{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>col_x</th>\n",
       "      <th>row_y</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>confidence</th>\n",
       "      <th>label</th>\n",
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       "      <th>caption</th>\n",
       "      <th>filename</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>459.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.888</td>\n",
       "      <td>human face</td>\n",
       "      <td>VL</td>\n",
       "      <td>a man with a white shirt and a black tie</td>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_149.jpg</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>431.0</td>\n",
       "      <td>115.0</td>\n",
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       "      <td>41.0</td>\n",
       "      <td>0.864</td>\n",
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       "      <td>a woman with a blonde hair and a white shirt</td>\n",
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       "    <tr>\n",
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       "      <td>160.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>79.0</td>\n",
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       "      <td>human face</td>\n",
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       "      <td>a close up of a cat's face</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>446.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>0.379</td>\n",
       "      <td>human face</td>\n",
       "      <td>VL</td>\n",
       "      <td>a brown cat with green eyes</td>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_24.jpg</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.0</td>\n",
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       "      <td>human face</td>\n",
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       "      <td>a man wearing a hat</td>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_94.jpg</td>\n",
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       "      <th>14323</th>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>0.924</td>\n",
       "      <td>dog</td>\n",
       "      <td>VL</td>\n",
       "      <td>a small dog standing on a rock near a stream</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_97.jpg</td>\n",
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       "    <tr>\n",
       "      <th>14324</th>\n",
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       "      <td>179.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>0.693</td>\n",
       "      <td>log</td>\n",
       "      <td>VL</td>\n",
       "      <td>a bird sitting on a rock next to a stream</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_97.jpg</td>\n",
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       "    <tr>\n",
       "      <th>14325</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>0.938</td>\n",
       "      <td>dog</td>\n",
       "      <td>VL</td>\n",
       "      <td>a small black and brown puppy is being held by...</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_58.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14326</th>\n",
       "      <td>0.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0.310</td>\n",
       "      <td>person</td>\n",
       "      <td>VL</td>\n",
       "      <td>a small brown dog is being held by a person</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_58.jpg</td>\n",
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       "    <tr>\n",
       "      <th>14327</th>\n",
       "      <td>137.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0.974</td>\n",
       "      <td>dog</td>\n",
       "      <td>VL</td>\n",
       "      <td>a small black and gray dog standing in the grass</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_6.jpg</td>\n",
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       "</table>\n",
       "<p>14328 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "       col_x  row_y  width  height  confidence       label source  \\\n",
       "0      459.0   97.0   29.0    34.0       0.888  human face     VL   \n",
       "1      431.0  115.0   31.0    41.0       0.864  human face     VL   \n",
       "2      160.0   76.0   63.0    79.0       0.377  human face     VL   \n",
       "3      446.0   85.0  173.0   203.0       0.379  human face     VL   \n",
       "4       -0.0  223.0  163.0   277.0       0.822  human face     VL   \n",
       "...      ...    ...    ...     ...         ...         ...    ...   \n",
       "14323    0.0   66.0  320.0   262.0       0.924         dog     VL   \n",
       "14324  156.0  366.0  179.0   134.0       0.693         log     VL   \n",
       "14325    2.0    0.0  396.0   374.0       0.938         dog     VL   \n",
       "14326    0.0  224.0  230.0   151.0       0.310      person     VL   \n",
       "14327  137.0   79.0  216.0   152.0       0.974         dog     VL   \n",
       "\n",
       "                                                 caption  \\\n",
       "0               a man with a white shirt and a black tie   \n",
       "1           a woman with a blonde hair and a white shirt   \n",
       "2                             a close up of a cat's face   \n",
       "3                            a brown cat with green eyes   \n",
       "4                                    a man wearing a hat   \n",
       "...                                                  ...   \n",
       "14323       a small dog standing on a rock near a stream   \n",
       "14324          a bird sitting on a rock next to a stream   \n",
       "14325  a small black and brown puppy is being held by...   \n",
       "14326        a small brown dog is being held by a person   \n",
       "14327   a small black and gray dog standing in the grass   \n",
       "\n",
       "                                              filename  \n",
       "0            oxford-iiit-pet/images/Abyssinian_149.jpg  \n",
       "1            oxford-iiit-pet/images/Abyssinian_149.jpg  \n",
       "2            oxford-iiit-pet/images/Abyssinian_149.jpg  \n",
       "3             oxford-iiit-pet/images/Abyssinian_24.jpg  \n",
       "4             oxford-iiit-pet/images/Abyssinian_94.jpg  \n",
       "...                                                ...  \n",
       "14323  oxford-iiit-pet/images/yorkshire_terrier_97.jpg  \n",
       "14324  oxford-iiit-pet/images/yorkshire_terrier_97.jpg  \n",
       "14325  oxford-iiit-pet/images/yorkshire_terrier_58.jpg  \n",
       "14326  oxford-iiit-pet/images/yorkshire_terrier_58.jpg  \n",
       "14327   oxford-iiit-pet/images/yorkshire_terrier_6.jpg  \n",
       "\n",
       "[14328 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_obj = pd.read_parquet('object_annotations.parquet')\n",
    "df_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>173.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>0.379</td>\n",
