLU2NjYhkeCcGvZGv+7Nq1dW7SNw+Focjo+Pl4XLlxo0XWdTmfo+rf72kX9HnhVVZViY2M1btw4zZgxQ5999pni4uJUX18vSfr6668VFxen+Ph4nT9/Xh988EHY2xwyZIjeeecdSdJ//vMf1dXVtXgej8cjv98vv98v6ereP1qmubW8lsvlUkNDgySpe/fuunjxYqtuf+DAgaqoqNCFCxcUDAb173//u91mR1OBQEBHjhyRJL3zzjsaMGCA/H6/zpw5I0nau3evsrKyJKnJ92u0ifo98JMnT2rTpk1yOBxyuVz68Y9/rCNHjmj16tVyu90qLCxUv379tHTpUqWkpGjQoEFhb3POnDl67rnntGzZMt1zzz1KTU1t8TyxsbF69NFH9dvf/lZ33HGHBgwY0JaHF1WaW8uNGzeGzp84caLy8/PVv39//exnP9OgQYO0fPlyDR8+XFOmTAl7+263W7NmzVJBQYESEhLk8XjadJgGN+bxeLR9+3a98MILSk9P18KFC/Xtb39bv//970O/xJw0aZKkq2+pv/b7NZrwVvpbUH19veLi4mSM0YYNG9S7d29Nnz69q8eCvlmbYDCoNWvWaMKECXrggQe6eixEqajfA78V+Xw+7dmzRw0NDerfv39oTwNdb/Pmzfrwww915coVDRs2TKNGjerqkRDF2AMHAEtF/S8xAcBWBBwALEXAAcBSBBwALEXAAcBS/weZ/wyKXVn/TwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n",
        "!ls \"/content/drive/My Drive/cattle\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BqimSt1wE098",
        "outputId": "eaace329-27eb-4d28-94c2-ca00eacd1b9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n",
            "data  data.rar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unrar x -r -o- \"/content/drive/My Drive/cattle/cattle.rar\" '/content/drive/My Drive/cattle/cattle/'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3EhMedsa0gn",
        "outputId": "f8cad28e-035e-48d4-d435-db06f2fd5ab6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "UNRAR 5.50 freeware      Copyright (c) 1993-2017 Alexander Roshal\n",
            "\n",
            "\n",
            "Extracting from /content/drive/My Drive/cattle/cattle.rar\n",
            "\n",
            "Creating    /content/drive/My Drive/cattle/cattle                     OK\n",
            "Creating    /content/drive/My Drive/cattle/cattle/data                OK\n",
            "Creating    /content/drive/My Drive/cattle/cattle/data/train          OK\n",
            "Creating    /content/drive/My Drive/cattle/cattle/data/train/images   OK\n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0000f2101250b009_jpg.rf.650930ad1ba122dd906e10184d24d011.jpg     \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0004fa3aa5bdb9ff_jpg.rf.594a07a17d4048a11f785f1f94757e1f.jpg     \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0006dbac092b5a91_jpg.rf.854326f490f8c0d91d1eb22638262fce.jpg     \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0007844dce093b4b_jpg.rf.0652d6658fa1013f7781f18c632741e5.jpg     \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00126a640cabc9c1_jpg.rf.36c8072847cbffe43d7c838e147ac8ba.jpg     \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00179fe9498b4f7b_jpg.rf.598585fdc7bcd545091aa7a5aedcbb34.jpg     \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/002017b9df1b68b6_jpg.rf.a222e28ad942a40d137b378184d45c4f.jpg     \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00281a008932b401_jpg.rf.cc85c2f590667471597ccfccae447df4.jpg     \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/002f7a227bbfafcd_jpg.rf.7713b05811b486632dc91e57c0e3b0a5.jpg     \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00342fe8d91c28a0_jpg.rf.6a3d7069ac68ad6c5c89a139a276d9f8.jpg     \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/003cf69a1dc78926_jpg.rf.88aca6878a9d1af8953953642addae64.jpg     \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00445abbdb8f72e7_jpg.rf.a253dab64d58c98a1c269ab3062d4985.jpg     \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0055d46c79cb30d2_jpg.rf.bc9a400e3eab27af895c193c1c1df353.jpg     \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/005b7b6b0b8ad54f_jpg.rf.17dd84a96ba946ac0d65bf0f80dfc412.jpg     \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0067402849a0fef2_jpg.rf.59b65784658856f2a13b166146464310.jpg     \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/007011484992db13_jpg.rf.f13655a442ff1f6f084a6ff0a5534f44.jpg     \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00748204b61c5399_jpg.rf.b65a8562d27eeb3f8137e2cb0602a7fb.jpg     \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0075f0b2f3f81f07_jpg.rf.51235e8230a13f040e1b1b4bf1d823ea.jpg     \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00766891b879c48b_jpg.rf.4e6fa0df85534672cd63a139f43effe5.jpg     \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/007ea5c8a9206099_jpg.rf.abd01d144c7844d529c63c18b6c0459b.jpg     \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/008091b93bf91004_jpg.rf.fa639f74bd5c8c1a0defb72dfbf8ac9f.jpg     \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0085ab4b08abd96a_jpg.rf.50b1d91a9f6f6b134fcf1214e7df4071.jpg     \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0092a699d0c030cf_jpg.rf.fea9ea7e0f276c1b9abe078741457f5c.jpg     \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00963ebe1f3676ac_jpg.rf.22388b2fd2015cd273fb08e37d3a5d91.jpg     \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/009ba8a902c87e80_jpg.rf.1da80b1298aad92232707f63dbb4c789.jpg     \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00bb291e0085bd78_jpg.rf.7506670160c2bd3d2485ea8dc00ec56b.jpg     \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00cc8943fb0f569d_jpg.rf.544494a7ec69c080431033922c66ea42.jpg     \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00cea4a90d6bc53b_jpg.rf.b6e047706b36c9664f3c9e9f08e30f8e.jpg     \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00d74e5ae46ff980_jpg.rf.1c0e4f717bba15ab003af392293e51d6.jpg     \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00d8a67bf777e47e_jpg.rf.c7ebcced1403649944543d08a0816180.jpg     \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00dad1a78369049d_jpg.rf.a3cabed5fa293c3e87f3467a63c61fa8.jpg     \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00dcf5fbf02233be_jpg.rf.812b17bca5bb2b6206d0fc52f06d2646.jpg     \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00ddfb38f49c18bf_jpg.rf.64e0b8dd9ce602aefb8a1dc0737ac782.jpg     \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00e6ccb5516d6bdf_jpg.rf.19cd499d1355802495b59508418753ff.jpg     \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00e6da6411e1e8ce_jpg.rf.a719cb2d6e9f73b671c7e4f596c5332b.jpg     \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00eea1bf8574f93d_jpg.rf.0d2be4d7eae129655bc76d9e3d5c0f57.jpg     \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00f14e10ea680e59_jpg.rf.25fe56dea8f6e015e1bb998c65faf6d6.jpg     \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/00f3b968e7ab5846_jpg.rf.eb0dbd4725aebde81e3d0d94ec9438f3.jpg     \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/010394523a36d0e3_jpg.rf.9daadf7d8fa67a37df2bb5a3715baf60.jpg     \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01082d44cf1902af_jpg.rf.a7151aab209c9415f3c3b622ce9f6f0d.jpg     \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/010be4f42f14b195_jpg.rf.42b218fb3807c00bb8553355e3c2afd1.jpg     \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01156535f73e8d67_jpg.rf.14c0e1d048ca70ae3c94a15d4375f822.jpg     \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0117424210ac5da0_jpg.rf.6988768cf1ddc70e4f1e88f1f5f37382.jpg     \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/011a4caaff0ced0e_jpg.rf.5bbceb7b9ba50f0f8e51bd4b1903e4dc.jpg     \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0124004ba43c4fba_jpg.rf.a5e947f2f2002deb97044cba51861fb2.jpg     \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0125d9e468447468_jpg.rf.f21ef5a5dfc41c79aec30783362b9bfe.jpg     \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0126cf8dfbabcbaf_jpg.rf.1a8a992e3a45d4b490816f120b725603.jpg     \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/012a0b9d970e0016_jpg.rf.891c7a33c778c741150b8f0bec35203d.jpg     \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/013ca824f09e56fb_jpg.rf.b1115b64b61a35051809da8be631b798.jpg     \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/014ac46d15798c10_jpg.rf.56be31a06955db8de2129778e3049a57.jpg     \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/014e985830f4e1ef_jpg.rf.60ae5645335e497dcdb1b47049067010.jpg     \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0152d996c465a675_jpg.rf.4b9e3c5a7d0a44f730032717a5efb41b.jpg     \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0158db74ea94fb3c_jpg.rf.ab10e233dcaf6f0fd5060220ea3e64cf.jpg     \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0174ff09c518b8d8_jpg.rf.e82168b1bd9888803fc2a300ffb84157.jpg     \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/017795e4594eb410_jpg.rf.028c819c2c496780229315c4fe57470c.jpg     \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/018050afe92fadab_jpg.rf.b363ea6800d30eef6b4f2f9e76a15997.jpg     \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0184f96682c0002e_jpg.rf.e2a61346b3db572b657351e24e21c91c.jpg     \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01881dc13c8c2c08_jpg.rf.5341fd705e3bbbc5a003a2f409b3fc0f.jpg     \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/018e6ed697d81faf_jpg.rf.77e55e31a42461ea3384321da8cfd7aa.jpg     \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/018f64e94e5a3dd8_jpg.rf.6709936433293450cdc286a4192c2295.jpg     \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01954759026d4f2c_jpg.rf.28ee3a21acfe3e526af432b87830bc48.jpg     \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01a9a7ec2c73e4ca_jpg.rf.8fa68c9a7185a158f3cb9f608bc40e68.jpg     \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01ab841e5251dba6_jpg.rf.ecb669e8f0fa57d7c81de73013a4857c.jpg     \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01b4aaf606aeb154_jpg.rf.a47150e0842a8152e1cce152f68bd5ea.jpg     \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01c0c40635ee5d86_jpg.rf.b7df6695b09744ac6e6a3713af6ffaf9.jpg     \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01dcc10008a7b526_jpg.rf.5190080bf3fb5eabdb1bf5fa3dcfae5f.jpg     \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01e399b0996ae318_jpg.rf.027fdabc881db12ba6c5666f28705257.jpg     \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01e4c234dda8d576_jpg.rf.130aa2785129df0ce36ad64e88808f98.jpg     \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01e65e498d96a9ec_jpg.rf.3b421ce9fd6fbd12836c3107b4cecf57.jpg     \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01e901b091cbeed5_jpg.rf.32736fbcce0f8cf40ab7a5e8329e5343.jpg     \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01f9a64f559000c9_jpg.rf.71b992e1676abce446ec81142e4a1962.jpg     \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01fc81cee842a131_jpg.rf.7da1867c8413871b558bf54cb9620c99.jpg     \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/01fe16e2a9310a3f_jpg.rf.f1fa6a25c770fce6c791068ee6cee721.jpg     \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02016f8ec0aba195_jpg.rf.b8932fcdc645b1da917b76f3222d63fb.jpg     \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0202c1dd9744773c_jpg.rf.12dbb6522bb575fa1a2d4fd8f5e10f2b.jpg     \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/020b4f59c43ba7d6_jpg.rf.64be7f134557f6c63f1501236328654a.jpg     \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/020c31d74f95fec2_jpg.rf.fbfff6b92fc502b0f6f1df30af1a7a1e.jpg     \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/021298e59c1b0ba9_jpg.rf.0f9c6d405fa9227074cfc6e730e7c846.jpg     \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/021f3be2182f8200_jpg.rf.022e966595daf4e2f6815d0df03a52c2.jpg     \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0224d1733e539060_jpg.rf.6edea69674ef0bc730e4424683952a89.jpg     \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0225f657e823c28d_jpg.rf.cceab0de104ae52b3389f07ca5d2adda.jpg     \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/024617353d9f9abb_jpg.rf.d7d87a38439e09a9023fd5bca4dac8f7.jpg     \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/024e75d0c45616a8_jpg.rf.cae0ec689380b94885316388ca6eac24.jpg     \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0259b4c747001062_jpg.rf.5c6dc6710419b68eb59310493f0360ef.jpg     \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/025be6787e0864cd_jpg.rf.409e2074eb248d45c184bea1c3987f27.jpg     \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02609f463c097334_jpg.rf.f4392b69827ada5b2154888d275e5484.jpg     \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02657be5b84db329_jpg.rf.c1ae86ecfcfb719a79abfcb6ad902a0c.jpg     \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02669aa65fc62de2_jpg.rf.9af45bcf7e133566758825ff3a0660fa.jpg     \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0266cd030c3bbbea_jpg.rf.97ade20184ab6e0be58f3c475d77a5e8.jpg     \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02694101af5c8fc7_jpg.rf.34d3d145d36f9bcab14d1d9bf5d52878.jpg     \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0272aba3ea8e658c_jpg.rf.feec38e65c6e6f2bdb7feebaa320ba94.jpg     \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0282a01261c10c3a_jpg.rf.13926b70d4f635e99568c19afe94b44e.jpg     \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0286c0a7243b6136_jpg.rf.5d7f35b8e0dd710eb32058633fc685bb.jpg     \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0286ffb2d969895d_jpg.rf.f537ede1157a04dd0ad23774c31a5bd9.jpg     \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/028ae0fe97d2eaf6_jpg.rf.c1aa21884d4716e945a0df666bc7f477.jpg     \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02934cc304ee2be1_jpg.rf.320e3917509e2250994f97dfda9e7cd0.jpg     \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/029a6fc7956b93c8_jpg.rf.b69a7d8edb8971a4e461421db18febc8.jpg     \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/029eccf9c91a9e39_jpg.rf.1d1058c37a3c3b695ef2a09739700be3.jpg     \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02a9acabce0f3add_jpg.rf.8595eff5731e183a233bc0d67bb6b202.jpg     \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02aa7d44b844d1f3_jpg.rf.09ae51f30f743f08cfa7e6e6f39bd917.jpg     \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02b12fe295225ba1_jpg.rf.220c71c7ce36c943cdff7c2c2f70b406.jpg     \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02b461da7337e435_jpg.rf.c6f16061a682a5a42980a471ae1b9060.jpg     \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02b47ee3d7af889a_jpg.rf.3068885c58fa97c290e1a736b509c4ff.jpg     \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02b4f2417500f210_jpg.rf.58c130f260189916d6d0581950de6cea.jpg     \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02b7cfb14b778e40_jpg.rf.9390a6f01927fdb9d9d91320b99b6e11.jpg     \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02c91a8515336047_jpg.rf.a3773a6665e28d70e64ee75d62d47e0b.jpg     \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02cbdd17ef77937a_jpg.rf.e4d89b0fa40ad9cdc4137a72b0989800.jpg     \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02d55e130df5d7ef_jpg.rf.7289a1fabdb879b030504bf6cb6cbce1.jpg     \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02da8f72ec8424db_jpg.rf.1b075cfd7f43c62b61c7490896b34384.jpg     \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02e9f9e072351317_jpg.rf.fdf4f71dee2bb7bf5f3e4ecc77ef6d63.jpg     \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02ec1a93800e881d_jpg.rf.de76c5fcf46165bf837c1bc6c4f5a274.jpg     \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/02ff8f90c494b923_jpg.rf.7603be5febd35fcad7dbaca0e598242e.jpg     \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0316f20904702d07_jpg.rf.bdccc09d3d02f7fb0fde467d248976ed.jpg     \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/031f1226a7c8ac83_jpg.rf.4faa5ae7766eed2e3e84ae8986e539d9.jpg     \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0328e1c01b517c90_jpg.rf.815bbad92ba59d395a71a7632721a9b0.jpg     \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0329b3d9db175f23_jpg.rf.1fd93cf59dca3f518c34ae6b7aa414b7.jpg     \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/032b52902b4bce2b_jpg.rf.d440ea8e8c850c722be37fdfd9350056.jpg     \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/032e70b001224998_jpg.rf.4da6eeb61aad0e84580b8d14ab74f3f1.jpg     \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0334636951c3bc4a_jpg.rf.04226205c483857db255fc2f1fb9bd05.jpg     \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/033bc25d5f672d9e_jpg.rf.ed4f347e960c971fc1ece3af9fa20fdb.jpg     \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/034ba3ab9005da81_jpg.rf.e6f989d6f163ddf16ca41801d1274cf1.jpg     \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/034f6d9ef90f1a6a_jpg.rf.7c785d3e4ea78dc681052a969b24963f.jpg     \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/03857db187dbd72b_jpg.rf.d1b205a65a578871ed89629c381aebf5.jpg     \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/038c45d12cf97dcd_jpg.rf.4cb28c31bcf2d5fc5a0b84daa8b0df76.jpg     \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/039030db9c7526c3_jpg.rf.be4f87a9d5e6ec48908dc2eab6addfc0.jpg     \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/03a241046025b7f9_jpg.rf.16e88d880882275e327d4826eb75f9de.jpg     \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/03ae0c1be5b03367_jpg.rf.9ef171e803e1a1aff35a6d3fe9b7ad34.jpg     \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/03b70baef8a9e672_jpg.rf.cc79b5fe9e5a3120862fc9499dd6fffc.jpg     \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/03bc80cfb3e09a72_jpg.rf.1231ba89092d71bb51bad05c949a3697.jpg     \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/03d2eddbe527fa87_jpg.rf.556b82d21f86edd36b214b1f27bf4d20.jpg     \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/03d63bf4bfe6373d_jpg.rf.911f873165d804350c224f81d48b5ce1.jpg     \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/03e8433322b19951_jpg.rf.57c1f627b9982071c98dc3a39baec351.jpg     \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/03e8c11a310fcce0_jpg.rf.3e1fe4b91c9bbb41244be65b111bb813.jpg     \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/03eed2f8b0501744_jpg.rf.62ed8054e84b75dc985742274eae624a.jpg     \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04071852274d18c2_jpg.rf.d434e4ad92c2d9fd6b4c11d212c03f76.jpg     \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0409b81ee3e62cfc_jpg.rf.a8e07b24ec99c46adf4cde52e0326e92.jpg     \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04241e7862e5466c_jpg.rf.82783b8676bca1f335de63d78d38e0d5.jpg     \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04293eb9f6b1e8ff_jpg.rf.d67a998fea27fad8b517c03266095a52.jpg     \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0429cc635a9b467f_jpg.rf.add4c80dc923677622feeac8ee3a1cc0.jpg     \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/042a28c8055ea2a2_jpg.rf.4434b84ec6df376de08aa54e24a3fb48.jpg     \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0433bd022aea658a_jpg.rf.d1997a23c1305fa3f40eec9eec25fa81.jpg     \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/043aa217f212fb86_jpg.rf.19c42cec8da0b36624794a89ae25487e.jpg     \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/043cf2457529de3d_jpg.rf.ed21b098048ea09e5fff7b0ddde5021a.jpg     \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0450b75f51d3ceb7_jpg.rf.56c623bdb4def203e53999c87751903d.jpg     \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/045345306618fc16_jpg.rf.973b10dfbe4574d462007e1877980e99.jpg     \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/045914960ccfff83_jpg.rf.e66577d08360bb59ef741686da938c4a.jpg     \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/045d44934407a4d9_jpg.rf.9c96911d7caf066768cae831a37970c5.jpg     \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0475a6ced72b172e_jpg.rf.4b05b4c2dc7afbe2f09068770df41a38.jpg     \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0480c43f614ede1a_jpg.rf.782478f2261dd2d9864c373afe111c4f.jpg     \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04820fe88594c600_jpg.rf.0ce7f06b00a7cdc9d6135b57040076d8.jpg     \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/048ed84aa78fe0ef_jpg.rf.ea1a00b913fdeca82951d742dd91727b.jpg     \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/048f28c16d4a7407_jpg.rf.158f84587e90ccfd9a6ffd2691901c38.jpg     \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0490c5c867d12adf_jpg.rf.c5068c9041af496fa1e74c452f86c1e2.jpg     \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04976cf98c24d1c6_jpg.rf.527a3c2e57522f83458a1c6394ef7f25.jpg     \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/049c3470b12d52fd_jpg.rf.138c947531d8d1d3b67097e585ef73a4.jpg     \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04a0a2bebabc9039_jpg.rf.5b1862d92dd9f65ec940945375605b67.jpg     \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04a5f0eecdc8f9dd_jpg.rf.610f7a7aceaabb7498117cd6c04bd9c9.jpg     \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04a7e41cbdd32093_jpg.rf.7615f0081c0deb8038b0154a88217b8e.jpg     \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04ad97efeee1f3fe_jpg.rf.09f0885e516bd1c76bc0587951e4a34d.jpg     \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04af7c23d369f6ee_jpg.rf.00ef2fca07dfe758917e4a8dbf1e7214.jpg     \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04b0c434a778ce67_jpg.rf.d183ec89189b6626e15630936b256811.jpg     \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04b2407286191060_jpg.rf.c186a8d6d91abd21eb1a0712b0624c91.jpg     \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04b4119afea16a52_jpg.rf.ee91ed01e8beb1ee8177caef0101302e.jpg     \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04c9f1fd3e2320bb_jpg.rf.f9bd84820d3fa72197d4a0f12232cb49.jpg     \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04caf7bf237dea5d_jpg.rf.32c8038269a425ced89efa41dce086e2.jpg     \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04d4d9fc6c8ba256_jpg.rf.a587fda94a5225498a16f36007200bea.jpg     \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04d9963dbe8f8b09_jpg.rf.f0c97546b4de69fb114f2c6eb6e6d585.jpg     \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04e083ec5f1b8cf7_jpg.rf.6150a6e46986bbae6da7e47018d793f6.jpg     \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04e5cfad6b86f162_jpg.rf.da905a22bb65af64d0681657fa06fa77.jpg     \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04ebb163baf58309_jpg.rf.163bdd82e84ed0a861bab6e2506c0ff1.jpg     \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04f0ecffc14cbe47_jpg.rf.e90e24a6a68d39eb03e45bd40e019592.jpg     \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04f791224cf9029a_jpg.rf.b8eaa0b3b1d4b61f1fabe02a90ba6ec8.jpg     \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/04fc27d5a1205d68_jpg.rf.218675ada02a69068e124aca9e790f0b.jpg     \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/05030a6c82f0b5ae_jpg.rf.e65da21ac9d4d28ee664a95aa1282c21.jpg     \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/050ad76d99bac759_jpg.rf.9c18695372e6f04426d21de265398bd5.jpg     \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0511688543463373_jpg.rf.8a18b3363b9f6341172818e066d61276.jpg     \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/053066361d5ef84c_jpg.rf.0c7e8579bdedac3f932153e8ef7287d3.jpg     \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/053b0a0ec9d7173a_jpg.rf.1950d3a266862bd97a2688d4f546560e.jpg     \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0555513563d9c791_jpg.rf.3a071ac916d0e0cde4c66bc49022e922.jpg     \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/055cc437cc845485_jpg.rf.0154c089692031718be82a21807d45dc.jpg     \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/055e90fd517e8c69_jpg.rf.4883a789da26b4ea78e8193f0e989837.jpg     \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0562e28dc3890a90_jpg.rf.f4a3fcc70f161ec6731d82e62ce67c6e.jpg     \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0564978d311c147a_jpg.rf.e199e88322a91537132407f54ac8d709.jpg     \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0566a4b5c6e2faf6_jpg.rf.7f2fee4b1a94f6315f648f70d54dc655.jpg     \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/057cfe2f7e98d282_jpg.rf.acf32b72f50863f89692144838b58540.jpg     \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/058763446d7aa7ac_jpg.rf.62bb39ad59213f126bf22c742bac6918.jpg     \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0593ef4ed215a040_jpg.rf.eca09bda92fee1cb9febb759f84316e7.jpg     \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/059aefc0da21e534_jpg.rf.bfcb7015b3af6fff6d498747af99a723.jpg     \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/05a5e46a872b3650_jpg.rf.6b6b127da4c3f61232b693e698befd05.jpg     \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/05b453368642ca1c_jpg.rf.8a0043d756c622e5ecb14582107544d2.jpg     \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/05c06faedbf36b46_jpg.rf.33593fd182bdc51830be4152282be263.jpg     \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/05c17088f97f47c7_jpg.rf.c70c1ded1b3e4cf6f83c1c938f319958.jpg     \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/05cb9ae2ea56810e_jpg.rf.b2f6740de9c5a4ee676725ec909f045e.jpg     \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/05cbdf5df45ae1b2_jpg.rf.c8db2e70ad89f33c7839c2a4b61aaf06.jpg     \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/05d1af1355d52892_jpg.rf.771db593c5ef50d30fc4925075875879.jpg     \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/05f459a9d3cfa5cc_jpg.rf.eda9bc55f698f6385892f4f8f25ab878.jpg     \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/05f7e9995475ed9b_jpg.rf.aaaead7e56f9e1d47da4f9daa436c3a6.jpg     \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/060b7ff7ca6f486d_jpg.rf.b911d2810ce522c3c0c5873f2252e2b8.jpg     \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/060b9fcd3abbcd80_jpg.rf.9ccf3c2733b14ad3732a8dd4e0c4c674.jpg     \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/062a4265ba8f36c6_jpg.rf.2cdf350ace6bce896ac2b5d3dc7febca.jpg     \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/063d180bf07ce1fa_jpg.rf.edf3049596ea08bd6fee6cec11649dd8.jpg     \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/06771b588add32aa_jpg.rf.ff1ef5cb4a612e5f8f94e5b6c6324bfe.jpg     \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/067be62699a5c910_jpg.rf.a248521098c50d5d3dc6a25ce6710f1a.jpg     \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/068155d18db57efe_jpg.rf.60f195347f96f436ff3ae3a8190306b1.jpg     \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/068ea1b9ee1a9827_jpg.rf.181c3bccd2374f1b3f6def75f2373508.jpg     \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/069b2a513eeb0672_jpg.rf.ea9bcc297d8c4ec0ae78c9444134d257.jpg     \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/069ec0283e558c3a_jpg.rf.3d00876135344f6181a708ff6bd1e68f.jpg     \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/069ec3b2a7f05d60_jpg.rf.b523d83a57a694bbc1b4fabed030decd.jpg     \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/06a2933b592f83fd_jpg.rf.01c3cfd25a81a39b4ea10987f4527e3b.jpg     \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/06b098bca5ffc74f_jpg.rf.d48a58e65d85fb6f0bc8d3c87dfb4678.jpg     \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/06b601d59f94b94a_jpg.rf.3f02e2edc207d5d51e1925ecf46eca6b.jpg     \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/06bc7a2561b4b78d_jpg.rf.b444685d7cf6b6e447beff473c46a112.jpg     \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/06cb47201e600368_jpg.rf.8b3980d479b0d6d4192a432557804cb2.jpg     \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/06cf286a3c20b8fa_jpg.rf.5db3905bf95fa2eb82f500d2c0257422.jpg     \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/06d3f992f49cbd81_jpg.rf.7c526862c9153d11c46c3c65e06e9f94.jpg     \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/06dfc6aa8581ab5e_jpg.rf.1bd0a3285e64f86c1bb75864ace0992a.jpg     \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/06e6e0049ad17de0_jpg.rf.9a2329eb1946927248ccc4027c6c8e81.jpg     \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/06f4e6d2fd724c62_jpg.rf.b31f2860bc049e435c10c80901bfcb67.jpg     \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/06fde57a5fb5fdb7_jpg.rf.6df740ba51056e7fd6038e53eecdc3d3.jpg     \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/07144c53eae76828_jpg.rf.05024accefbc236de7d00f367d100a89.jpg     \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0721982744a976f4_jpg.rf.91721dbf2d245bbf2e2ed7f7a450b8ea.jpg     \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0747de78c44c3e54_jpg.rf.d8e63dd4be42940ef35d46c110874be8.jpg     \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/075e0435a4e9f6ea_jpg.rf.133a5fdad66b7282b5eb67a356f04366.jpg     \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0773970cbb6348dd_jpg.rf.a9843fccbe3188ad055d9f0e5a3801c7.jpg     \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0777aeed8876183b_jpg.rf.e4ebb16e95f859812145ab95de112540.jpg     \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/077ae37ffbf3ab13_jpg.rf.c96ace3e578da4f28594f694ed6be86a.jpg     \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/07a255ff417d56d4_jpg.rf.7712fbe35eeb42293854f1cafd0306b5.jpg     \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/07b2986bd890136f_jpg.rf.f027ef3c85fe1e8aac5b9191dddd155a.jpg     \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/07bdf6ea44b4c33d_jpg.rf.a5624c436edb32f4568a15b9a90f800f.jpg     \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/07c129bf45c99060_jpg.rf.697630d628f8a6e4cadc4b82e51697e7.jpg     \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/07cb71579c076032_jpg.rf.b82edac4ab22e1982eff07143e492101.jpg     \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/07d39a68ad2a9fd3_jpg.rf.d3ef7e4cc482326f3266fec4ef0fa46d.jpg     \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/07d699697cd8b682_jpg.rf.95d1e99c54407511cdcd80c20ae145b0.jpg     \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/07ee905f4ad6dc24_jpg.rf.8a00fa67c9fd9baf6743f9ec984a4c99.jpg     \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/07ef06f566dedfc4_jpg.rf.ffe47e9c0c27a39569ec0ee2f0150acd.jpg     \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/07efa8e7a289bf4a_jpg.rf.9443fc5255d594f5ffed6f1134f74a89.jpg     \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/07f1fe9ce97c8fac_jpg.rf.57af4db58cf36d8a04964488298db296.jpg     \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/081a56b58b166981_jpg.rf.5c57c76c16a8847eadb431694a46aef8.jpg     \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/081cfbbf163edda4_jpg.rf.45d39450b18e93089cf1c60caebd2dad.jpg     \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0822196685d9b701_jpg.rf.13a139803b038c43283602625bdeb800.jpg     \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0833166de17ea0a7_jpg.rf.0ba0908ec1544fdcab95ac97aefe0fc5.jpg     \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/084227cb213d0699_jpg.rf.3e89724ee438edad1850951673f745b4.jpg     \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/084723607f56c2c7_jpg.rf.d13e171455f344dd6f9a143d5486cefe.jpg     \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/084f148f504b9fe0_jpg.rf.a7692c478cc7a8e84987f1087fb71f8f.jpg     \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/images/0855b3672c4e6cc4_jpg.rf.f6910091ad9bb4d46274594d196862ae.jpg     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Creating    /content/drive/My Drive/cattle/cattle/data/train/labels   OK\n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0000f2101250b009_jpg.rf.650930ad1ba122dd906e10184d24d011.