       "      <td>human face</td>\n",
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       "      <td>a brown cat with green eyes</td>\n",
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       "      <td>179.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>0.693</td>\n",
       "      <td>log</td>\n",
       "      <td>VL</td>\n",
       "      <td>a bird sitting on a rock next to a stream</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_97.jpg</td>\n",
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       "      <th>14325</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>0.938</td>\n",
       "      <td>dog</td>\n",
       "      <td>VL</td>\n",
       "      <td>a small black and brown puppy is being held by...</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_58.jpg</td>\n",
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       "      <th>14326</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>151.0</td>\n",
       "      <td>0.310</td>\n",
       "      <td>person</td>\n",
       "      <td>VL</td>\n",
       "      <td>a small brown dog is being held by a person</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_58.jpg</td>\n",
       "      <td>[0.0, 224.0, 230.0, 151.0]</td>\n",
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       "      <th>14327</th>\n",
       "      <td>137.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0.974</td>\n",
       "      <td>dog</td>\n",
       "      <td>VL</td>\n",
       "      <td>a small black and gray dog standing in the grass</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_6.jpg</td>\n",
       "      <td>[137.0, 79.0, 216.0, 152.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14328 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       col_x  row_y  width  height  confidence       label source  \\\n",
       "0      459.0   97.0   29.0    34.0       0.888  human face     VL   \n",
       "1      431.0  115.0   31.0    41.0       0.864  human face     VL   \n",
       "2      160.0   76.0   63.0    79.0       0.377  human face     VL   \n",
       "3      446.0   85.0  173.0   203.0       0.379  human face     VL   \n",
       "4       -0.0  223.0  163.0   277.0       0.822  human face     VL   \n",
       "...      ...    ...    ...     ...         ...         ...    ...   \n",
       "14323    0.0   66.0  320.0   262.0       0.924         dog     VL   \n",
       "14324  156.0  366.0  179.0   134.0       0.693         log     VL   \n",
       "14325    2.0    0.0  396.0   374.0       0.938         dog     VL   \n",
       "14326    0.0  224.0  230.0   151.0       0.310      person     VL   \n",
       "14327  137.0   79.0  216.0   152.0       0.974         dog     VL   \n",
       "\n",
       "                                                 caption  \\\n",
       "0               a man with a white shirt and a black tie   \n",
       "1           a woman with a blonde hair and a white shirt   \n",
       "2                             a close up of a cat's face   \n",
       "3                            a brown cat with green eyes   \n",
       "4                                    a man wearing a hat   \n",
       "...                                                  ...   \n",
       "14323       a small dog standing on a rock near a stream   \n",
       "14324          a bird sitting on a rock next to a stream   \n",
       "14325  a small black and brown puppy is being held by...   \n",
       "14326        a small brown dog is being held by a person   \n",
       "14327   a small black and gray dog standing in the grass   \n",
       "\n",
       "                                              filename  \\\n",
       "0            oxford-iiit-pet/images/Abyssinian_149.jpg   \n",
       "1            oxford-iiit-pet/images/Abyssinian_149.jpg   \n",
       "2            oxford-iiit-pet/images/Abyssinian_149.jpg   \n",
       "3             oxford-iiit-pet/images/Abyssinian_24.jpg   \n",
       "4             oxford-iiit-pet/images/Abyssinian_94.jpg   \n",
       "...                                                ...   \n",
       "14323  oxford-iiit-pet/images/yorkshire_terrier_97.jpg   \n",
       "14324  oxford-iiit-pet/images/yorkshire_terrier_97.jpg   \n",
       "14325  oxford-iiit-pet/images/yorkshire_terrier_58.jpg   \n",
       "14326  oxford-iiit-pet/images/yorkshire_terrier_58.jpg   \n",
       "14327   oxford-iiit-pet/images/yorkshire_terrier_6.jpg   \n",
       "\n",
       "                               bbox  \n",
       "0         [459.0, 97.0, 29.0, 34.0]  \n",
       "1        [431.0, 115.0, 31.0, 41.0]  \n",
       "2         [160.0, 76.0, 63.0, 79.0]  \n",
       "3       [446.0, 85.0, 173.0, 203.0]  \n",
       "4       [-0.0, 223.0, 163.0, 277.0]  \n",
       "...                             ...  \n",
       "14323     [0.0, 66.0, 320.0, 262.0]  \n",
       "14324  [156.0, 366.0, 179.0, 134.0]  \n",
       "14325      [2.0, 0.0, 396.0, 374.0]  \n",
       "14326    [0.0, 224.0, 230.0, 151.0]  \n",
       "14327   [137.0, 79.0, 216.0, 152.0]  \n",
       "\n",
       "[14328 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new column bbox with a list of the bounding box coordinates\n",
    "df_obj['bbox'] = df_obj.apply(lambda row: [row['col_x'], row['row_y'], row['width'], row['height']], axis=1)\n",
    "df_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confidence</th>\n",
       "      <th>label</th>\n",
       "      <th>filename</th>\n",
       "      <th>bbox</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.888</td>\n",
       "      <td>human face</td>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_149.jpg</td>\n",
       "      <td>[459.0, 97.0, 29.0, 34.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.864</td>\n",
       "      <td>human face</td>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_149.jpg</td>\n",
       "      <td>[431.0, 115.0, 31.0, 41.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.377</td>\n",
       "      <td>human face</td>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_149.jpg</td>\n",
       "      <td>[160.0, 76.0, 63.0, 79.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.379</td>\n",
       "      <td>human face</td>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_24.jpg</td>\n",
       "      <td>[446.0, 85.0, 173.0, 203.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.822</td>\n",
       "      <td>human face</td>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_94.jpg</td>\n",
       "      <td>[-0.0, 223.0, 163.0, 277.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14323</th>\n",
       "      <td>0.924</td>\n",
       "      <td>dog</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_97.jpg</td>\n",
       "      <td>[0.0, 66.0, 320.0, 262.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14324</th>\n",
       "      <td>0.693</td>\n",
       "      <td>log</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_97.jpg</td>\n",
       "      <td>[156.0, 366.0, 179.0, 134.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14325</th>\n",
       "      <td>0.938</td>\n",
       "      <td>dog</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_58.jpg</td>\n",
       "      <td>[2.0, 0.0, 396.0, 374.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14326</th>\n",
       "      <td>0.310</td>\n",
       "      <td>person</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_58.jpg</td>\n",
       "      <td>[0.0, 224.0, 230.0, 151.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14327</th>\n",
       "      <td>0.974</td>\n",
       "      <td>dog</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_6.jpg</td>\n",
       "      <td>[137.0, 79.0, 216.0, 152.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14328 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       confidence       label  \\\n",
       "0           0.888  human face   \n",
       "1           0.864  human face   \n",
       "2           0.377  human face   \n",
       "3           0.379  human face   \n",
       "4           0.822  human face   \n",
       "...           ...         ...   \n",
       "14323       0.924         dog   \n",
       "14324       0.693         log   \n",
       "14325       0.938         dog   \n",
       "14326       0.310      person   \n",
       "14327       0.974         dog   \n",
       "\n",
       "                                              filename  \\\n",
       "0            oxford-iiit-pet/images/Abyssinian_149.jpg   \n",
       "1            oxford-iiit-pet/images/Abyssinian_149.jpg   \n",
       "2            oxford-iiit-pet/images/Abyssinian_149.jpg   \n",
       "3             oxford-iiit-pet/images/Abyssinian_24.jpg   \n",
       "4             oxford-iiit-pet/images/Abyssinian_94.jpg   \n",
       "...                                                ...   \n",
       "14323  oxford-iiit-pet/images/yorkshire_terrier_97.jpg   \n",
       "14324  oxford-iiit-pet/images/yorkshire_terrier_97.jpg   \n",
       "14325  oxford-iiit-pet/images/yorkshire_terrier_58.jpg   \n",
       "14326  oxford-iiit-pet/images/yorkshire_terrier_58.jpg   \n",
       "14327   oxford-iiit-pet/images/yorkshire_terrier_6.jpg   \n",
       "\n",
       "                               bbox  \n",
       "0         [459.0, 97.0, 29.0, 34.0]  \n",
       "1        [431.0, 115.0, 31.0, 41.0]  \n",
       "2         [160.0, 76.0, 63.0, 79.0]  \n",
       "3       [446.0, 85.0, 173.0, 203.0]  \n",
       "4       [-0.0, 223.0, 163.0, 277.0]  \n",
       "...                             ...  \n",
       "14323     [0.0, 66.0, 320.0, 262.0]  \n",
       "14324  [156.0, 366.0, 179.0, 134.0]  \n",
       "14325      [2.0, 0.0, 396.0, 374.0]  \n",
       "14326    [0.0, 224.0, 230.0, 151.0]  \n",
       "14327   [137.0, 79.0, 216.0, 152.0]  \n",
       "\n",
       "[14328 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_obj = df_obj.drop(columns=['col_x', 'row_y', 'width', 'height', 'caption', 'source'])\n",
    "df_obj "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group by filenmae and aggregate the bbox, confidence, label columns into a list\n",
    "df_obj = df_obj.groupby('filename').agg({'bbox': list, 'confidence': list, 'label': list}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>filename</th>\n",
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       "      <th>label</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_1.jpg</td>\n",
       "      <td>[[98.0, 71.0, 359.0, 269.0]]</td>\n",
       "      <td>[0.878000020980835]</td>\n",
       "      <td>[cat]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_10.jpg</td>\n",
       "      <td>[[-1.0, 105.0, 287.0, 395.0]]</td>\n",
       "      <td>[0.9679999947547913]</td>\n",
       "      <td>[cat]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_100.jpg</td>\n",
       "      <td>[[48.0, 72.0, 288.0, 371.0], [0.0, 31.0, 148.0...</td>\n",
       "      <td>[0.9539999961853027, 0.4880000054836273, 0.430...</td>\n",
       "      <td>[cat, strap, strap, pillow]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_101.jpg</td>\n",
       "      <td>[[55.0, 32.0, 313.0, 204.0]]</td>\n",
       "      <td>[0.859000027179718]</td>\n",
       "      <td>[cat]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_102.jpg</td>\n",
       "      <td>[[22.0, 25.0, 477.0, 440.0], [198.0, 4.0, 73.0...</td>\n",
       "      <td>[0.9800000190734863, 0.6980000138282776, 0.460...</td>\n",
       "      <td>[cat, lamp, flower arrangement]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_95.jpg</td>\n",
       "      <td>[[103.0, 69.0, 250.0, 223.0]]</td>\n",
       "      <td>[0.9490000009536743]</td>\n",
       "      <td>[dog]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7271</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_96.jpg</td>\n",
       "      <td>[[1.0, 0.0, 390.0, 335.0], [144.0, 137.0, 130....</td>\n",
       "      <td>[0.9829999804496765, 0.6150000095367432]</td>\n",
       "      <td>[dog, bandanna]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7272</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_97.jpg</td>\n",
       "      <td>[[0.0, 66.0, 320.0, 262.0], [156.0, 366.0, 179...</td>\n",
       "      <td>[0.9240000247955322, 0.6930000185966492]</td>\n",
       "      <td>[dog, log]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7273</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_98.jpg</td>\n",
       "      <td>[[90.0, 107.0, 239.0, 241.0]]</td>\n",
       "      <td>[0.9679999947547913]</td>\n",
       "      <td>[dog]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7274</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_99.jpg</td>\n",
       "      <td>[[183.0, 46.0, 264.0, 272.0], [47.0, 30.0, 175...</td>\n",
       "      <td>[0.9900000095367432, 0.9210000038146973, 0.532...</td>\n",
       "      <td>[dog, chair, chair]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7275 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             filename  \\\n",
       "0             oxford-iiit-pet/images/Abyssinian_1.jpg   \n",
       "1            oxford-iiit-pet/images/Abyssinian_10.jpg   \n",
       "2           oxford-iiit-pet/images/Abyssinian_100.jpg   \n",
       "3           oxford-iiit-pet/images/Abyssinian_101.jpg   \n",
       "4           oxford-iiit-pet/images/Abyssinian_102.jpg   \n",
       "...                                               ...   \n",
       "7270  oxford-iiit-pet/images/yorkshire_terrier_95.jpg   \n",
       "7271  oxford-iiit-pet/images/yorkshire_terrier_96.jpg   \n",
       "7272  oxford-iiit-pet/images/yorkshire_terrier_97.jpg   \n",
       "7273  oxford-iiit-pet/images/yorkshire_terrier_98.jpg   \n",
       "7274  oxford-iiit-pet/images/yorkshire_terrier_99.jpg   \n",