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0004fa3aa5bdb9ff_jpg.rf.594a07a17d4048a11f785f1f94757e1f.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0006dbac092b5a91_jpg.rf.854326f490f8c0d91d1eb22638262fce.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0007844dce093b4b_jpg.rf.0652d6658fa1013f7781f18c632741e5.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00126a640cabc9c1_jpg.rf.36c8072847cbffe43d7c838e147ac8ba.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00179fe9498b4f7b_jpg.rf.598585fdc7bcd545091aa7a5aedcbb34.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/002017b9df1b68b6_jpg.rf.a222e28ad942a40d137b378184d45c4f.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00281a008932b401_jpg.rf.cc85c2f590667471597ccfccae447df4.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/002f7a227bbfafcd_jpg.rf.7713b05811b486632dc91e57c0e3b0a5.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00342fe8d91c28a0_jpg.rf.6a3d7069ac68ad6c5c89a139a276d9f8.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/003cf69a1dc78926_jpg.rf.88aca6878a9d1af8953953642addae64.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00445abbdb8f72e7_jpg.rf.a253dab64d58c98a1c269ab3062d4985.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0055d46c79cb30d2_jpg.rf.bc9a400e3eab27af895c193c1c1df353.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/005b7b6b0b8ad54f_jpg.rf.17dd84a96ba946ac0d65bf0f80dfc412.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0067402849a0fef2_jpg.rf.59b65784658856f2a13b166146464310.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/007011484992db13_jpg.rf.f13655a442ff1f6f084a6ff0a5534f44.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00748204b61c5399_jpg.rf.b65a8562d27eeb3f8137e2cb0602a7fb.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0075f0b2f3f81f07_jpg.rf.51235e8230a13f040e1b1b4bf1d823ea.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00766891b879c48b_jpg.rf.4e6fa0df85534672cd63a139f43effe5.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/007ea5c8a9206099_jpg.rf.abd01d144c7844d529c63c18b6c0459b.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/008091b93bf91004_jpg.rf.fa639f74bd5c8c1a0defb72dfbf8ac9f.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0085ab4b08abd96a_jpg.rf.50b1d91a9f6f6b134fcf1214e7df4071.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0092a699d0c030cf_jpg.rf.fea9ea7e0f276c1b9abe078741457f5c.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00963ebe1f3676ac_jpg.rf.22388b2fd2015cd273fb08e37d3a5d91.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/009ba8a902c87e80_jpg.rf.1da80b1298aad92232707f63dbb4c789.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00bb291e0085bd78_jpg.rf.7506670160c2bd3d2485ea8dc00ec56b.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00cc8943fb0f569d_jpg.rf.544494a7ec69c080431033922c66ea42.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00cea4a90d6bc53b_jpg.rf.b6e047706b36c9664f3c9e9f08e30f8e.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00d74e5ae46ff980_jpg.rf.1c0e4f717bba15ab003af392293e51d6.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00d8a67bf777e47e_jpg.rf.c7ebcced1403649944543d08a0816180.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00dad1a78369049d_jpg.rf.a3cabed5fa293c3e87f3467a63c61fa8.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00dcf5fbf02233be_jpg.rf.812b17bca5bb2b6206d0fc52f06d2646.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00ddfb38f49c18bf_jpg.rf.64e0b8dd9ce602aefb8a1dc0737ac782.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00e6ccb5516d6bdf_jpg.rf.19cd499d1355802495b59508418753ff.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00e6da6411e1e8ce_jpg.rf.a719cb2d6e9f73b671c7e4f596c5332b.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00eea1bf8574f93d_jpg.rf.0d2be4d7eae129655bc76d9e3d5c0f57.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00f14e10ea680e59_jpg.rf.25fe56dea8f6e015e1bb998c65faf6d6.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/00f3b968e7ab5846_jpg.rf.eb0dbd4725aebde81e3d0d94ec9438f3.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/010394523a36d0e3_jpg.rf.9daadf7d8fa67a37df2bb5a3715baf60.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01082d44cf1902af_jpg.rf.a7151aab209c9415f3c3b622ce9f6f0d.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/010be4f42f14b195_jpg.rf.42b218fb3807c00bb8553355e3c2afd1.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01156535f73e8d67_jpg.rf.14c0e1d048ca70ae3c94a15d4375f822.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0117424210ac5da0_jpg.rf.6988768cf1ddc70e4f1e88f1f5f37382.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/011a4caaff0ced0e_jpg.rf.5bbceb7b9ba50f0f8e51bd4b1903e4dc.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0124004ba43c4fba_jpg.rf.a5e947f2f2002deb97044cba51861fb2.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0125d9e468447468_jpg.rf.f21ef5a5dfc41c79aec30783362b9bfe.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0126cf8dfbabcbaf_jpg.rf.1a8a992e3a45d4b490816f120b725603.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/012a0b9d970e0016_jpg.rf.891c7a33c778c741150b8f0bec35203d.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/013ca824f09e56fb_jpg.rf.b1115b64b61a35051809da8be631b798.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/014ac46d15798c10_jpg.rf.56be31a06955db8de2129778e3049a57.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/014e985830f4e1ef_jpg.rf.60ae5645335e497dcdb1b47049067010.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0152d996c465a675_jpg.rf.4b9e3c5a7d0a44f730032717a5efb41b.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0158db74ea94fb3c_jpg.rf.ab10e233dcaf6f0fd5060220ea3e64cf.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0174ff09c518b8d8_jpg.rf.e82168b1bd9888803fc2a300ffb84157.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/017795e4594eb410_jpg.rf.028c819c2c496780229315c4fe57470c.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/018050afe92fadab_jpg.rf.b363ea6800d30eef6b4f2f9e76a15997.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0184f96682c0002e_jpg.rf.e2a61346b3db572b657351e24e21c91c.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01881dc13c8c2c08_jpg.rf.5341fd705e3bbbc5a003a2f409b3fc0f.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/018e6ed697d81faf_jpg.rf.77e55e31a42461ea3384321da8cfd7aa.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/018f64e94e5a3dd8_jpg.rf.6709936433293450cdc286a4192c2295.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01954759026d4f2c_jpg.rf.28ee3a21acfe3e526af432b87830bc48.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01a9a7ec2c73e4ca_jpg.rf.8fa68c9a7185a158f3cb9f608bc40e68.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01ab841e5251dba6_jpg.rf.ecb669e8f0fa57d7c81de73013a4857c.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01b4aaf606aeb154_jpg.rf.a47150e0842a8152e1cce152f68bd5ea.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01c0c40635ee5d86_jpg.rf.b7df6695b09744ac6e6a3713af6ffaf9.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01dcc10008a7b526_jpg.rf.5190080bf3fb5eabdb1bf5fa3dcfae5f.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01e399b0996ae318_jpg.rf.027fdabc881db12ba6c5666f28705257.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01e4c234dda8d576_jpg.rf.130aa2785129df0ce36ad64e88808f98.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01e65e498d96a9ec_jpg.rf.3b421ce9fd6fbd12836c3107b4cecf57.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01e901b091cbeed5_jpg.rf.32736fbcce0f8cf40ab7a5e8329e5343.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01f9a64f559000c9_jpg.rf.71b992e1676abce446ec81142e4a1962.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01fc81cee842a131_jpg.rf.7da1867c8413871b558bf54cb9620c99.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/01fe16e2a9310a3f_jpg.rf.f1fa6a25c770fce6c791068ee6cee721.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02016f8ec0aba195_jpg.rf.b8932fcdc645b1da917b76f3222d63fb.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0202c1dd9744773c_jpg.rf.12dbb6522bb575fa1a2d4fd8f5e10f2b.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/020b4f59c43ba7d6_jpg.rf.64be7f134557f6c63f1501236328654a.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/020c31d74f95fec2_jpg.rf.fbfff6b92fc502b0f6f1df30af1a7a1e.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/021298e59c1b0ba9_jpg.rf.0f9c6d405fa9227074cfc6e730e7c846.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/021f3be2182f8200_jpg.rf.022e966595daf4e2f6815d0df03a52c2.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0224d1733e539060_jpg.rf.6edea69674ef0bc730e4424683952a89.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0225f657e823c28d_jpg.rf.cceab0de104ae52b3389f07ca5d2adda.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/024617353d9f9abb_jpg.rf.d7d87a38439e09a9023fd5bca4dac8f7.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/024e75d0c45616a8_jpg.rf.cae0ec689380b94885316388ca6eac24.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0259b4c747001062_jpg.rf.5c6dc6710419b68eb59310493f0360ef.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/025be6787e0864cd_jpg.rf.409e2074eb248d45c184bea1c3987f27.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02609f463c097334_jpg.rf.f4392b69827ada5b2154888d275e5484.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02657be5b84db329_jpg.rf.c1ae86ecfcfb719a79abfcb6ad902a0c.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02669aa65fc62de2_jpg.rf.9af45bcf7e133566758825ff3a0660fa.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0266cd030c3bbbea_jpg.rf.97ade20184ab6e0be58f3c475d77a5e8.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02694101af5c8fc7_jpg.rf.34d3d145d36f9bcab14d1d9bf5d52878.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0272aba3ea8e658c_jpg.rf.feec38e65c6e6f2bdb7feebaa320ba94.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0282a01261c10c3a_jpg.rf.13926b70d4f635e99568c19afe94b44e.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0286c0a7243b6136_jpg.rf.5d7f35b8e0dd710eb32058633fc685bb.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0286ffb2d969895d_jpg.rf.f537ede1157a04dd0ad23774c31a5bd9.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/028ae0fe97d2eaf6_jpg.rf.c1aa21884d4716e945a0df666bc7f477.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02934cc304ee2be1_jpg.rf.320e3917509e2250994f97dfda9e7cd0.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/029a6fc7956b93c8_jpg.rf.b69a7d8edb8971a4e461421db18febc8.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/029eccf9c91a9e39_jpg.rf.1d1058c37a3c3b695ef2a09739700be3.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02a9acabce0f3add_jpg.rf.8595eff5731e183a233bc0d67bb6b202.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02aa7d44b844d1f3_jpg.rf.09ae51f30f743f08cfa7e6e6f39bd917.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02b12fe295225ba1_jpg.rf.220c71c7ce36c943cdff7c2c2f70b406.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02b461da7337e435_jpg.rf.c6f16061a682a5a42980a471ae1b9060.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02b47ee3d7af889a_jpg.rf.3068885c58fa97c290e1a736b509c4ff.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02b4f2417500f210_jpg.rf.58c130f260189916d6d0581950de6cea.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02b7cfb14b778e40_jpg.rf.9390a6f01927fdb9d9d91320b99b6e11.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02c91a8515336047_jpg.rf.a3773a6665e28d70e64ee75d62d47e0b.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02cbdd17ef77937a_jpg.rf.e4d89b0fa40ad9cdc4137a72b0989800.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02d55e130df5d7ef_jpg.rf.7289a1fabdb879b030504bf6cb6cbce1.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02da8f72ec8424db_jpg.rf.1b075cfd7f43c62b61c7490896b34384.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02e9f9e072351317_jpg.rf.fdf4f71dee2bb7bf5f3e4ecc77ef6d63.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02ec1a93800e881d_jpg.rf.de76c5fcf46165bf837c1bc6c4f5a274.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/02ff8f90c494b923_jpg.rf.7603be5febd35fcad7dbaca0e598242e.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0316f20904702d07_jpg.rf.bdccc09d3d02f7fb0fde467d248976ed.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/031f1226a7c8ac83_jpg.rf.4faa5ae7766eed2e3e84ae8986e539d9.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0328e1c01b517c90_jpg.rf.815bbad92ba59d395a71a7632721a9b0.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0329b3d9db175f23_jpg.rf.1fd93cf59dca3f518c34ae6b7aa414b7.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/032b52902b4bce2b_jpg.rf.d440ea8e8c850c722be37fdfd9350056.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/032e70b001224998_jpg.rf.4da6eeb61aad0e84580b8d14ab74f3f1.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0334636951c3bc4a_jpg.rf.04226205c483857db255fc2f1fb9bd05.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/033bc25d5f672d9e_jpg.rf.ed4f347e960c971fc1ece3af9fa20fdb.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/034ba3ab9005da81_jpg.rf.e6f989d6f163ddf16ca41801d1274cf1.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/034f6d9ef90f1a6a_jpg.rf.7c785d3e4ea78dc681052a969b24963f.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/03857db187dbd72b_jpg.rf.d1b205a65a578871ed89629c381aebf5.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/038c45d12cf97dcd_jpg.rf.4cb28c31bcf2d5fc5a0b84daa8b0df76.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/039030db9c7526c3_jpg.rf.be4f87a9d5e6ec48908dc2eab6addfc0.