       "\n",
       "                                                   bbox  \\\n",
       "0                          [[98.0, 71.0, 359.0, 269.0]]   \n",
       "1                         [[-1.0, 105.0, 287.0, 395.0]]   \n",
       "2     [[48.0, 72.0, 288.0, 371.0], [0.0, 31.0, 148.0...   \n",
       "3                          [[55.0, 32.0, 313.0, 204.0]]   \n",
       "4     [[22.0, 25.0, 477.0, 440.0], [198.0, 4.0, 73.0...   \n",
       "...                                                 ...   \n",
       "7270                      [[103.0, 69.0, 250.0, 223.0]]   \n",
       "7271  [[1.0, 0.0, 390.0, 335.0], [144.0, 137.0, 130....   \n",
       "7272  [[0.0, 66.0, 320.0, 262.0], [156.0, 366.0, 179...   \n",
       "7273                      [[90.0, 107.0, 239.0, 241.0]]   \n",
       "7274  [[183.0, 46.0, 264.0, 272.0], [47.0, 30.0, 175...   \n",
       "\n",
       "                                             confidence  \\\n",
       "0                                   [0.878000020980835]   \n",
       "1                                  [0.9679999947547913]   \n",
       "2     [0.9539999961853027, 0.4880000054836273, 0.430...   \n",
       "3                                   [0.859000027179718]   \n",
       "4     [0.9800000190734863, 0.6980000138282776, 0.460...   \n",
       "...                                                 ...   \n",
       "7270                               [0.9490000009536743]   \n",
       "7271           [0.9829999804496765, 0.6150000095367432]   \n",
       "7272           [0.9240000247955322, 0.6930000185966492]   \n",
       "7273                               [0.9679999947547913]   \n",
       "7274  [0.9900000095367432, 0.9210000038146973, 0.532...   \n",
       "\n",
       "                                label  \n",
       "0                               [cat]  \n",
       "1                               [cat]  \n",
       "2         [cat, strap, strap, pillow]  \n",
       "3                               [cat]  \n",
       "4     [cat, lamp, flower arrangement]  \n",
       "...                               ...  \n",
       "7270                            [dog]  \n",
       "7271                  [dog, bandanna]  \n",
       "7272                       [dog, log]  \n",
       "7273                            [dog]  \n",
       "7274              [dog, chair, chair]  \n",
       "\n",
       "[7275 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>filename</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_1.jpg</td>\n",
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       "      <td>[0.878000020980835]</td>\n",
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       "      <td>[{'bbox': [98.0, 71.0, 359.0, 269.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_10.jpg</td>\n",
       "      <td>[[-1.0, 105.0, 287.0, 395.0]]</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_100.jpg</td>\n",
       "      <td>[[48.0, 72.0, 288.0, 371.0], [0.0, 31.0, 148.0...</td>\n",
       "      <td>[0.9539999961853027, 0.4880000054836273, 0.430...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_101.jpg</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_102.jpg</td>\n",
       "      <td>[[22.0, 25.0, 477.0, 440.0], [198.0, 4.0, 73.0...</td>\n",
       "      <td>[0.9800000190734863, 0.6980000138282776, 0.460...</td>\n",
       "      <td>[cat, lamp, flower arrangement]</td>\n",
       "      <td>[{'bbox': [22.0, 25.0, 477.0, 440.0], 'confide...</td>\n",
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       "      <th>7270</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_95.jpg</td>\n",
       "      <td>[[103.0, 69.0, 250.0, 223.0]]</td>\n",
       "      <td>[0.9490000009536743]</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[{'bbox': [103.0, 69.0, 250.0, 223.0], 'confid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7271</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_96.jpg</td>\n",
       "      <td>[[1.0, 0.0, 390.0, 335.0], [144.0, 137.0, 130....</td>\n",
       "      <td>[0.9829999804496765, 0.6150000095367432]</td>\n",
       "      <td>[dog, bandanna]</td>\n",
       "      <td>[{'bbox': [1.0, 0.0, 390.0, 335.0], 'confidenc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7272</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_97.jpg</td>\n",
       "      <td>[[0.0, 66.0, 320.0, 262.0], [156.0, 366.0, 179...</td>\n",
       "      <td>[0.9240000247955322, 0.6930000185966492]</td>\n",
       "      <td>[dog, log]</td>\n",
       "      <td>[{'bbox': [0.0, 66.0, 320.0, 262.0], 'confiden...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7273</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_98.jpg</td>\n",
       "      <td>[[90.0, 107.0, 239.0, 241.0]]</td>\n",
       "      <td>[0.9679999947547913]</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[{'bbox': [90.0, 107.0, 239.0, 241.0], 'confid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7274</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_99.jpg</td>\n",
       "      <td>[[183.0, 46.0, 264.0, 272.0], [47.0, 30.0, 175...</td>\n",
       "      <td>[0.9900000095367432, 0.9210000038146973, 0.532...</td>\n",
       "      <td>[dog, chair, chair]</td>\n",
       "      <td>[{'bbox': [183.0, 46.0, 264.0, 272.0], 'confid...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7275 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             filename  \\\n",
       "0             oxford-iiit-pet/images/Abyssinian_1.jpg   \n",
       "1            oxford-iiit-pet/images/Abyssinian_10.jpg   \n",
       "2           oxford-iiit-pet/images/Abyssinian_100.jpg   \n",
       "3           oxford-iiit-pet/images/Abyssinian_101.jpg   \n",
       "4           oxford-iiit-pet/images/Abyssinian_102.jpg   \n",
       "...                                               ...   \n",
       "7270  oxford-iiit-pet/images/yorkshire_terrier_95.jpg   \n",
       "7271  oxford-iiit-pet/images/yorkshire_terrier_96.jpg   \n",
       "7272  oxford-iiit-pet/images/yorkshire_terrier_97.jpg   \n",
       "7273  oxford-iiit-pet/images/yorkshire_terrier_98.jpg   \n",
       "7274  oxford-iiit-pet/images/yorkshire_terrier_99.jpg   \n",
       "\n",
       "                                                   bbox  \\\n",
       "0                          [[98.0, 71.0, 359.0, 269.0]]   \n",
       "1                         [[-1.0, 105.0, 287.0, 395.0]]   \n",
       "2     [[48.0, 72.0, 288.0, 371.0], [0.0, 31.0, 148.0...   \n",
       "3                          [[55.0, 32.0, 313.0, 204.0]]   \n",
       "4     [[22.0, 25.0, 477.0, 440.0], [198.0, 4.0, 73.0...   \n",
       "...                                                 ...   \n",
       "7270                      [[103.0, 69.0, 250.0, 223.0]]   \n",
       "7271  [[1.0, 0.0, 390.0, 335.0], [144.0, 137.0, 130....   \n",
       "7272  [[0.0, 66.0, 320.0, 262.0], [156.0, 366.0, 179...   \n",
       "7273                      [[90.0, 107.0, 239.0, 241.0]]   \n",
       "7274  [[183.0, 46.0, 264.0, 272.0], [47.0, 30.0, 175...   \n",
       "\n",
       "                                             confidence  \\\n",
       "0                                   [0.878000020980835]   \n",
       "1                                  [0.9679999947547913]   \n",
       "2     [0.9539999961853027, 0.4880000054836273, 0.430...   \n",
       "3                                   [0.859000027179718]   \n",
       "4     [0.9800000190734863, 0.6980000138282776, 0.460...   \n",
       "...                                                 ...   \n",
       "7270                               [0.9490000009536743]   \n",
       "7271           [0.9829999804496765, 0.6150000095367432]   \n",
       "7272           [0.9240000247955322, 0.6930000185966492]   \n",
       "7273                               [0.9679999947547913]   \n",
       "7274  [0.9900000095367432, 0.9210000038146973, 0.532...   \n",
       "\n",
       "                                label  \\\n",
       "0                               [cat]   \n",
       "1                               [cat]   \n",
       "2         [cat, strap, strap, pillow]   \n",
       "3                               [cat]   \n",
       "4     [cat, lamp, flower arrangement]   \n",
       "...                               ...   \n",
       "7270                            [dog]   \n",
       "7271                  [dog, bandanna]   \n",
       "7272                       [dog, log]   \n",
       "7273                            [dog]   \n",
       "7274              [dog, chair, chair]   \n",
       "\n",
       "                                                 labels  \n",
       "0     [{'bbox': [98.0, 71.0, 359.0, 269.0], 'confide...  \n",
       "1     [{'bbox': [-1.0, 105.0, 287.0, 395.0], 'confid...  \n",
       "2     [{'bbox': [48.0, 72.0, 288.0, 371.0], 'confide...  \n",
       "3     [{'bbox': [55.0, 32.0, 313.0, 204.0], 'confide...  \n",
       "4     [{'bbox': [22.0, 25.0, 477.0, 440.0], 'confide...  \n",
       "...                                                 ...  \n",
       "7270  [{'bbox': [103.0, 69.0, 250.0, 223.0], 'confid...  \n",
       "7271  [{'bbox': [1.0, 0.0, 390.0, 335.0], 'confidenc...  \n",
       "7272  [{'bbox': [0.0, 66.0, 320.0, 262.0], 'confiden...  \n",
       "7273  [{'bbox': [90.0, 107.0, 239.0, 241.0], 'confid...  \n",
       "7274  [{'bbox': [183.0, 46.0, 264.0, 272.0], 'confid...  \n",
       "\n",
       "[7275 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new column 'labels' with a dict of bbox, confidence, label\n",
    "df_obj['labels'] = df_obj.apply(lambda row: [{'bbox': bbox, 'confidence': confidence, 'label': label} for bbox, confidence, label in zip(row['bbox'], row['confidence'], row['label'])], axis=1)\n",
    "df_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_1.jpg</td>\n",
       "      <td>[{'bbox': [98.0, 71.0, 359.0, 269.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_10.jpg</td>\n",
       "      <td>[{'bbox': [-1.0, 105.0, 287.0, 395.0], 'confid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_100.jpg</td>\n",
       "      <td>[{'bbox': [48.0, 72.0, 288.0, 371.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_101.jpg</td>\n",
       "      <td>[{'bbox': [55.0, 32.0, 313.0, 204.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_102.jpg</td>\n",
       "      <td>[{'bbox': [22.0, 25.0, 477.0, 440.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_95.jpg</td>\n",
       "      <td>[{'bbox': [103.0, 69.0, 250.0, 223.0], 'confid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7271</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_96.jpg</td>\n",
       "      <td>[{'bbox': [1.0, 0.0, 390.0, 335.0], 'confidenc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7272</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_97.jpg</td>\n",
       "      <td>[{'bbox': [0.0, 66.0, 320.0, 262.0], 'confiden...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7273</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_98.jpg</td>\n",
       "      <td>[{'bbox': [90.0, 107.0, 239.0, 241.0], 'confid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7274</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_99.jpg</td>\n",
       "      <td>[{'bbox': [183.0, 46.0, 264.0, 272.0], 'confid...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7275 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             filename  \\\n",
       "0             oxford-iiit-pet/images/Abyssinian_1.jpg   \n",
       "1            oxford-iiit-pet/images/Abyssinian_10.jpg   \n",
       "2           oxford-iiit-pet/images/Abyssinian_100.jpg   \n",
       "3           oxford-iiit-pet/images/Abyssinian_101.jpg   \n",
       "4           oxford-iiit-pet/images/Abyssinian_102.jpg   \n",
       "...                                               ...   \n",
       "7270  oxford-iiit-pet/images/yorkshire_terrier_95.jpg   \n",
       "7271  oxford-iiit-pet/images/yorkshire_terrier_96.jpg   \n",
       "7272  oxford-iiit-pet/images/yorkshire_terrier_97.jpg   \n",
       "7273  oxford-iiit-pet/images/yorkshire_terrier_98.jpg   \n",
       "7274  oxford-iiit-pet/images/yorkshire_terrier_99.jpg   \n",
       "\n",
       "                                                 labels  \n",
       "0     [{'bbox': [98.0, 71.0, 359.0, 269.0], 'confide...  \n",
       "1     [{'bbox': [-1.0, 105.0, 287.0, 395.0], 'confid...  \n",
       "2     [{'bbox': [48.0, 72.0, 288.0, 371.0], 'confide...  \n",
       "3     [{'bbox': [55.0, 32.0, 313.0, 204.0], 'confide...  \n",
       "4     [{'bbox': [22.0, 25.0, 477.0, 440.0], 'confide...  \n",
       "...                                                 ...  \n",
       "7270  [{'bbox': [103.0, 69.0, 250.0, 223.0], 'confid...  \n",
       "7271  [{'bbox': [1.0, 0.0, 390.0, 335.0], 'confidenc...  \n",
       "7272  [{'bbox': [0.0, 66.0, 320.0, 262.0], 'confiden...  \n",
       "7273  [{'bbox': [90.0, 107.0, 239.0, 241.0], 'confid...  \n",
       "7274  [{'bbox': [183.0, 46.0, 264.0, 272.0], 'confid...  \n",
       "\n",
       "[7275 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all columns except filename and labels\n",
    "df_obj = df_obj[['filename', 'labels']]\n",
    "df_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'bbox': [98.0, 71.0, 359.0, 269.0],\n",
       "  'confidence': 0.878000020980835,\n",
       "  'label': 'cat'}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot the first row 'filename' and 'labels' column using matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "row = df_obj.iloc[0]\n",
    "filename = row['filename']\n",
    "labels = row['labels']\n",
    "\n",
    "labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load the image\n",
    "img = Image.open(f'{filename}')\n",
    "img = np.array(img)\n",
    "\n",
    "# create figure and axes\n",
    "fig, ax = plt.subplots(1)\n",