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/03a241046025b7f9_jpg.rf.16e88d880882275e327d4826eb75f9de.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/03ae0c1be5b03367_jpg.rf.9ef171e803e1a1aff35a6d3fe9b7ad34.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/03b70baef8a9e672_jpg.rf.cc79b5fe9e5a3120862fc9499dd6fffc.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/03bc80cfb3e09a72_jpg.rf.1231ba89092d71bb51bad05c949a3697.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/03d2eddbe527fa87_jpg.rf.556b82d21f86edd36b214b1f27bf4d20.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/03d63bf4bfe6373d_jpg.rf.911f873165d804350c224f81d48b5ce1.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/03e8433322b19951_jpg.rf.57c1f627b9982071c98dc3a39baec351.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/03e8c11a310fcce0_jpg.rf.3e1fe4b91c9bbb41244be65b111bb813.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/03eed2f8b0501744_jpg.rf.62ed8054e84b75dc985742274eae624a.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04071852274d18c2_jpg.rf.d434e4ad92c2d9fd6b4c11d212c03f76.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0409b81ee3e62cfc_jpg.rf.a8e07b24ec99c46adf4cde52e0326e92.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04241e7862e5466c_jpg.rf.82783b8676bca1f335de63d78d38e0d5.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04293eb9f6b1e8ff_jpg.rf.d67a998fea27fad8b517c03266095a52.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0429cc635a9b467f_jpg.rf.add4c80dc923677622feeac8ee3a1cc0.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/042a28c8055ea2a2_jpg.rf.4434b84ec6df376de08aa54e24a3fb48.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0433bd022aea658a_jpg.rf.d1997a23c1305fa3f40eec9eec25fa81.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/043aa217f212fb86_jpg.rf.19c42cec8da0b36624794a89ae25487e.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/043cf2457529de3d_jpg.rf.ed21b098048ea09e5fff7b0ddde5021a.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0450b75f51d3ceb7_jpg.rf.56c623bdb4def203e53999c87751903d.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/045345306618fc16_jpg.rf.973b10dfbe4574d462007e1877980e99.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/045914960ccfff83_jpg.rf.e66577d08360bb59ef741686da938c4a.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/045d44934407a4d9_jpg.rf.9c96911d7caf066768cae831a37970c5.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0475a6ced72b172e_jpg.rf.4b05b4c2dc7afbe2f09068770df41a38.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0480c43f614ede1a_jpg.rf.782478f2261dd2d9864c373afe111c4f.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04820fe88594c600_jpg.rf.0ce7f06b00a7cdc9d6135b57040076d8.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/048ed84aa78fe0ef_jpg.rf.ea1a00b913fdeca82951d742dd91727b.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/048f28c16d4a7407_jpg.rf.158f84587e90ccfd9a6ffd2691901c38.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0490c5c867d12adf_jpg.rf.c5068c9041af496fa1e74c452f86c1e2.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04976cf98c24d1c6_jpg.rf.527a3c2e57522f83458a1c6394ef7f25.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/049c3470b12d52fd_jpg.rf.138c947531d8d1d3b67097e585ef73a4.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04a0a2bebabc9039_jpg.rf.5b1862d92dd9f65ec940945375605b67.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04a5f0eecdc8f9dd_jpg.rf.610f7a7aceaabb7498117cd6c04bd9c9.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04a7e41cbdd32093_jpg.rf.7615f0081c0deb8038b0154a88217b8e.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04ad97efeee1f3fe_jpg.rf.09f0885e516bd1c76bc0587951e4a34d.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04af7c23d369f6ee_jpg.rf.00ef2fca07dfe758917e4a8dbf1e7214.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04b0c434a778ce67_jpg.rf.d183ec89189b6626e15630936b256811.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04b2407286191060_jpg.rf.c186a8d6d91abd21eb1a0712b0624c91.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04b4119afea16a52_jpg.rf.ee91ed01e8beb1ee8177caef0101302e.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04c9f1fd3e2320bb_jpg.rf.f9bd84820d3fa72197d4a0f12232cb49.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04caf7bf237dea5d_jpg.rf.32c8038269a425ced89efa41dce086e2.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04d4d9fc6c8ba256_jpg.rf.a587fda94a5225498a16f36007200bea.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04d9963dbe8f8b09_jpg.rf.f0c97546b4de69fb114f2c6eb6e6d585.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04e083ec5f1b8cf7_jpg.rf.6150a6e46986bbae6da7e47018d793f6.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04e5cfad6b86f162_jpg.rf.da905a22bb65af64d0681657fa06fa77.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04ebb163baf58309_jpg.rf.163bdd82e84ed0a861bab6e2506c0ff1.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04f0ecffc14cbe47_jpg.rf.e90e24a6a68d39eb03e45bd40e019592.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04f791224cf9029a_jpg.rf.b8eaa0b3b1d4b61f1fabe02a90ba6ec8.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/04fc27d5a1205d68_jpg.rf.218675ada02a69068e124aca9e790f0b.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/05030a6c82f0b5ae_jpg.rf.e65da21ac9d4d28ee664a95aa1282c21.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/050ad76d99bac759_jpg.rf.9c18695372e6f04426d21de265398bd5.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0511688543463373_jpg.rf.8a18b3363b9f6341172818e066d61276.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/053066361d5ef84c_jpg.rf.0c7e8579bdedac3f932153e8ef7287d3.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/053b0a0ec9d7173a_jpg.rf.1950d3a266862bd97a2688d4f546560e.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0555513563d9c791_jpg.rf.3a071ac916d0e0cde4c66bc49022e922.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/055cc437cc845485_jpg.rf.0154c089692031718be82a21807d45dc.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/055e90fd517e8c69_jpg.rf.4883a789da26b4ea78e8193f0e989837.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0562e28dc3890a90_jpg.rf.f4a3fcc70f161ec6731d82e62ce67c6e.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0564978d311c147a_jpg.rf.e199e88322a91537132407f54ac8d709.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0566a4b5c6e2faf6_jpg.rf.7f2fee4b1a94f6315f648f70d54dc655.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/057cfe2f7e98d282_jpg.rf.acf32b72f50863f89692144838b58540.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/058763446d7aa7ac_jpg.rf.62bb39ad59213f126bf22c742bac6918.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0593ef4ed215a040_jpg.rf.eca09bda92fee1cb9febb759f84316e7.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/059aefc0da21e534_jpg.rf.bfcb7015b3af6fff6d498747af99a723.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/05a5e46a872b3650_jpg.rf.6b6b127da4c3f61232b693e698befd05.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/05b453368642ca1c_jpg.rf.8a0043d756c622e5ecb14582107544d2.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/05c06faedbf36b46_jpg.rf.33593fd182bdc51830be4152282be263.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/05c17088f97f47c7_jpg.rf.c70c1ded1b3e4cf6f83c1c938f319958.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/05cb9ae2ea56810e_jpg.rf.b2f6740de9c5a4ee676725ec909f045e.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/05cbdf5df45ae1b2_jpg.rf.c8db2e70ad89f33c7839c2a4b61aaf06.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/05d1af1355d52892_jpg.rf.771db593c5ef50d30fc4925075875879.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/05f459a9d3cfa5cc_jpg.rf.eda9bc55f698f6385892f4f8f25ab878.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/05f7e9995475ed9b_jpg.rf.aaaead7e56f9e1d47da4f9daa436c3a6.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/060b7ff7ca6f486d_jpg.rf.b911d2810ce522c3c0c5873f2252e2b8.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/060b9fcd3abbcd80_jpg.rf.9ccf3c2733b14ad3732a8dd4e0c4c674.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/062a4265ba8f36c6_jpg.rf.2cdf350ace6bce896ac2b5d3dc7febca.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/063d180bf07ce1fa_jpg.rf.edf3049596ea08bd6fee6cec11649dd8.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/06771b588add32aa_jpg.rf.ff1ef5cb4a612e5f8f94e5b6c6324bfe.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/067be62699a5c910_jpg.rf.a248521098c50d5d3dc6a25ce6710f1a.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/068155d18db57efe_jpg.rf.60f195347f96f436ff3ae3a8190306b1.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/068ea1b9ee1a9827_jpg.rf.181c3bccd2374f1b3f6def75f2373508.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/069b2a513eeb0672_jpg.rf.ea9bcc297d8c4ec0ae78c9444134d257.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/069ec0283e558c3a_jpg.rf.3d00876135344f6181a708ff6bd1e68f.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/069ec3b2a7f05d60_jpg.rf.b523d83a57a694bbc1b4fabed030decd.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/06a2933b592f83fd_jpg.rf.01c3cfd25a81a39b4ea10987f4527e3b.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/06b098bca5ffc74f_jpg.rf.d48a58e65d85fb6f0bc8d3c87dfb4678.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/06b601d59f94b94a_jpg.rf.3f02e2edc207d5d51e1925ecf46eca6b.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/06bc7a2561b4b78d_jpg.rf.b444685d7cf6b6e447beff473c46a112.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/06cb47201e600368_jpg.rf.8b3980d479b0d6d4192a432557804cb2.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/06cf286a3c20b8fa_jpg.rf.5db3905bf95fa2eb82f500d2c0257422.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/06d3f992f49cbd81_jpg.rf.7c526862c9153d11c46c3c65e06e9f94.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/06dfc6aa8581ab5e_jpg.rf.1bd0a3285e64f86c1bb75864ace0992a.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/06e6e0049ad17de0_jpg.rf.9a2329eb1946927248ccc4027c6c8e81.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/06f4e6d2fd724c62_jpg.rf.b31f2860bc049e435c10c80901bfcb67.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/06fde57a5fb5fdb7_jpg.rf.6df740ba51056e7fd6038e53eecdc3d3.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/07144c53eae76828_jpg.rf.05024accefbc236de7d00f367d100a89.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0721982744a976f4_jpg.rf.91721dbf2d245bbf2e2ed7f7a450b8ea.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0747de78c44c3e54_jpg.rf.d8e63dd4be42940ef35d46c110874be8.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/075e0435a4e9f6ea_jpg.rf.133a5fdad66b7282b5eb67a356f04366.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0773970cbb6348dd_jpg.rf.a9843fccbe3188ad055d9f0e5a3801c7.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0777aeed8876183b_jpg.rf.e4ebb16e95f859812145ab95de112540.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/077ae37ffbf3ab13_jpg.rf.c96ace3e578da4f28594f694ed6be86a.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/07a255ff417d56d4_jpg.rf.7712fbe35eeb42293854f1cafd0306b5.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/07b2986bd890136f_jpg.rf.f027ef3c85fe1e8aac5b9191dddd155a.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/07bdf6ea44b4c33d_jpg.rf.a5624c436edb32f4568a15b9a90f800f.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/07c129bf45c99060_jpg.rf.697630d628f8a6e4cadc4b82e51697e7.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/07cb71579c076032_jpg.rf.b82edac4ab22e1982eff07143e492101.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/07d39a68ad2a9fd3_jpg.rf.d3ef7e4cc482326f3266fec4ef0fa46d.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/07d699697cd8b682_jpg.rf.95d1e99c54407511cdcd80c20ae145b0.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/07ee905f4ad6dc24_jpg.rf.8a00fa67c9fd9baf6743f9ec984a4c99.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/07ef06f566dedfc4_jpg.rf.ffe47e9c0c27a39569ec0ee2f0150acd.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/07efa8e7a289bf4a_jpg.rf.9443fc5255d594f5ffed6f1134f74a89.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/07f1fe9ce97c8fac_jpg.rf.57af4db58cf36d8a04964488298db296.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/081a56b58b166981_jpg.rf.5c57c76c16a8847eadb431694a46aef8.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/081cfbbf163edda4_jpg.rf.45d39450b18e93089cf1c60caebd2dad.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0822196685d9b701_jpg.rf.13a139803b038c43283602625bdeb800.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0833166de17ea0a7_jpg.rf.0ba0908ec1544fdcab95ac97aefe0fc5.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/084227cb213d0699_jpg.rf.3e89724ee438edad1850951673f745b4.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/084723607f56c2c7_jpg.rf.d13e171455f344dd6f9a143d5486cefe.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/084f148f504b9fe0_jpg.rf.a7692c478cc7a8e84987f1087fb71f8f.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/train/labels/0855b3672c4e6cc4_jpg.rf.f6910091ad9bb4d46274594d196862ae.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Creating    /content/drive/My Drive/cattle/cattle/data/valid          OK\n",