    "\n",
    "# display the image\n",
    "ax.imshow(img)\n",
    "\n",
    "# plot bbox on the image along with labels and confidence\n",
    "for label in labels:\n",
    "    bbox = label['bbox']\n",
    "    rect = patches.Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3], linewidth=1, edgecolor='r', facecolor='none')\n",
    "    ax.add_patch(rect)\n",
    "    ax.text(bbox[0], bbox[1], f\"{label['label']} {label['confidence']:.2f}\", color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>video</th>\n",
       "      <th>frame_timestamp</th>\n",
       "      <th>filename</th>\n",
       "      <th>caption</th>\n",
       "      <th>ann_index</th>\n",
       "      <th>label</th>\n",
       "      <th>class_id</th>\n",
       "      <th>vl_label</th>\n",
       "      <th>vl_class_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_144.jpg</td>\n",
       "      <td>a cat standing on a wooden floor next to a glass</td>\n",
       "      <td>49.0</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_10.jpg</td>\n",
       "      <td>a cat laying on a bed</td>\n",
       "      <td>11.0</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_155.jpg</td>\n",
       "      <td>a cat yawning on a couch</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_101.jpg</td>\n",
       "      <td>a cat with green eyes is laying on a green bla...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_156.jpg</td>\n",
       "      <td>a cat is sitting on a couch</td>\n",
       "      <td>1857.0</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7304</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_99.jpg</td>\n",
       "      <td>a small dog standing on a sidewalk</td>\n",
       "      <td>7348.0</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[2]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7305</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_56.jpg</td>\n",
       "      <td>a small dog is standing in the grass</td>\n",
       "      <td>7301.0</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[2]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7306</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_97.jpg</td>\n",
       "      <td>a small dog is standing on a rock</td>\n",
       "      <td>7346.0</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[2]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7307</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_58.jpg</td>\n",
       "      <td>a small black and brown puppy is being held by...</td>\n",
       "      <td>7303.0</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[2]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7308</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_6.jpg</td>\n",
       "      <td>a small black and gray dog standing in the grass</td>\n",
       "      <td>7316.0</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[2]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7309 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     video  frame_timestamp                                         filename  \\\n",
       "0     None              NaN        oxford-iiit-pet/images/Abyssinian_144.jpg   \n",
       "1     None              NaN         oxford-iiit-pet/images/Abyssinian_10.jpg   \n",
       "2     None              NaN        oxford-iiit-pet/images/Abyssinian_155.jpg   \n",
       "3     None              NaN        oxford-iiit-pet/images/Abyssinian_101.jpg   \n",
       "4     None              NaN        oxford-iiit-pet/images/Abyssinian_156.jpg   \n",
       "...    ...              ...                                              ...   \n",
       "7304  None              NaN  oxford-iiit-pet/images/yorkshire_terrier_99.jpg   \n",
       "7305  None              NaN  oxford-iiit-pet/images/yorkshire_terrier_56.jpg   \n",
       "7306  None              NaN  oxford-iiit-pet/images/yorkshire_terrier_97.jpg   \n",
       "7307  None              NaN  oxford-iiit-pet/images/yorkshire_terrier_58.jpg   \n",
       "7308  None              NaN   oxford-iiit-pet/images/yorkshire_terrier_6.jpg   \n",
       "\n",
       "                                                caption  ann_index  label  \\\n",
       "0      a cat standing on a wooden floor next to a glass       49.0  [cat]   \n",
       "1                                 a cat laying on a bed       11.0  [cat]   \n",
       "2                              a cat yawning on a couch     1856.0  [cat]   \n",
       "3     a cat with green eyes is laying on a green bla...        2.0  [cat]   \n",
       "4                           a cat is sitting on a couch     1857.0  [cat]   \n",
       "...                                                 ...        ...    ...   \n",
       "7304                 a small dog standing on a sidewalk     7348.0  [dog]   \n",
       "7305               a small dog is standing in the grass     7301.0  [dog]   \n",
       "7306                  a small dog is standing on a rock     7346.0  [dog]   \n",
       "7307  a small black and brown puppy is being held by...     7303.0  [dog]   \n",
       "7308   a small black and gray dog standing in the grass     7316.0  [dog]   \n",
       "\n",
       "     class_id vl_label vl_class_id  \n",
       "0         [1]     None        None  \n",
       "1         [1]     None        None  \n",
       "2         [1]     None        None  \n",
       "3         [1]     None        None  \n",
       "4         [1]     None        None  \n",
       "...       ...      ...         ...  \n",
       "7304      [2]     None        None  \n",
       "7305      [2]     None        None  \n",
       "7306      [2]     None        None  \n",
       "7307      [2]     None        None  \n",
       "7308      [2]     None        None  \n",
       "\n",
       "[7309 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet('image_annotations.parquet')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>caption</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_144.jpg</td>\n",
       "      <td>a cat standing on a wooden floor next to a glass</td>\n",
       "      <td>[cat]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_10.jpg</td>\n",
       "      <td>a cat laying on a bed</td>\n",
       "      <td>[cat]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_155.jpg</td>\n",
       "      <td>a cat yawning on a couch</td>\n",
       "      <td>[cat]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_101.jpg</td>\n",
       "      <td>a cat with green eyes is laying on a green bla...</td>\n",
       "      <td>[cat]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_156.jpg</td>\n",
       "      <td>a cat is sitting on a couch</td>\n",
       "      <td>[cat]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7304</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_99.jpg</td>\n",
       "      <td>a small dog standing on a sidewalk</td>\n",
       "      <td>[dog]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7305</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_56.jpg</td>\n",
       "      <td>a small dog is standing in the grass</td>\n",
       "      <td>[dog]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7306</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_97.jpg</td>\n",
       "      <td>a small dog is standing on a rock</td>\n",
       "      <td>[dog]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7307</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_58.jpg</td>\n",
       "      <td>a small black and brown puppy is being held by...</td>\n",
       "      <td>[dog]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7308</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_6.jpg</td>\n",
       "      <td>a small black and gray dog standing in the grass</td>\n",
       "      <td>[dog]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7309 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             filename  \\\n",
       "0           oxford-iiit-pet/images/Abyssinian_144.jpg   \n",
       "1            oxford-iiit-pet/images/Abyssinian_10.jpg   \n",
       "2           oxford-iiit-pet/images/Abyssinian_155.jpg   \n",
       "3           oxford-iiit-pet/images/Abyssinian_101.jpg   \n",
       "4           oxford-iiit-pet/images/Abyssinian_156.jpg   \n",
       "...                                               ...   \n",
       "7304  oxford-iiit-pet/images/yorkshire_terrier_99.jpg   \n",
       "7305  oxford-iiit-pet/images/yorkshire_terrier_56.jpg   \n",
       "7306  oxford-iiit-pet/images/yorkshire_terrier_97.jpg   \n",
       "7307  oxford-iiit-pet/images/yorkshire_terrier_58.jpg   \n",
       "7308   oxford-iiit-pet/images/yorkshire_terrier_6.jpg   \n",
       "\n",
       "                                                caption  label  \n",
       "0      a cat standing on a wooden floor next to a glass  [cat]  \n",
       "1                                 a cat laying on a bed  [cat]  \n",
       "2                              a cat yawning on a couch  [cat]  \n",
       "3     a cat with green eyes is laying on a green bla...  [cat]  \n",
       "4                           a cat is sitting on a couch  [cat]  \n",
       "...                                                 ...    ...  \n",
       "7304                 a small dog standing on a sidewalk  [dog]  \n",
       "7305               a small dog is standing in the grass  [dog]  \n",
       "7306                  a small dog is standing on a rock  [dog]  \n",
       "7307  a small black and brown puppy is being held by...  [dog]  \n",
       "7308   a small black and gray dog standing in the grass  [dog]  \n",
       "\n",
       "[7309 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop all columns except filename, caption, label, class id\n",
    "df = df[['filename', 'caption', 'label']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>caption</th>\n",
       "      <th>label</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_144.jpg</td>\n",
       "      <td>a cat standing on a wooden floor next to a glass</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[{'bbox': [91.0, 13.0, 408.0, 345.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_10.jpg</td>\n",
       "      <td>a cat laying on a bed</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[{'bbox': [-1.0, 105.0, 287.0, 395.0], 'confid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_155.jpg</td>\n",
       "      <td>a cat yawning on a couch</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[{'bbox': [58.0, 43.0, 502.0, 285.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_101.jpg</td>\n",
       "      <td>a cat with green eyes is laying on a green bla...</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[{'bbox': [55.0, 32.0, 313.0, 204.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_156.jpg</td>\n",
       "      <td>a cat is sitting on a couch</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[{'bbox': [11.0, 26.0, 444.0, 295.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_99.jpg</td>\n",
       "      <td>a small dog standing on a sidewalk</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[{'bbox': [183.0, 46.0, 264.0, 272.0], 'confid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7271</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_56.jpg</td>\n",
       "      <td>a small dog is standing in the grass</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[{'bbox': [52.0, -0.0, 448.0, 351.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7272</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_97.jpg</td>\n",
       "      <td>a small dog is standing on a rock</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[{'bbox': [0.0, 66.0, 320.0, 262.0], 'confiden...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7273</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_58.jpg</td>\n",
       "      <td>a small black and brown puppy is being held by...</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[{'bbox': [2.0, 0.0, 396.0, 374.0], 'confidenc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7274</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_6.jpg</td>\n",
       "      <td>a small black and gray dog standing in the grass</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[{'bbox': [137.0, 79.0, 216.0, 152.0], 'confid...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7275 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             filename  \\\n",
       "0           oxford-iiit-pet/images/Abyssinian_144.jpg   \n",
       "1            oxford-iiit-pet/images/Abyssinian_10.jpg   \n",
       "2           oxford-iiit-pet/images/Abyssinian_155.jpg   \n",
       "3           oxford-iiit-pet/images/Abyssinian_101.jpg   \n",
       "4           oxford-iiit-pet/images/Abyssinian_156.jpg   \n",
       "...                                               ...   \n",
       "7270  oxford-iiit-pet/images/yorkshire_terrier_99.jpg   \n",
       "7271  oxford-iiit-pet/images/yorkshire_terrier_56.jpg   \n",
       "7272  oxford-iiit-pet/images/yorkshire_terrier_97.jpg   \n",
       "7273  oxford-iiit-pet/images/yorkshire_terrier_58.jpg   \n",
       "7274   oxford-iiit-pet/images/yorkshire_terrier_6.jpg   \n",
       "\n",
       "                                                caption  label  \\\n",
       "0      a cat standing on a wooden floor next to a glass  [cat]   \n",
       "1                                 a cat laying on a bed  [cat]   \n",
       "2                              a cat yawning on a couch  [cat]   \n",
       "3     a cat with green eyes is laying on a green bla...  [cat]   \n",
       "4                           a cat is sitting on a couch  [cat]   \n",
       "...                                                 ...    ...   \n",