            "Creating    /content/drive/My Drive/cattle/cattle/data/valid/images   OK\n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0035ea973a26443e_jpg.rf.c16372c13bb47ba651d83d531e4c00ab.jpg     \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/006022ce83084ebc_jpg.rf.95353ee28dde0d28192f5cf954ed183d.jpg     \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/006a11b61561170d_jpg.rf.b823ab8a93f1f786ec572b324a2f8751.jpg     \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/00d84d869e53dcc4_jpg.rf.71f129f426fb0e9664fba8515c0c8dc5.jpg     \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/010c63cfa7fc9694_jpg.rf.50825009b65a98a7fae98758a2d24d00.jpg     \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/011f61d3f70fc503_jpg.rf.ac041405b3b06e9fbcf61e29b4bd3844.jpg     \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/01525005358fce02_jpg.rf.1856e533435d600b304320fedd0b58a8.jpg     \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/01aedba1a0a76f20_jpg.rf.33fb128078123a91e760cff5c8f180b2.jpg     \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0264d0cd67f57e1c_jpg.rf.35e4f7af07b94706a87ec3a01813fdea.jpg     \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/026fd9ac9f553bd3_jpg.rf.4f323313f17fa95a38262c20ff2420f5.jpg     \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/02c3f36741307b7b_jpg.rf.7b960fad408b8125ac85747a3f713597.jpg     \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/03188dd92c454301_jpg.rf.05f605c08b205d3d9ea036f67d720a18.jpg     \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/031db55dd2420683_jpg.rf.51b9381b995b5d465f400be01ffdccdd.jpg     \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/03e8b76f6d605630_jpg.rf.b22d959c4d0591dd36d479a82201f5e1.jpg     \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/04fe426c33044ddd_jpg.rf.013107b8f20f452b4ca762b1bcec5543.jpg     \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/050d0b0313895c5c_jpg.rf.b2278ff191e32d49abd276d969004e33.jpg     \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0518aa69eb54654b_jpg.rf.1131c0f0a0d7f89d1a8b955b93b95d51.jpg     \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0538b513ceccf779_jpg.rf.800a332529a3d5f481c78154d2c0fa23.jpg     \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0548535bdae3b558_jpg.rf.bcb66cd4a69db615122edea767da0aee.jpg     \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/059edc0c4474dc21_jpg.rf.dd0413813cc0021c3b414e47464e2901.jpg     \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0639c4fcdcfc9989_jpg.rf.fba36dd072a2749776d1774b513b2139.jpg     \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/06e30742052dc9e3_jpg.rf.2d5cac5af96298d0b581ee0d41c5b40b.jpg     \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0783a1bf4704f28c_jpg.rf.dc5ffb3f490237f80aac26b7634901eb.jpg     \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/08a1beac37884783_jpg.rf.c86b562eae4b1c4fc3147e0742191aaf.jpg     \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/08d0d353bd717cd2_jpg.rf.3786f1a5f49f8024fda88be50353c0f4.jpg     \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0991edda83c43bba_jpg.rf.95ec8efc620f274251eaa5dd4370c1b2.jpg     \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/09c063ba7192afa0_jpg.rf.268dd8f5268d40bbaad2f34a267a84ea.jpg     \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0a0f165d0d4b3c66_jpg.rf.e0b3f9f57fbeb6d6c405fa3aad4c17d9.jpg     \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0a59fef54530bb19_jpg.rf.6fdba876b2c45910fe602ad5e83332df.jpg     \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0afb99fc2ce00355_jpg.rf.aebd36ad3036d1f804b256f8aa44d425.jpg     \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0bb22bf977396515_jpg.rf.de67ef1576f692f0a0b9819cdefa04b7.jpg     \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0cbab40e1891ba91_jpg.rf.95ba476e0bfc6efead9def909cceac01.jpg     \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0e2602ca3a0d9e9e_jpg.rf.6d596440c27f160bc2c837fd36ffc84b.jpg     \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0fc56df96463e1f3_jpg.rf.0bc76f9d9cdb74b64e0d52bcbb5045a1.jpg     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/images/0fd913054835a391_jpg.rf.985fca6b426c3d6fc833ce20c60c38d2.jpg     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Creating    /content/drive/My Drive/cattle/cattle/data/valid/labels   OK\n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0035ea973a26443e_jpg.rf.c16372c13bb47ba651d83d531e4c00ab.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/006022ce83084ebc_jpg.rf.95353ee28dde0d28192f5cf954ed183d.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/006a11b61561170d_jpg.rf.b823ab8a93f1f786ec572b324a2f8751.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/00d84d869e53dcc4_jpg.rf.71f129f426fb0e9664fba8515c0c8dc5.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/010c63cfa7fc9694_jpg.rf.50825009b65a98a7fae98758a2d24d00.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/011f61d3f70fc503_jpg.rf.ac041405b3b06e9fbcf61e29b4bd3844.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/01525005358fce02_jpg.rf.1856e533435d600b304320fedd0b58a8.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/01aedba1a0a76f20_jpg.rf.33fb128078123a91e760cff5c8f180b2.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0264d0cd67f57e1c_jpg.rf.35e4f7af07b94706a87ec3a01813fdea.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/026fd9ac9f553bd3_jpg.rf.4f323313f17fa95a38262c20ff2420f5.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/02c3f36741307b7b_jpg.rf.7b960fad408b8125ac85747a3f713597.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/03188dd92c454301_jpg.rf.05f605c08b205d3d9ea036f67d720a18.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/031db55dd2420683_jpg.rf.51b9381b995b5d465f400be01ffdccdd.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/03e8b76f6d605630_jpg.rf.b22d959c4d0591dd36d479a82201f5e1.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/04fe426c33044ddd_jpg.rf.013107b8f20f452b4ca762b1bcec5543.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/050d0b0313895c5c_jpg.rf.b2278ff191e32d49abd276d969004e33.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0518aa69eb54654b_jpg.rf.1131c0f0a0d7f89d1a8b955b93b95d51.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0538b513ceccf779_jpg.rf.800a332529a3d5f481c78154d2c0fa23.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0548535bdae3b558_jpg.rf.bcb66cd4a69db615122edea767da0aee.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/059edc0c4474dc21_jpg.rf.dd0413813cc0021c3b414e47464e2901.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0639c4fcdcfc9989_jpg.rf.fba36dd072a2749776d1774b513b2139.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/06e30742052dc9e3_jpg.rf.2d5cac5af96298d0b581ee0d41c5b40b.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0783a1bf4704f28c_jpg.rf.dc5ffb3f490237f80aac26b7634901eb.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/08a1beac37884783_jpg.rf.c86b562eae4b1c4fc3147e0742191aaf.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/08d0d353bd717cd2_jpg.rf.3786f1a5f49f8024fda88be50353c0f4.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0991edda83c43bba_jpg.rf.95ec8efc620f274251eaa5dd4370c1b2.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/09c063ba7192afa0_jpg.rf.268dd8f5268d40bbaad2f34a267a84ea.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0a0f165d0d4b3c66_jpg.rf.e0b3f9f57fbeb6d6c405fa3aad4c17d9.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0a59fef54530bb19_jpg.rf.6fdba876b2c45910fe602ad5e83332df.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0afb99fc2ce00355_jpg.rf.aebd36ad3036d1f804b256f8aa44d425.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0bb22bf977396515_jpg.rf.de67ef1576f692f0a0b9819cdefa04b7.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0cbab40e1891ba91_jpg.rf.95ba476e0bfc6efead9def909cceac01.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0e2602ca3a0d9e9e_jpg.rf.6d596440c27f160bc2c837fd36ffc84b.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0fc56df96463e1f3_jpg.rf.0bc76f9d9cdb74b64e0d52bcbb5045a1.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/drive/My Drive/cattle/cattle/data/valid/labels/0fd913054835a391_jpg.rf.985fca6b426c3d6fc833ce20c60c38d2.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "All OK\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Preparing the dataset:"
      ],
      "metadata": {
        "id": "M9n8cH_fG4dV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.patches as patches\n",
        "import matplotlib.pyplot as plt\n",
        "from bs4 import BeautifulSoup\n",
        "from PIL import Image\n",
        "import torchvision\n",
        "from torchvision import transforms, datasets, models\n",
        "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n",
        "import time"
      ],
      "metadata": {
        "id": "twosA8H7pdc8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import fnmatch\n",
        "import glob\n",
        "\n",
        "def generate_target(dirpath): \n",
        "\n",
        "    #num_files = len(fnmatch.filter(os.listdir(dirpath), '*.txt'))\n",
        "    boxes = []\n",
        "    labels = []\n",
        "    for filename in glob.glob(dirpath + '*.txt'):\n",
        "        with open(os.path.join(os.getcwd(), filename), 'r') as f: # open in readonly mode  \n",
        "            lines = f.readlines()\n",
        "            for line in lines:\n",
        "                #print(line)\n",
        "                line = line.rstrip('\\n').split(' ')\n",
        "                labels.append(int(line[0]))\n",
        "                boxes.append([416*float(line[1]) - (416*float(line[3])/2), 416*float(line[2]) - (416*float(line[4])/2) , 416*float(line[1]) + (416*float(line[3])/2), 416*float(line[2]) + (416*float(line[4])/2)])\n",
        "\n",
        "    boxes = torch.as_tensor(boxes, dtype=torch.float32) \n",
        "    #print(boxes)\n",
        "    labels = torch.as_tensor(labels, dtype=torch.int64) \n",
        "    \n",
        "    target = {}\n",
        "    target[\"boxes\"] = boxes\n",
        "    target[\"labels\"] = labels\n",
        "    \n",
        "    return target\n",
        "\n",
        "def plot_image_from_output(img, annotation):\n",
        "    \n",
        "    img = img.cpu().permute(1,2,0)\n",
        "    \n",
        "    fig,ax = plt.subplots(1)\n",
        "    ax.imshow(img)\n",
        "    \n",
        "    for idx in range(len(annotation[\"boxes\"])):\n",
        "        xmin, ymin, xmax, ymax = annotation[\"boxes\"][idx]\n",
        "\n",
        "        if annotation['labels'][idx] == 0 :\n",
        "            rect = patches.Rectangle((xmin,ymin),(xmax-xmin),(ymax-ymin),linewidth=1,edgecolor='r',facecolor='none')\n",
        "        \n",
        "        elif annotation['labels'][idx] == 1 :\n",
        "            \n",
        "            rect = patches.Rectangle((xmin,ymin),(xmax-xmin),(ymax-ymin),linewidth=1,edgecolor='g',facecolor='none')\n",
        "\n",
        "        ax.add_patch(rect)\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "HiOjhXWfvGWP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MaskDataset(object):\n",
        "    def __init__(self, transforms, path):\n",
        "        '''\n",
        "        path: path to train folder or test folder\n",
        "        '''\n",
        "        # define the path to the images and what transform will be used\n",
        "        self.transforms = transforms\n",
        "        self.path = path\n",
        "        self.imgs = list(sorted(os.listdir(self.path)))\n",
        "\n",
        "\n",
        "    def __getitem__(self, idx): #special method\n",
        "        # load images ad masks\n",
        "        file_image = self.imgs[idx]\n",
        "        file_label = self.imgs[idx][:-3] + 'txt'\n",
        "        img_path = os.path.join(self.path, file_image)\n",
        "        \n",
        "        if 'train' in self.path:\n",
        "            label_path = \"/content/drive/My Drive/cattle/cattle/data/train/labels/\"\n",
        "        else:\n",
        "            label_path = \"/content/drive/My Drive/cattle/cattle/data/valid/labels/\"\n",
        "\n",
        "        img = Image.open(img_path).convert(\"RGB\")\n",
        "        #Generate Label\n",
        "        target = generate_target(label_path)\n",
        "        \n",
        "        if self.transforms is not None:\n",
        "            img = self.transforms(img)\n",
        "\n",
        "        return img, target\n",
        "\n",
        "    def __len__(self): \n",
        "        return len(self.imgs)\n",
        "\n",
        "data_transform = transforms.Compose([  # transforms.Compose : a class that calls the functions in a list consecutively\n",
        "        transforms.ToTensor() # ToTensor : convert numpy image to torch.Tensor type\n",
        "    ])\n",
        "\n",
        "def collate_fn(batch):\n",
        "    return tuple(zip(*batch))\n",
        "\n",
        "dataset = MaskDataset(data_transform, '/content/drive/My Drive/cattle/cattle/data/train/images/')\n",
        "test_dataset = MaskDataset(data_transform, '/content/drive/My Drive/cattle/cattle/data/valid/images/')\n",
        "\n",
        "print(len(dataset), len(test_dataset))\n",
        "\n",
        "data_loader = torch.utils.data.DataLoader(dataset, batch_size=4, collate_fn=collate_fn)\n",
        "test_data_loader = torch.utils.data.DataLoader(test_dataset, batch_size=4, collate_fn=collate_fn)"
      ],
      "metadata": {
        "id": "5iwagq0mvKxr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9494bb2-36d9-49b4-e319-3254f63242e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "245 35\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_model_instance_segmentation(num_classes):\n",
        "  \n",
        "    model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)\n",
        "    in_features = model.roi_heads.box_predictor.cls_score.in_features\n",
        "    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "regqw6LCvPG9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = 2 # standing vs. sitting\n",
        "\n",
        "model = get_model_instance_segmentation(num_classes)\n",
        "\n",
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') \n",
        "model.to(device)"
      ],
      "metadata": {
        "id": "r66mugyNvSVN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f595c355-e86a-44dd-b101-26455ebc2f31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FasterRCNN(\n",
              "  (transform): GeneralizedRCNNTransform(\n",
              "      Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
              "      Resize(min_size=(800,), max_size=1333, mode='bilinear')\n",
              "  )\n",
              "  (backbone): BackboneWithFPN(\n",
              "    (body): IntermediateLayerGetter(\n",
              "      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "      (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "      (layer1): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (layer2): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (3): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (layer3): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (3): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (4): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (5): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (layer4): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(2048, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d(2048, eps=0.0)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(2048, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(2048, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (fpn): FeaturePyramidNetwork(\n",
              "      (inner_blocks): ModuleList(\n",
              "        (0): Conv2dNormActivation(\n",
              "          (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (1): Conv2dNormActivation(\n",
              "          (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (2): Conv2dNormActivation(\n",
              "          (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (3): Conv2dNormActivation(\n",
              "          (0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (layer_blocks): ModuleList(\n",
              "        (0): Conv2dNormActivation(\n",
              "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        )\n",
              "        (1): Conv2dNormActivation(\n",
              "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        )\n",
              "        (2): Conv2dNormActivation(\n",
              "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        )\n",
              "        (3): Conv2dNormActivation(\n",
              "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (extra_blocks): LastLevelMaxPool()\n",
              "    )\n",
              "  )\n",
              "  (rpn): RegionProposalNetwork(\n",
              "    (anchor_generator): AnchorGenerator()\n",
              "    (head): RPNHead(\n",
              "      (conv): Sequential(\n",
              "        (0): Conv2dNormActivation(\n",
              "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (1): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
              "    )\n",
              "  )\n",
              "  (roi_heads): RoIHeads(\n",
              "    (box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)\n",
              "    (box_head): TwoMLPHead(\n",
              "      (fc6): Linear(in_features=12544, out_features=1024, bias=True)\n",
              "      (fc7): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "    )\n",
              "    (box_predictor): FastRCNNPredictor(\n",
              "      (cls_score): Linear(in_features=1024, out_features=2, bias=True)\n",
              "      (bbox_pred): Linear(in_features=1024, out_features=8, bias=True)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 10\n",
        "params = [p for p in model.parameters() if p.requires_grad]\n",
        "optimizer = torch.optim.SGD(params, lr=0.005,\n",
        "                                momentum=0.9, weight_decay=0.0005)"
      ],
      "metadata": {
        "id": "0DjQfkNbvX_w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('----------------------train start--------------------------')\n",
        "for epoch in range(num_epochs):\n",
        "    start = time.time()\n",
        "    model.train()\n",
        "    i = 0    \n",
        "    epoch_loss = 0\n",
        "    print(len(data_loader))\n",
        "    for imgs, annotations in data_loader:\n",
        "        i += 1\n",
        "        imgs = list(img.to(device) for img in imgs)\n",
        "        annotations = [{k: v.to(device) for k, v in t.items()} for t in annotations]\n",
        "        loss_dict = model(imgs, annotations) \n",
        "        losses = sum(loss for loss in loss_dict.values())        \n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        losses.backward()\n",
        "        optimizer.step() \n",
        "        epoch_loss += losses\n",
        "    print(f'epoch : {epoch+1}, Loss : {epoch_loss}, time : {time.time() - start}')"
      ],
      "metadata": {
        "id": "AKNwzE_Zvdbu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "423db08a-2b38-4428-8f0d-c5ea8d7c9468"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------train start--------------------------\n",
            "62\n",
            "epoch : 1, Loss : 112.79353332519531, time : 101.09458613395691\n",
            "62\n",
            "epoch : 2, Loss : 95.09561920166016, time : 98.92532181739807\n",
            "62\n",
            "epoch : 3, Loss : 90.12130737304688, time : 99.06679916381836\n",
            "62\n",
            "epoch : 4, Loss : 88.83211517333984, time : 99.1734688282013\n",
            "62\n",
            "epoch : 5, Loss : 86.60063171386719, time : 98.89120864868164\n",
            "62\n",
            "epoch : 6, Loss : 84.3432388305664, time : 99.02369594573975\n",
            "62\n",
            "epoch : 7, Loss : 83.64877319335938, time : 98.9253568649292\n",
            "62\n",
            "epoch : 8, Loss : 81.17373657226562, time : 98.61225461959839\n",
            "62\n",
            "epoch : 9, Loss : 80.22746276855469, time : 99.13045692443848\n",
            "62\n",
            "epoch : 10, Loss : 80.07015228271484, time : 98.92957067489624\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(),f'model_{num_epochs}.pt')"
      ],
      "metadata": {
        "id": "ycbGNklIvgux"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_state_dict(torch.load(f'model_{num_epochs}.pt'))"
      ],
      "metadata": {
        "id": "VZ5N0ggawDc1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17c4d7c4-1a1a-4905-a632-a0a85b927c72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def make_prediction(model, img, threshold):\n",
        "    model.eval()\n",
        "    preds = model(img)\n",
        "    for id in range(len(preds)) :\n",
        "        idx_list = []\n",
        "\n",
        "        for idx, score in enumerate(preds[id]['scores']) :\n",
        "            if score > threshold : \n",
        "                idx_list.append(idx)\n",
        "\n",
        "        preds[id]['boxes'] = preds[id]['boxes'][idx_list]\n",
        "        preds[id]['labels'] = preds[id]['labels'][idx_list]\n",
        "        preds[id]['scores'] = preds[id]['scores'][idx_list]\n",
        "\n",
        "    return preds"
      ],
      "metadata": {
        "id": "XuhwpvJnwG4a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad(): \n",
        "    # batch size of the test set = 4\n",
        "    for imgs, annotations in test_data_loader:\n",
        "        imgs = list(img.to(device) for img in imgs)\n",
        "\n",
        "        pred = make_prediction(model, imgs, 0.5)\n",
        "        print(pred)\n",
        "        break"
      ],
      "metadata": {
        "id": "StmSRRoIwL6f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c6aefe5-3838-4aec-e0b7-b015249bb7e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'boxes': tensor([[2.7713e+02, 1.4546e+02, 4.1307e+02, 4.1600e+02],\n",
            "        [3.1279e+02, 0.0000e+00, 4.1446e+02, 2.1813e+02],\n",
            "        [3.9679e+00, 6.0443e+01, 1.2719e+02, 4.1542e+02],\n",
            "        [3.3436e+02, 7.4648e+01, 4.1269e+02, 2.5947e+02],\n",
            "        [3.0728e+02, 2.0777e+02, 4.1369e+02, 3.6192e+02],\n",
            "        [2.1017e+00, 1.3964e+02, 8.7784e+01, 3.3709e+02],\n",
            "        [2.2282e+02, 0.0000e+00, 4.1044e+02, 1.8036e+02],\n",
            "        [1.9924e+02, 2.1506e+02, 2.9680e+02, 4.0821e+02],\n",
            "        [0.0000e+00, 7.7851e+00, 7.1905e+01, 2.5393e+02],\n",
            "        [2.3871e+02, 2.7959e+02, 4.0271e+02, 3.7437e+02],\n",
            "        [2.3282e+02, 2.1672e+02, 3.3017e+02, 4.0976e+02],\n",
            "        [2.6124e+02, 2.0344e+02, 3.6276e+02, 4.0381e+02],\n",
            "        [3.0492e+02, 1.6233e+02, 4.1551e+02, 3.0492e+02],\n",
            "        [0.0000e+00, 2.1975e+02, 1.1325e+02, 4.1600e+02],\n",
            "        [1.6642e+02, 2.1609e+02, 2.6385e+02, 4.0898e+02],\n",
            "        [4.4620e+01, 1.0681e+01, 2.0805e+02, 1.8617e+02],\n",
            "        [7.8492e+01, 1.6849e+02, 1.9403e+02, 3.8482e+02],\n",
            "        [2.0355e+02, 2.3658e-01, 3.9811e+02, 3.8521e+02],\n",
            "        [1.2976e+02, 2.0679e+02, 2.2828e+02, 3.9541e+02],\n",
            "        [6.4836e+00, 4.0973e+00, 3.1059e+02, 2.4025e+02],\n",
            "        [5.7792e+00, 1.2040e+00, 8.5372e+01, 9.2349e+01],\n",
            "        [2.4243e+02, 9.1885e+01, 4.1076e+02, 3.2288e+02],\n",
            "        [2.9263e+02, 9.2389e+01, 3.8842e+02, 2.4052e+02],\n",
            "        [7.7637e+01, 5.7859e+01, 1.8269e+02, 2.5329e+02],\n",
            "        [2.1083e+02, 6.3828e+01, 3.1426e+02, 2.4934e+02],\n",
            "        [2.4729e+02, 1.6760e+02, 3.4535e+02, 3.4111e+02],\n",
            "        [7.8994e+00, 7.5816e+01, 2.1482e+02, 3.3106e+02],\n",
            "        [2.1476e+02, 1.6838e+02, 3.1218e+02, 3.4144e+02],\n",
            "        [1.7639e+02, 8.9699e+00, 3.7989e+02, 1.1641e+02],\n",
            "        [1.1429e+02, 1.6842e+02, 2.1258e+02, 3.4171e+02],\n",
            "        [2.4528e+02, 5.9362e+01, 3.4701e+02, 2.5148e+02],\n",
            "        [8.0227e+01, 1.1227e+02, 4.0991e+02, 3.3894e+02],\n",
            "        [1.4687e+02, 1.6735e+02, 2.4554e+02, 3.4260e+02],\n",
            "        [1.8086e+02, 1.6816e+02, 2.7808e+02, 3.4261e+02],\n",
            "        [5.6792e+01, 1.5039e+01, 2.4476e+02, 3.8375e+02],\n",
            "        [2.3278e+01, 5.0446e+00, 1.9410e+02, 1.0135e+02],\n",
            "        [3.6613e+02, 5.9464e+01, 4.1514e+02, 1.9122e+02],\n",
            "        [1.7862e+02, 6.7463e+01, 2.7927e+02, 2.4678e+02],\n",
            "        [1.6891e+02, 2.4169e+00, 3.4271e+02, 1.8858e+02],\n",
            "        [1.0423e+02, 7.4904e+00, 2.7802e+02, 1.8687e+02],\n",
            "        [3.3168e+02, 6.1926e+00, 3.9446e+02, 1.5021e+02],\n",
            "        [9.5606e+01, 2.2868e+02, 2.7056e+02, 3.3727e+02],\n",
            "        [1.6199e+02, 2.2633e+02, 3.4186e+02, 3.3612e+02],\n",
            "        [2.3097e+02, 1.6169e+02, 4.1390e+02, 2.7146e+02],\n",
            "        [3.7254e+02, 4.8892e+00, 3.9204e+02, 5.6750e+01],\n",
            "        [2.5828e+01, 1.7680e+01, 1.6466e+02, 2.5068e+02],\n",
            "        [1.4826e+02, 6.8709e+01, 2.4365e+02, 2.4578e+02],\n",
            "        [1.1085e+02, 1.4015e+00, 4.0310e+02, 2.2506e+02],\n",
            "        [2.9508e+02, 3.0073e+02, 4.1419e+02, 3.7572e+02],\n",
            "        [3.3224e+00, 1.4556e+02, 2.5063e+02, 4.1357e+02],\n",
            "        [3.0257e+02, 2.4829e+02, 3.9326e+02, 3.4093e+02],\n",
            "        [1.0185e+02, 6.7410e+01, 2.2754e+02, 1.9940e+02],\n",
            "        [2.7844e+02, 1.8757e+02, 3.8406e+02, 3.5114e+02],\n",
            "        [3.5825e+02, 3.6874e+02, 3.7634e+02, 3.8344e+02],\n",
            "        [6.4067e-01, 4.4912e+01, 3.2928e+01, 1.2515e+02],\n",
            "        [2.1008e+01, 2.4254e+02, 1.3577e+02, 3.7725e+02],\n",
            "        [2.5475e+02, 8.5685e+01, 3.8674e+02, 1.8990e+02],\n",
            "        [2.7361e+01, 1.8859e+02, 3.4765e+02, 3.7624e+02],\n",
            "        [3.8205e+02, 3.7263e+02, 4.0359e+02, 3.8436e+02],\n",
            "        [3.0193e+01, 7.0535e+01, 1.4678e+02, 2.0721e+02],\n",
            "        [3.0430e+01, 1.6419e+02, 1.3364e+02, 3.4770e+02],\n",
            "        [1.1271e+02, 3.5907e+01, 3.1647e+02, 3.7751e+02],\n",
            "        [1.8309e+01, 2.9319e+02, 8.2345e+01, 3.5662e+02],\n",
            "        [2.7414e-03, 9.9770e+01, 8.9486e+01, 2.3065e+02],\n",
            "        [6.3215e+01, 1.5265e+02, 1.6195e+02, 3.2366e+02],\n",
            "        [0.0000e+00, 1.3755e+02, 3.6784e+01, 2.4687e+02],\n",
            "        [1.2963e+02, 1.9507e+02, 3.0659e+02, 3.0402e+02],\n",
            "        [2.6309e+02, 1.2188e+02, 3.6416e+02, 2.9588e+02],\n",
            "        [3.5926e+02, 1.1933e+02, 4.1395e+02, 2.9392e+02],\n",
            "        [9.2730e+01, 1.4517e+02, 2.3612e+02, 2.9132e+02],\n",
            "        [0.0000e+00, 9.0423e+01, 3.7702e+01, 2.0233e+02],\n",
            "        [2.1281e+02, 1.0984e+02, 3.8088e+02, 2.2512e+02],\n",
            "        [1.8025e+02, 1.7719e+02, 3.5727e+02, 2.8813e+02],\n",
            "        [1.9610e+02, 1.1733e+02, 2.9674e+02, 2.9362e+02],\n",
            "        [1.6382e+02, 1.1733e+02, 2.6261e+02, 2.9162e+02],\n",
            "        [1.5684e+01, 6.3657e+01, 7.0595e+01, 1.7694e+02],\n",
            "        [4.9630e+00, 8.0404e+01, 1.0621e+02, 2.9461e+02],\n",
            "        [2.2933e+02, 1.2052e+02, 3.3128e+02, 2.9425e+02],\n",
            "        [3.2312e+02, 1.5051e+02, 4.0455e+02, 2.4402e+02],\n",
            "        [2.2775e+02, 2.0925e+02, 4.0138e+02, 3.2120e+02],\n",
            "        [3.1978e-01, 1.8080e+02, 4.3651e+01, 2.9767e+02],\n",
            "        [3.8251e+02, 3.0004e+02, 4.1263e+02, 3.5320e+02],\n",
            "        [2.4656e+00, 1.1711e+02, 6.3260e+01, 2.0879e+02],\n",
            "        [3.2320e+02, 1.8389e+02, 4.0233e+02, 2.7490e+02],\n",
            "        [9.1498e+01, 2.8595e+02, 2.8156e+02, 3.8417e+02],\n",
            "        [1.4202e+01, 5.4575e+01, 5.5249e+01, 1.4781e+02],\n",
            "        [3.8742e+02, 3.7184e+02, 3.9912e+02, 3.8377e+02],\n",
            "        [2.3475e+01, 1.3837e+02, 1.4534e+02, 2.7280e+02],\n",
            "        [6.5597e+01, 1.1009e+02, 2.4149e+02, 2.3639e+02],\n",
            "        [3.0350e+02, 1.5749e+02, 3.7460e+02, 2.2914e+02],\n",
            "        [0.0000e+00, 1.7285e+02, 4.5232e+01, 4.1600e+02],\n",
            "        [1.2727e+02, 9.5793e+01, 3.0397e+02, 2.1251e+02],\n",
            "        [1.4125e+02, 1.4676e+02, 3.2059e+02, 2.5454e+02],\n",
            "        [0.0000e+00, 2.6927e+02, 2.7940e+01, 4.1600e+02],\n",
            "        [3.0262e+02, 1.8802e+02, 3.7541e+02, 2.6425e+02],\n",
            "        [1.3004e+01, 2.1042e+02, 1.8815e+02, 3.1833e+02]], device='cuda:0'), 'labels': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
            "       device='cuda:0'), 'scores': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 0.9999, 0.9999, 0.9999, 0.9998,\n",
            "        0.9998, 0.9998, 0.9997, 0.9997, 0.9997, 0.9996, 0.9996, 0.9996, 0.9995,\n",
            "        0.9992, 0.9990, 0.9989, 0.9989, 0.9988, 0.9983, 0.9982, 0.9981, 0.9977,\n",
            "        0.9973, 0.9972, 0.9972, 0.9970, 0.9970, 0.9966, 0.9959, 0.9947, 0.9940,\n",
            "        0.9938, 0.9936, 0.9927, 0.9914, 0.9910, 0.9902, 0.9891, 0.9874, 0.9848,\n",
            "        0.9840, 0.9840, 0.9839, 0.9824, 0.9816, 0.9779, 0.9754, 0.9753, 0.9744,\n",
            "        0.9733, 0.9701, 0.9655, 0.9655, 0.9466, 0.9431, 0.9424, 0.9387, 0.9367,\n",
            "        0.9029, 0.8957, 0.8954, 0.8685, 0.8558, 0.8388, 0.8240, 0.8233, 0.8152,\n",
            "        0.8095, 0.8037, 0.8010, 0.7827, 0.7632, 0.7455, 0.7397, 0.7336, 0.6872,\n",
            "        0.6830, 0.6539, 0.6397, 0.6229, 0.6141, 0.6121, 0.6103, 0.6031, 0.6006,\n",
            "        0.5654, 0.5571, 0.5551, 0.5340, 0.5283, 0.5190], device='cuda:0')}, {'boxes': tensor([[2.7430e+02, 1.4208e+02, 4.1323e+02, 4.1445e+02],\n",
            "        [3.0823e+02, 0.0000e+00, 4.1236e+02, 1.9130e+02],\n",
            "        [3.3333e+02, 8.0703e+01, 4.1229e+02, 2.7924e+02],\n",
            "        [3.2327e+00, 1.2557e+02, 1.2258e+02, 4.0612e+02],\n",
            "        [3.2805e+02, 1.7521e+02, 4.1063e+02, 3.7609e+02],\n",
            "        [1.8284e+02, 2.1711e+02, 2.7923e+02, 4.0683e+02],\n",
            "        [2.3022e+02, 2.4451e+02, 4.0594e+02, 3.7770e+02],\n",
            "        [1.5865e-02, 7.8794e+00, 7.1110e+01, 2.4776e+02],\n",
            "        [2.1361e+02, 4.9644e-01, 4.0312e+02, 1.1506e+02],\n",
            "        [1.4959e+02, 2.1726e+02, 2.4654e+02, 4.0823e+02],\n",
            "        [1.1539e+02, 2.1548e+02, 2.1386e+02, 4.0886e+02],\n",
            "        [2.0259e+00, 2.7681e+01, 1.8947e+02, 4.0017e+02],\n",
            "        [2.1635e+02, 2.1847e+02, 3.1188e+02, 4.0759e+02],\n",