       "7270                 a small dog standing on a sidewalk  [dog]   \n",
       "7271               a small dog is standing in the grass  [dog]   \n",
       "7272                  a small dog is standing on a rock  [dog]   \n",
       "7273  a small black and brown puppy is being held by...  [dog]   \n",
       "7274   a small black and gray dog standing in the grass  [dog]   \n",
       "\n",
       "                                                 labels  \n",
       "0     [{'bbox': [91.0, 13.0, 408.0, 345.0], 'confide...  \n",
       "1     [{'bbox': [-1.0, 105.0, 287.0, 395.0], 'confid...  \n",
       "2     [{'bbox': [58.0, 43.0, 502.0, 285.0], 'confide...  \n",
       "3     [{'bbox': [55.0, 32.0, 313.0, 204.0], 'confide...  \n",
       "4     [{'bbox': [11.0, 26.0, 444.0, 295.0], 'confide...  \n",
       "...                                                 ...  \n",
       "7270  [{'bbox': [183.0, 46.0, 264.0, 272.0], 'confid...  \n",
       "7271  [{'bbox': [52.0, -0.0, 448.0, 351.0], 'confide...  \n",
       "7272  [{'bbox': [0.0, 66.0, 320.0, 262.0], 'confiden...  \n",
       "7273  [{'bbox': [2.0, 0.0, 396.0, 374.0], 'confidenc...  \n",
       "7274  [{'bbox': [137.0, 79.0, 216.0, 152.0], 'confid...  \n",
       "\n",
       "[7275 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge with df_obj on filename\n",
    "df = df.merge(df_obj, on='filename')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_144.jpg</td>\n",
       "      <td>a cat standing on a wooden floor next to a glass</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[{'bbox': [91.0, 13.0, 408.0, 345.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_10.jpg</td>\n",
       "      <td>a cat laying on a bed</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[{'bbox': [-1.0, 105.0, 287.0, 395.0], 'confid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_155.jpg</td>\n",
       "      <td>a cat yawning on a couch</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[{'bbox': [58.0, 43.0, 502.0, 285.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_101.jpg</td>\n",
       "      <td>a cat with green eyes is laying on a green bla...</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[{'bbox': [55.0, 32.0, 313.0, 204.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oxford-iiit-pet/images/Abyssinian_156.jpg</td>\n",
       "      <td>a cat is sitting on a couch</td>\n",
       "      <td>[cat]</td>\n",
       "      <td>[{'bbox': [11.0, 26.0, 444.0, 295.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_99.jpg</td>\n",
       "      <td>a small dog standing on a sidewalk</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[{'bbox': [183.0, 46.0, 264.0, 272.0], 'confid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7271</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_56.jpg</td>\n",
       "      <td>a small dog is standing in the grass</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[{'bbox': [52.0, -0.0, 448.0, 351.0], 'confide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7272</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_97.jpg</td>\n",
       "      <td>a small dog is standing on a rock</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[{'bbox': [0.0, 66.0, 320.0, 262.0], 'confiden...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7273</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_58.jpg</td>\n",
       "      <td>a small black and brown puppy is being held by...</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[{'bbox': [2.0, 0.0, 396.0, 374.0], 'confidenc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7274</th>\n",
       "      <td>oxford-iiit-pet/images/yorkshire_terrier_6.jpg</td>\n",
       "      <td>a small black and gray dog standing in the grass</td>\n",
       "      <td>[dog]</td>\n",
       "      <td>[{'bbox': [137.0, 79.0, 216.0, 152.0], 'confid...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7275 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             filename  \\\n",
       "0           oxford-iiit-pet/images/Abyssinian_144.jpg   \n",
       "1            oxford-iiit-pet/images/Abyssinian_10.jpg   \n",
       "2           oxford-iiit-pet/images/Abyssinian_155.jpg   \n",
       "3           oxford-iiit-pet/images/Abyssinian_101.jpg   \n",
       "4           oxford-iiit-pet/images/Abyssinian_156.jpg   \n",
       "...                                               ...   \n",
       "7270  oxford-iiit-pet/images/yorkshire_terrier_99.jpg   \n",
       "7271  oxford-iiit-pet/images/yorkshire_terrier_56.jpg   \n",
       "7272  oxford-iiit-pet/images/yorkshire_terrier_97.jpg   \n",
       "7273  oxford-iiit-pet/images/yorkshire_terrier_58.jpg   \n",
       "7274   oxford-iiit-pet/images/yorkshire_terrier_6.jpg   \n",
       "\n",
       "                                                caption image_labels  \\\n",
       "0      a cat standing on a wooden floor next to a glass        [cat]   \n",
       "1                                 a cat laying on a bed        [cat]   \n",
       "2                              a cat yawning on a couch        [cat]   \n",
       "3     a cat with green eyes is laying on a green bla...        [cat]   \n",
       "4                           a cat is sitting on a couch        [cat]   \n",
       "...                                                 ...          ...   \n",
       "7270                 a small dog standing on a sidewalk        [dog]   \n",
       "7271               a small dog is standing in the grass        [dog]   \n",
       "7272                  a small dog is standing on a rock        [dog]   \n",
       "7273  a small black and brown puppy is being held by...        [dog]   \n",
       "7274   a small black and gray dog standing in the grass        [dog]   \n",
       "\n",
       "                                                objects  \n",
       "0     [{'bbox': [91.0, 13.0, 408.0, 345.0], 'confide...  \n",
       "1     [{'bbox': [-1.0, 105.0, 287.0, 395.0], 'confid...  \n",
       "2     [{'bbox': [58.0, 43.0, 502.0, 285.0], 'confide...  \n",
       "3     [{'bbox': [55.0, 32.0, 313.0, 204.0], 'confide...  \n",
       "4     [{'bbox': [11.0, 26.0, 444.0, 295.0], 'confide...  \n",
       "...                                                 ...  \n",
       "7270  [{'bbox': [183.0, 46.0, 264.0, 272.0], 'confid...  \n",
       "7271  [{'bbox': [52.0, -0.0, 448.0, 351.0], 'confide...  \n",
       "7272  [{'bbox': [0.0, 66.0, 320.0, 262.0], 'confiden...  \n",
       "7273  [{'bbox': [2.0, 0.0, 396.0, 374.0], 'confidenc...  \n",
       "7274  [{'bbox': [137.0, 79.0, 216.0, 152.0], 'confid...  \n",
       "\n",
       "[7275 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename label to image_label, class_id to image_class_id\n",
    "df.rename(columns={'label': 'image_labels', 'labels': 'objects'}, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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