            "        [2.6334e+02, 2.0542e+02, 3.6060e+02, 4.0545e+02],\n",
            "        [8.2990e+01, 2.1668e+02, 1.8022e+02, 4.0981e+02],\n",
            "        [5.5970e+00, 1.2764e+00, 8.7331e+01, 9.4126e+01],\n",
            "        [3.0384e+02, 1.4766e+02, 4.1567e+02, 2.8919e+02],\n",
            "        [2.0327e+02, 0.0000e+00, 3.9731e+02, 3.8888e+02],\n",
            "        [5.4047e+00, 8.2586e+00, 2.7829e+02, 2.2006e+02],\n",
            "        [2.8666e+02, 8.7680e+01, 3.8440e+02, 2.4329e+02],\n",
            "        [8.7227e+01, 1.1605e+02, 4.1034e+02, 3.3993e+02],\n",
            "        [1.6331e+02, 1.7237e+02, 2.6278e+02, 3.4400e+02],\n",
            "        [1.2909e+02, 1.7193e+02, 2.3019e+02, 3.4378e+02],\n",
            "        [4.1682e+01, 5.5485e+00, 2.0760e+02, 9.6949e+01],\n",
            "        [9.6304e+01, 6.2654e+01, 1.9850e+02, 2.4664e+02],\n",
            "        [2.3143e+02, 1.6867e+02, 3.2888e+02, 3.4271e+02],\n",
            "        [9.5201e+01, 1.6888e+02, 1.9668e+02, 3.4163e+02],\n",
            "        [1.9753e+02, 1.7060e+02, 2.9575e+02, 3.4329e+02],\n",
            "        [2.8699e+02, 2.7873e+02, 3.9402e+02, 3.8374e+02],\n",
            "        [3.6542e+02, 5.9852e+01, 4.1532e+02, 1.9912e+02],\n",
            "        [8.1141e+01, 2.3635e+01, 2.8350e+02, 3.4628e+02],\n",
            "        [1.1810e+02, 5.3902e+00, 2.9388e+02, 1.8821e+02],\n",
            "        [2.7656e+02, 2.0725e+01, 4.0894e+02, 2.8317e+02],\n",
            "        [6.8938e+00, 9.6835e+01, 3.0211e+02, 2.8884e+02],\n",
            "        [2.2913e+02, 6.8421e+01, 3.2965e+02, 2.4783e+02],\n",
            "        [7.5950e+01, 2.2646e+02, 2.6212e+02, 3.3723e+02],\n",
            "        [2.2990e+01, 1.9368e+01, 1.6402e+02, 2.5196e+02],\n",
            "        [1.3118e+02, 6.7685e+01, 2.2739e+02, 2.4459e+02],\n",
            "        [1.8395e+02, 1.8100e+01, 2.9532e+02, 2.4113e+02],\n",
            "        [6.4259e+01, 1.6741e+02, 1.6191e+02, 3.4253e+02],\n",
            "        [3.2852e+02, 3.8385e+00, 3.9354e+02, 1.5379e+02],\n",
            "        [1.9051e+02, 2.6850e+00, 3.5383e+02, 1.8797e+02],\n",
            "        [2.6451e+02, 1.5096e+02, 3.6243e+02, 3.2723e+02],\n",
            "        [2.6218e+01, 1.8132e+02, 3.5310e+02, 3.7348e+02],\n",
            "        [3.7222e+02, 4.6736e+00, 3.9208e+02, 5.7200e+01],\n",
            "        [1.2124e+00, 2.4829e+02, 7.0745e+01, 4.1600e+02],\n",
            "        [7.2715e+01, 4.0618e+01, 2.2952e+02, 1.8875e+02],\n",
            "        [6.6068e-01, 4.0789e+01, 3.0666e+01, 1.4243e+02],\n",
            "        [2.0593e+01, 2.3595e+02, 1.4328e+02, 3.8118e+02],\n",
            "        [3.5652e+02, 3.6887e+02, 3.7540e+02, 3.8361e+02],\n",
            "        [3.5913e+02, 1.2180e+02, 4.1518e+02, 2.9091e+02],\n",
            "        [1.5196e+02, 6.0516e+01, 3.4356e+02, 4.0604e+02],\n",
            "        [8.5174e+01, 3.4051e+01, 1.7148e+02, 2.1336e+02],\n",
            "        [6.4850e+01, 2.3984e+01, 4.1388e+02, 2.3422e+02],\n",
            "        [1.4552e+02, 8.2751e+01, 2.8464e+02, 2.2180e+02],\n",
            "        [1.2778e+02, 2.1182e+02, 3.0683e+02, 3.2272e+02],\n",
            "        [3.8204e+02, 3.7283e+02, 4.0348e+02, 3.8429e+02],\n",
            "        [1.7156e+02, 2.4240e+02, 3.5710e+02, 3.5396e+02],\n",
            "        [2.1424e+01, 2.8557e+02, 9.1248e+01, 3.6439e+02],\n",
            "        [1.7907e+02, 9.7115e+01, 3.5087e+02, 2.0970e+02],\n",
            "        [4.3457e+00, 8.4168e+01, 1.0588e+02, 2.9124e+02],\n",
            "        [2.2844e+02, 1.4350e+02, 4.0733e+02, 2.5736e+02],\n",
            "        [1.6293e+02, 9.9080e+01, 2.6396e+02, 2.7842e+02],\n",
            "        [2.0909e+01, 8.1508e+01, 1.8616e+02, 2.0613e+02],\n",
            "        [1.4704e+01, 3.5218e+01, 7.5174e+01, 1.8032e+02],\n",
            "        [1.9457e+02, 2.1049e+02, 3.7125e+02, 3.2161e+02],\n",
            "        [1.9639e+02, 1.0141e+02, 2.9833e+02, 2.7969e+02],\n",
            "        [0.0000e+00, 1.7873e+02, 9.4226e+01, 3.1918e+02],\n",
            "        [4.4953e-01, 1.1317e+02, 4.4022e+01, 2.3420e+02],\n",
            "        [1.6635e+02, 1.4518e+02, 3.3437e+02, 2.5710e+02],\n",
            "        [4.0568e-01, 1.6387e+02, 4.3975e+01, 2.8273e+02],\n",
            "        [3.8190e+02, 2.9945e+02, 4.1214e+02, 3.5240e+02],\n",
            "        [2.6890e+02, 4.9122e+01, 3.8692e+02, 1.8883e+02],\n",
            "        [3.2466e+02, 1.5780e+02, 4.0477e+02, 2.5110e+02],\n",
            "        [3.0329e+02, 1.4897e+02, 3.7479e+02, 2.2006e+02],\n",
            "        [0.0000e+00, 1.7377e+02, 4.5461e+01, 4.1600e+02],\n",
            "        [2.1628e+02, 2.3649e+02, 2.6364e+02, 3.3707e+02],\n",
            "        [3.2487e+02, 2.0926e+02, 4.0358e+02, 2.9865e+02],\n",
            "        [9.7458e+01, 1.7684e+02, 2.6362e+02, 2.9130e+02],\n",
            "        [2.1153e+02, 1.7699e+02, 3.8791e+02, 2.8720e+02],\n",
            "        [2.2130e+01, 1.3819e+02, 1.4720e+02, 2.7352e+02],\n",
            "        [1.1285e+02, 1.1398e+02, 2.1348e+02, 2.9543e+02],\n",
            "        [7.9424e+01, 1.1825e+02, 1.7897e+02, 2.9558e+02],\n",
            "        [1.3473e+01, 5.4116e+01, 5.3371e+01, 1.4115e+02],\n",
            "        [0.0000e+00, 2.7055e+02, 2.6702e+01, 4.1600e+02],\n",
            "        [1.5733e+02, 2.8677e+02, 3.5009e+02, 3.8386e+02],\n",
            "        [3.0260e+02, 1.7959e+02, 3.7585e+02, 2.5426e+02],\n",
            "        [1.5718e+02, 2.3098e+02, 2.0326e+02, 3.3057e+02],\n",
            "        [7.4744e+01, 2.8591e+02, 2.6654e+02, 3.8347e+02]], device='cuda:0'), 'labels': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], device='cuda:0'), 'scores': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 0.9999, 0.9999, 0.9999, 0.9999,\n",
            "        0.9999, 0.9998, 0.9998, 0.9998, 0.9998, 0.9997, 0.9997, 0.9996, 0.9995,\n",
            "        0.9990, 0.9985, 0.9981, 0.9973, 0.9971, 0.9969, 0.9969, 0.9968, 0.9968,\n",
            "        0.9965, 0.9963, 0.9960, 0.9942, 0.9930, 0.9917, 0.9917, 0.9902, 0.9901,\n",
            "        0.9893, 0.9876, 0.9873, 0.9866, 0.9839, 0.9839, 0.9827, 0.9826, 0.9825,\n",
            "        0.9821, 0.9780, 0.9776, 0.9712, 0.9615, 0.9549, 0.9538, 0.9522, 0.9489,\n",
            "        0.9488, 0.9455, 0.9418, 0.9342, 0.9266, 0.9154, 0.9153, 0.9052, 0.8939,\n",
            "        0.8882, 0.8741, 0.8239, 0.8128, 0.8065, 0.7935, 0.7750, 0.7452, 0.7443,\n",
            "        0.7329, 0.7267, 0.6732, 0.6584, 0.6493, 0.6281, 0.6135, 0.6040, 0.6015,\n",
            "        0.5960, 0.5959, 0.5772, 0.5743, 0.5597, 0.5414, 0.5347, 0.5235],\n",
            "       device='cuda:0')}, {'boxes': tensor([[2.8147e+02, 1.4590e+02, 4.1305e+02, 4.0932e+02],\n",
            "        [3.0768e+02, 0.0000e+00, 4.1170e+02, 1.9037e+02],\n",
            "        [1.5799e+00, 1.6708e+02, 1.0222e+02, 3.9365e+02],\n",
            "        [3.2764e+02, 1.7170e+02, 4.1007e+02, 3.7839e+02],\n",
            "        [2.5273e+02, 1.1404e+00, 4.1479e+02, 2.5257e+02],\n",
            "        [3.8741e+00, 1.3178e+01, 1.3292e+02, 3.7520e+02],\n",
            "        [3.1726e+02, 7.3956e+01, 4.0507e+02, 2.5149e+02],\n",
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            "        [2.5460e+01, 4.0581e+00, 2.2192e+02, 2.0601e+02],\n",
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            "        [1.2259e+02, 1.9120e+02, 4.0876e+02, 3.8047e+02],\n",
            "        [1.6580e+02, 2.1709e+02, 2.6349e+02, 4.1036e+02],\n",
            "        [2.3866e+02, 2.8101e+02, 4.0311e+02, 3.7379e+02],\n",
            "        [2.3227e+02, 2.1675e+02, 3.3031e+02, 4.1033e+02],\n",
            "        [1.9892e+02, 2.1634e+02, 2.9622e+02, 4.1065e+02],\n",
            "        [8.2975e+01, 1.6564e+02, 1.9810e+02, 3.8615e+02],\n",
            "        [4.3194e+00, 1.1322e+00, 8.8463e+01, 9.3263e+01],\n",
            "        [3.0576e+02, 1.4665e+02, 4.1455e+02, 2.8828e+02],\n",
            "        [2.0130e+02, 4.5741e-01, 3.9906e+02, 3.8567e+02],\n",
            "        [1.3282e+02, 2.1879e+02, 2.3092e+02, 4.1120e+02],\n",
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            "        [0.0000e+00, 2.2486e+01, 2.6767e+02, 2.9329e+02],\n",
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            "        [1.4720e+02, 1.6825e+02, 2.4589e+02, 3.4227e+02],\n",
            "        [2.8034e+00, 1.1722e+02, 3.0803e+02, 3.3858e+02],\n",
            "        [1.8067e+02, 1.6677e+02, 2.7872e+02, 3.4133e+02],\n",
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            "        [2.4625e+02, 1.6600e+02, 3.4610e+02, 3.4145e+02],\n",
            "        [2.1392e+02, 1.6530e+02, 3.1189e+02, 3.4029e+02],\n",
            "        [1.7894e+02, 6.4790e+01, 2.8137e+02, 2.4961e+02],\n",
            "        [3.6567e+02, 5.9521e+01, 4.1499e+02, 1.9465e+02],\n",
            "        [6.5194e+01, 1.6784e+02, 1.6157e+02, 3.4309e+02],\n",
            "        [4.7434e+01, 2.0497e+02, 1.4477e+02, 4.0284e+02],\n",
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            "        [1.9238e+02, 1.7740e+02, 3.7410e+02, 2.8830e+02],\n",
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            "        [3.5810e+02, 9.0363e+01, 4.1393e+02, 2.6356e+02],\n",
            "        [2.1490e+02, 1.2711e+02, 3.8348e+02, 2.3987e+02],\n",
            "        [1.9562e+02, 1.1789e+02, 2.9644e+02, 2.9249e+02],\n",
            "        [3.0298e+02, 1.1416e+02, 3.7584e+02, 1.8666e+02],\n",
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            "        [0.0000e+00, 1.7270e+02, 3.7198e+01, 2.7708e+02],\n",
            "        [3.2450e+02, 1.6615e+02, 4.0583e+02, 2.6007e+02],\n",
            "        [1.1404e+02, 1.3465e+02, 2.1230e+02, 3.0999e+02],\n",
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            "        [2.2554e+02, 2.0951e+02, 4.0235e+02, 3.2084e+02],\n",
            "        [1.9559e-01, 9.1548e+01, 4.8182e+01, 3.2663e+02],\n",
            "        [1.9184e+01, 3.0112e+02, 7.9094e+01, 3.4688e+02],\n",
            "        [1.5600e+02, 2.8461e+02, 3.4689e+02, 3.8284e+02],\n",
            "        [1.4726e+01, 1.4473e+02, 1.8444e+02, 2.6119e+02],\n",
            "        [3.0635e+02, 2.4675e+02, 3.9131e+02, 3.3599e+02],\n",
            "        [5.4658e+01, 2.1461e+02, 2.4994e+02, 3.7536e+02],\n",
            "        [3.2549e+02, 2.0806e+02, 4.0470e+02, 2.9985e+02],\n",
            "        [1.3675e+01, 5.4888e+01, 5.3935e+01, 1.4193e+02],\n",
            "        [3.0317e+02, 1.6490e+02, 3.7533e+02, 2.3787e+02],\n",
            "        [1.3954e+01, 1.9478e+02, 1.8756e+02, 3.0304e+02],\n",
            "        [2.3787e+02, 3.7797e+01, 3.8437e+02, 1.9108e+02],\n",
            "        [6.3823e+01, 9.7143e+01, 1.6379e+02, 2.8506e+02],\n",
            "        [0.0000e+00, 2.6898e+02, 2.7096e+01, 4.1600e+02],\n",
            "        [1.5858e+02, 1.4662e+02, 3.4080e+02, 2.5728e+02],\n",
            "        [9.5578e+01, 1.4712e+02, 2.7209e+02, 2.6017e+02],\n",
            "        [2.5361e-01, 2.0427e+02, 4.3916e+01, 3.2323e+02]], device='cuda:0'), 'labels': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1], device='cuda:0'), 'scores': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 0.9999, 0.9999, 0.9999,\n",
            "        0.9998, 0.9998, 0.9998, 0.9997, 0.9996, 0.9996, 0.9996, 0.9996, 0.9996,\n",
            "        0.9995, 0.9994, 0.9994, 0.9992, 0.9992, 0.9991, 0.9985, 0.9984, 0.9981,\n",
            "        0.9973, 0.9970, 0.9968, 0.9963, 0.9953, 0.9950, 0.9940, 0.9921, 0.9919,\n",
            "        0.9918, 0.9909, 0.9905, 0.9899, 0.9897, 0.9895, 0.9889, 0.9827, 0.9827,\n",
            "        0.9822, 0.9821, 0.9818, 0.9809, 0.9786, 0.9750, 0.9709, 0.9695, 0.9599,\n",
            "        0.9582, 0.9476, 0.9378, 0.9349, 0.9331, 0.9222, 0.9109, 0.9090, 0.9017,\n",
            "        0.8934, 0.8875, 0.8734, 0.8731, 0.8461, 0.8431, 0.8389, 0.8249, 0.8236,\n",
            "        0.8233, 0.8184, 0.8096, 0.8079, 0.7944, 0.7892, 0.7844, 0.7799, 0.7770,\n",
            "        0.7732, 0.7560, 0.7484, 0.7150, 0.7010, 0.6811, 0.6810, 0.6562, 0.6533,\n",
            "        0.6502, 0.6411, 0.6379, 0.6303, 0.6285, 0.6008, 0.5950, 0.5730, 0.5651,\n",
            "        0.5443], device='cuda:0')}, {'boxes': tensor([[2.7640e+02, 1.4080e+02, 4.1363e+02, 4.1168e+02],\n",
            "        [3.1292e+02, 9.0693e+01, 4.1600e+02, 2.9455e+02],\n",
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            "        [3.2707e+02, 1.8351e+02, 4.1028e+02, 3.7800e+02],\n",
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            "        [0.0000e+00, 6.9784e+00, 7.0867e+01, 2.5063e+02],\n",
            "        [1.8273e+02, 2.1624e+02, 2.7944e+02, 4.0862e+02],\n",
            "        [1.4872e+02, 2.1458e+02, 2.4634e+02, 4.0843e+02],\n",
            "        [2.1611e+02, 2.1666e+02, 3.1261e+02, 4.0779e+02],\n",
            "        [1.1555e+02, 2.1478e+02, 2.1328e+02, 4.0920e+02],\n",
            "        [2.4934e+02, 2.1498e+02, 3.4614e+02, 4.0945e+02],\n",
            "        [7.6740e+01, 1.6770e+02, 1.9284e+02, 3.8778e+02],\n",
            "        [3.0399e+01, 1.1406e+00, 2.4141e+02, 2.0484e+02],\n",
            "        [2.0546e+02, 1.2920e+00, 3.9880e+02, 3.8118e+02],\n",
            "        [5.3104e+00, 1.0974e+00, 8.5590e+01, 9.2065e+01],\n",
            "        [5.7894e+00, 1.9959e+01, 2.9949e+02, 3.1091e+02],\n",
            "        [3.6533e+02, 6.0015e+01, 4.1532e+02, 1.9990e+02],\n",
            "        [2.6679e+01, 5.8106e+01, 2.1634e+02, 4.0779e+02],\n",
            "        [2.7266e+02, 9.4390e+01, 3.9435e+02, 2.1755e+02],\n",
            "        [1.9773e+02, 1.6827e+02, 2.9512e+02, 3.4295e+02],\n",
            "        [1.6426e+02, 1.6845e+02, 2.6254e+02, 3.4399e+02],\n",
            "        [2.3089e+02, 1.6694e+02, 3.2831e+02, 3.4210e+02],\n",
            "        [3.5132e+01, 0.0000e+00, 1.9342e+02, 9.9699e+01],\n",
            "        [1.2963e+02, 1.6717e+02, 2.2927e+02, 3.4305e+02],\n",
            "        [5.2274e+01, 1.1283e+02, 3.9243e+02, 3.3226e+02],\n",
            "        [0.0000e+00, 1.2854e+02, 8.4268e+01, 3.1748e+02],\n",
            "        [1.5350e+02, 4.6866e+00, 3.2447e+02, 1.8963e+02],\n",
            "        [1.5499e+01, 2.0321e+02, 1.1227e+02, 3.4457e+02],\n",
            "        [1.2610e+02, 1.8178e+02, 4.0725e+02, 3.7499e+02],\n",
            "        [6.0454e+01, 6.4661e+01, 1.6379e+02, 2.4813e+02],\n",
            "        [3.7279e+02, 3.7411e+00, 3.9198e+02, 5.7643e+01],\n",
            "        [2.7733e+02, 2.1332e+01, 4.0087e+02, 2.7858e+02],\n",
            "        [9.6994e+01, 7.0009e+01, 1.9662e+02, 2.5107e+02],\n",
            "        [2.3180e+02, 6.9537e+01, 3.2865e+02, 2.4735e+02],\n",
            "        [1.1207e+02, 0.0000e+00, 4.0189e+02, 2.2347e+02],\n",
            "        [2.6373e+02, 1.5095e+02, 3.6247e+02, 3.2587e+02],\n",
            "        [1.6214e+02, 7.0513e+01, 2.6270e+02, 2.4836e+02],\n",
            "        [2.6424e+01, 7.9389e+01, 1.3538e+02, 2.7588e+02],\n",
            "        [2.9905e+02, 3.0072e+02, 4.1061e+02, 3.7200e+02],\n",
            "        [8.7071e-01, 2.4756e+02, 7.0222e+01, 4.1600e+02],\n",
            "        [2.6258e-01, 4.2796e+01, 3.2304e+01, 1.2714e+02],\n",
            "        [1.0684e+02, 4.2507e+01, 2.6642e+02, 1.8736e+02],\n",
            "        [3.2692e+02, 3.6346e+00, 3.9245e+02, 1.5494e+02],\n",
            "        [1.8067e+02, 8.1678e+01, 3.1700e+02, 2.2134e+02],\n",
            "        [2.0076e+01, 2.4444e+02, 1.3857e+02, 3.7824e+02],\n",
            "        [3.5990e+02, 1.3797e+02, 4.1503e+02, 3.0619e+02],\n",
            "        [1.4966e+02, 5.8761e+01, 3.4391e+02, 4.0547e+02],\n",
            "        [3.5645e+02, 3.6858e+02, 3.7523e+02, 3.8384e+02],\n",
            "        [1.7154e+01, 2.9444e+02, 8.3509e+01, 3.5652e+02],\n",
            "        [2.8739e+02, 1.7186e+02, 3.8690e+02, 3.4747e+02],\n",
            "        [0.0000e+00, 1.0025e+02, 9.0194e+01, 2.2970e+02],\n",
            "        [9.7027e+01, 1.3263e+02, 1.9520e+02, 3.0807e+02],\n",
            "        [1.7842e+02, 1.4452e+02, 3.1628e+02, 2.8999e+02],\n",
            "        [3.8233e+02, 3.7274e+02, 4.0391e+02, 3.8426e+02],\n",
            "        [0.0000e+00, 1.2083e+02, 3.6702e+01, 2.3007e+02],\n",
            "        [3.5412e+02, 8.4821e+01, 4.1247e+02, 2.5751e+02],\n",
            "        [1.1245e+02, 1.4335e+02, 2.5011e+02, 2.8912e+02],\n",
            "        [1.5656e+02, 2.4268e+02, 3.4070e+02, 3.5365e+02],\n",
            "        [8.8697e+01, 2.4075e+02, 2.7488e+02, 3.5372e+02],\n",
            "        [1.8021e+02, 2.1056e+02, 3.5419e+02, 3.2134e+02],\n",
            "        [1.9048e+02, 1.2906e+02, 3.7308e+02, 2.4182e+02],\n",
            "        [3.2240e+02, 1.4008e+02, 4.0593e+02, 2.3754e+02],\n",
            "        [6.4292e+01, 1.3400e+02, 1.6136e+02, 3.0865e+02],\n",
            "        [3.0619e+02, 2.4689e+02, 3.9031e+02, 3.3591e+02],\n",
            "        [3.8183e+02, 2.9955e+02, 4.1256e+02, 3.5178e+02],\n",
            "        [2.3005e+02, 1.5812e+02, 4.0368e+02, 2.7419e+02],\n",
            "        [2.0311e+01, 8.5692e+01, 1.8340e+02, 2.0548e+02],\n",
            "        [3.2954e-01, 1.5789e+02, 4.3506e+01, 2.7323e+02],\n",
            "        [7.9516e+01, 2.0954e+02, 2.5709e+02, 3.2167e+02],\n",
            "        [3.2463e+02, 1.7384e+02, 4.0594e+02, 2.6842e+02],\n",
            "        [1.3327e+02, 1.9255e+02, 3.0302e+02, 3.0562e+02],\n",
            "        [1.7168e+00, 8.8823e+01, 4.8444e+01, 3.2678e+02],\n",
            "        [2.4509e+02, 4.3611e+01, 4.0082e+02, 1.8470e+02],\n",
            "        [4.2409e-01, 2.0655e+02, 4.3499e+01, 3.2196e+02],\n",
            "        [0.0000e+00, 1.7246e+02, 4.4776e+01, 4.1600e+02],\n",
            "        [1.2551e+01, 1.7498e+02, 2.6175e+02, 3.7794e+02],\n",
            "        [0.0000e+00, 2.7017e+02, 2.6434e+01, 4.1600e+02],\n",
            "        [3.0301e+02, 1.4110e+02, 3.7496e+02, 2.1296e+02],\n",
            "        [5.0335e+00, 1.2267e+02, 6.9310e+01, 2.0786e+02],\n",
            "        [2.1884e+01, 1.6812e+02, 1.4564e+02, 3.0874e+02],\n",
            "        [1.6220e+01, 4.5775e+01, 9.6058e+01, 1.7575e+02],\n",
            "        [1.2469e+02, 1.2790e+02, 3.0944e+02, 2.4115e+02],\n",
            "        [3.2494e+02, 2.2555e+02, 4.0511e+02, 3.1686e+02],\n",
            "        [1.4176e+01, 5.6104e+01, 5.5060e+01, 1.4517e+02],\n",
            "        [1.6100e+01, 1.4475e+02, 1.8646e+02, 2.5768e+02],\n",
            "        [1.5840e+02, 2.8686e+02, 3.4855e+02, 3.8294e+02],\n",
            "        [3.0309e+02, 1.7292e+02, 3.7474e+02, 2.4747e+02],\n",
            "        [9.9888e+00, 2.1324e+02, 2.1002e+02, 3.2802e+02]], device='cuda:0'), 'labels': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
            "       device='cuda:0'), 'scores': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 0.9999, 0.9999, 0.9998, 0.9998,\n",
            "        0.9998, 0.9998, 0.9998, 0.9997, 0.9996, 0.9995, 0.9995, 0.9994, 0.9989,\n",
            "        0.9986, 0.9984, 0.9981, 0.9981, 0.9970, 0.9966, 0.9966, 0.9963, 0.9961,\n",
            "        0.9958, 0.9946, 0.9894, 0.9875, 0.9870, 0.9855, 0.9852, 0.9847, 0.9844,\n",
            "        0.9842, 0.9841, 0.9831, 0.9824, 0.9806, 0.9787, 0.9785, 0.9742, 0.9722,\n",
            "        0.9716, 0.9693, 0.9681, 0.9665, 0.9660, 0.9655, 0.9508, 0.9347, 0.9294,\n",
            "        0.9288, 0.9203, 0.9192, 0.9111, 0.8971, 0.8856, 0.8780, 0.8691, 0.8460,\n",
            "        0.8378, 0.8291, 0.8204, 0.8050, 0.8038, 0.8021, 0.7939, 0.7915, 0.7648,\n",
            "        0.7592, 0.7447, 0.7418, 0.7254, 0.7192, 0.6893, 0.6880, 0.6718, 0.6601,\n",
            "        0.6528, 0.6415, 0.6357, 0.6309, 0.6247, 0.5947, 0.5947, 0.5733, 0.5631,\n",
            "        0.5528], device='cuda:0')}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "labels = []\n",
        "preds_adj_all = []\n",
        "annot_all = []\n",
        "\n",
        "for im, annot in tqdm(test_data_loader, position = 0, leave = True):\n",
        "    im = list(img.to(device) for img in im)\n",
        "\n",
        "    for t in annot:\n",
        "        labels += t['labels']\n",
        "\n",
        "    with torch.no_grad():\n",
        "        preds_adj = make_prediction(model, im, 0.1)\n",
        "        preds_adj = [{k: v.to(torch.device('cpu')) for k, v in t.items()} for t in preds_adj]\n",
        "        preds_adj_all.append(preds_adj)\n",
        "        annot_all.append(annot)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CEhXSN4swsFu",
        "outputId": "09b72d33-de4a-41cd-9877-0f6f4c6b2758"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9/9 [00:04<00:00,  1.86it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "\n",
        "def get_batch_statistics(outputs, targets, iou_threshold):\n",
        "    \"\"\" Compute true positives, predicted scores and predicted labels per sample \"\"\"\n",
        "    batch_metrics = []\n",
        "    for sample_i in range(len(outputs)):\n",
        "\n",
        "        if outputs[sample_i] is None:\n",
        "            continue\n",
        "\n",
        "        output = outputs[sample_i] # predict\n",
        "        # pred_boxes = output['boxes']\n",
        "        # pred_scores = output['scores']\n",
        "        # pred_labels = output['labels']\n",
        "\n",
        "        true_positives = torch.zeros(output['boxes'].shape[0])   # 예측 객체 개수\n",
        " \n",
        "        annotations = targets[sample_i]  # actual\n",
        "        target_labels = annotations['labels'] if len(annotations) else []\n",
        "        if len(annotations):    # len(annotations) = 3\n",
        "            detected_boxes = []\n",
        "            target_boxes = annotations['boxes']\n",
        "\n",
        "            for pred_i, (pred_box, pred_label) in enumerate(zip(output['boxes'], output['labels'])): # 예측값에 대해서..\n",
        "\n",
        "                # If targets are found break\n",
        "                if len(detected_boxes) == len(target_labels): # annotations -> target_labels\n",
        "                    break\n",
        "\n",
        "                # Ignore if label is not one of the target labels\n",
        "                if pred_label not in target_labels:\n",
        "                    continue\n",
        "\n",
        "                iou, box_index = bbox_iou(pred_box.unsqueeze(0), target_boxes).max(0)   # box_index : 실제 어떤 바운딩 박스랑 IoU 가 가장 높은지 index\n",
        "                if iou >= iou_threshold and box_index not in detected_boxes: # iou만 맞으면 통과?\n",
        "                    true_positives[pred_i] = 1\n",
        "                    detected_boxes += [box_index]  # 예측된거랑 실제랑 매핑해서 하나씩 index 채움\n",
        "        batch_metrics.append([true_positives, output['scores'], output['labels']])\n",
        "    return batch_metrics\n",
        "\n",
        "def bbox_iou(box1, box2, x1y1x2y2=True):\n",
        "    \"\"\"\n",
        "    Returns the IoU of two bounding boxes\n",
        "    \"\"\"\n",
        "    if not x1y1x2y2:\n",
        "        # Transform from center and width to exact coordinates\n",
        "        b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2\n",
        "        b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2\n",
        "        b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2\n",
        "        b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2\n",
        "    else:\n",
        "        # Get the coordinates of bounding boxes\n",
        "        b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]\n",
        "        b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]\n",
        "\n",
        "    # get the corrdinates of the intersection rectangle\n",
        "    inter_rect_x1 = torch.max(b1_x1, b2_x1)\n",
        "    inter_rect_y1 = torch.max(b1_y1, b2_y1)\n",
        "    inter_rect_x2 = torch.min(b1_x2, b2_x2)\n",
        "    inter_rect_y2 = torch.min(b1_y2, b2_y2)\n",
        "    # Intersection area\n",
        "    inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(inter_rect_y2 - inter_rect_y1 + 1, min=0)\n",
        "    # Union Area\n",
        "    b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)\n",
        "    b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)\n",
        "\n",
        "    iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)\n",
        "\n",
        "    return iou\n",
        "\n",
        "def ap_per_class(tp, conf, pred_cls, target_cls):\n",
        "    \"\"\" Compute the average precision, given the recall and precision curves.\n",
        "    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.\n",
        "    # Arguments\n",
        "        tp:    True positives (list).\n",
        "        conf:  Objectness value from 0-1 (list).\n",
        "        pred_cls: Predicted object classes (list).\n",
        "        target_cls: True object classes (list).\n",
        "    # Returns\n",
        "        The average precision as computed in py-faster-rcnn.\n",
        "    \"\"\"\n",
        "\n",
        "    # Sort by objectness\n",
        "    i = torch.argsort(-conf)\n",
        "    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]\n",
        "\n",
        "    # Find unique classes\n",
        "    unique_classes = torch.unique(target_cls)   # 2가 거의 예측안됨\n",
        "    print(unique_classes)\n",
        "    # Create Precision-Recall curve and compute AP for each class\n",
        "    ap, p, r = [], [], []\n",
        "    for c in unique_classes:\n",
        "        i = pred_cls == c\n",
        "        n_gt = (target_cls == c).sum()  # Number of ground truth objects\n",
        "        n_p = i.sum()  # Number of predicted objects\n",
        "\n",
        "        if n_p == 0 and n_gt == 0:\n",
        "            continue\n",
        "        elif n_p == 0 or n_gt == 0:\n",
        "            ap.append(0)\n",
        "            r.append(0)\n",
        "            p.append(0)\n",
        "        else:\n",
        "            # Accumulate FPs and TPs\n",
        "            fpc = torch.cumsum(1 - tp[i],-1)\n",
        "            tpc = torch.cumsum(tp[i],-1)\n",
        "\n",
        "            # Recall\n",
        "            recall_curve = tpc / (n_gt + 1e-16)\n",
        "            r.append(recall_curve[-1])\n",
        "\n",
        "            # Precision\n",
        "            precision_curve = tpc / (tpc + fpc)\n",
        "            p.append(precision_curve[-1])\n",
        "\n",
        "            # AP from recall-precision curve\n",
        "            ap.append(compute_ap(recall_curve, precision_curve))\n",
        "\n",
        "    # Compute F1 score (harmonic mean of precision and recall)\n",
        "\n",
        "    p, r, ap = torch.tensor(np.array(p)), torch.tensor(np.array(r)), torch.tensor(np.array(ap))\n",
        "    f1 = 2 * p * r / (p + r + 1e-16)\n",
        "\n",
        "    return p, r, ap, f1, unique_classes\n",
        "\n",
        "def compute_ap(recall, precision):\n",
        "    \"\"\" Compute the average precision, given the recall and precision curves.\n",
        "    Code originally from https://github.com/rbgirshick/py-faster-rcnn.\n",
        "    # Arguments\n",
        "        recall:    The recall curve (list).\n",
        "        precision: The precision curve (list).\n",
        "    # Returns\n",
        "        The average precision as computed in py-faster-rcnn.\n",
        "    \"\"\"\n",
        "    # correct AP calculation\n",
        "    # first append sentinel values at the end\n",
        "    mrec = np.concatenate(([0.0], recall, [1.0]))\n",
        "    mpre = np.concatenate(([0.0], precision, [0.0]))\n",
        "    print\n",
        "\n",
        "    # compute the precision envelope\n",
        "    for i in range(mpre.size - 1, 0, -1):\n",
        "        mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])\n",
        "\n",
        "    # to calculate area under PR curve, look for points\n",
        "    # where X axis (recall) changes value\n",
        "    i = np.where(mrec[1:] != mrec[:-1])[0]\n",
        "\n",
        "    # and sum (\\Delta recall) * prec\n",
        "    ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])\n",
        "    return ap"
      ],
      "metadata": {
        "id": "qm7SFvg3wsus"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_metrics = []\n",
        "print(len(preds_adj_all))\n",
        "for batch_i in range(len(preds_adj_all)):\n",
        "    sample_metrics += get_batch_statistics(preds_adj_all[batch_i], annot_all[batch_i], iou_threshold=0.5) \n",
        "\n",
        "true_positives, pred_scores, pred_labels = [torch.cat(x, 0) for x in list(zip(*sample_metrics))]  # all the batches get concatenated\n",
        "\n",
        "precision, recall, AP, f1, ap_class = ap_per_class(true_positives, pred_scores, pred_labels, torch.tensor(labels))\n",
        "mAP = torch.mean(AP)\n",
        "print(f'mAP : {mAP}')\n",
        "print(f'AP : {AP}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJ5B300PwwmV",
        "outputId": "e60fda45-36b3-44ee-ba54-e320470a7cdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9\n",
            "tensor([0, 1])\n",
            "mAP : 0.16964215606293542\n",
            "AP : tensor([0.0000, 0.3393], dtype=torch.float64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since I only trained it for **10 epochs**, the **mAP is not very high**.\n",
        "This result took a lot of time on Google Colab and we need to **train it for more epochs**."
      ],
      "metadata": {
        "id": "Qn_J91z2H2tx"
      }
    }
  ]
}