08-GCP-data-manipulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GCP Data Manipulation\n",
    "\n",
    "This notebook is dedicated to shifting data around my various GCP buckets.\n",
    "\n",
    "I'd like the following my bucket:\n",
    "* 1 folder with all the data (train/valid/test & val_test, a combination of valid + test)\n",
    "* 1 folder with 10% of the data (train/test) - this should have the same distribution as the whole data\n",
    "\n",
    "**Note:** I've combined the validation and test folders in `/val_test/`.\n",
    "\n",
    "Mainly working with this resource: https://cloud.google.com/storage/docs/gsutil/commands/cp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['0213d790e89c9f2a.jpg', 'ea585f0a157679db.jpg', '8f8c68e58073738a.jpg'],\n",
       " 1730,\n",
       " 34835)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "# Get all filepaths\n",
    "train_imgs = os.listdir(\"train\")\n",
    "#val_imgs = os.listdir(\"validation\")\n",
    "test_imgs = os.listdir(\"val_test\")\n",
    "train_imgs[:3],len(test_imgs), len(train_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34835, 859, 2112)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_imgs), len(val_imgs), len(test_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make folder which combines validation and test images (val images only have 800, test images only have 2000)\n",
    "os.mkdir(\"val_test\")\n",
    "!cp -a validation/. val_test/\n",
    "!cp -a test/. val_test/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2971"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check length of combined validation and test images\n",
    "val_test_imgs = os.listdir(\"val_test\")\n",
    "print(len(val_test_imgs) == len(val_imgs + test_imgs))\n",
    "len(val_test_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use filepaths to move 10% of val_test and train (at random) into subset folders\n",
    "os.mkdir(\"train_10\")\n",
    "os.mkdir(\"val_test_10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3483, 297)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from shutil import copy\n",
    "import random\n",
    "\n",
    "# Get random 10% of trian/val_test image paths\n",
    "train_10_imgs = random.sample(train_imgs, len(train_imgs)//10)\n",
    "val_test_10_imgs = random.sample(val_test_imgs, len(val_test_imgs)//10)\n",
    "len(train_10_imgs), len(val_test_10_imgs)"
   ]
  },
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   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Copying: 8230cec77b1e6673.jpg\n"
     ]
    }
   ],
   "source": [
    "# Move 10% of train images into their own folder\n",
    "for img in train_10_imgs:\n",
    "    #print(f\"Copying: {img}\")\n",
    "    copy(\"train/\"+img, \"train_10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Move 10% of val_test images into their own folder\n",
    "for img in val_test_10_imgs:\n",
    "    #print(f\"Copying: {img}\")\n",
    "    copy(\"val_test/\"+img, \"val_test_10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3483, 297)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(\"train_10\")), len(os.listdir(\"val_test_10\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Check to see if train_10 matches the images in the folder\n",
    "print(sorted(os.listdir(\"train_10\")) == sorted(train_10_imgs))\n",
    "print(sorted(os.listdir(\"val_test_10\")) == sorted(val_test_10_imgs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Move images to GCP\n",
    "Now some data manipulation has happened, time to move all image folders to a GCP bucket for later use.\n",
    "\n",
    "Example GCP code: `gsutil cp *.txt gs://my-bucket` (moves all `.txt` files in current directory to `my-bucket` on GCP.\n",
    "\n",
    "When copying large amounts of files, I should use: `gsutil -m cp -r dir gs://my-bucket`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying file://val_test_10/9ee4d07829846ff3.jpg [Content-Type=image/jpeg]...\n",
      "Copying file://val_test_10/614a58f39595275a.jpg [Content-Type=image/jpeg]...\n",
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     ]
    }
   ],
   "source": [
    "# # 10% of training data\n",
    "# !gsutil -m cp -r train_10 gs://airbnb-amenity-detection/open-images-data\n",
    "\n",
    "# # Training data\n",
    "# !gsutil -m cp -r train gs://airbnb-amenity-detection/open-images-data\n",
    "\n",
    "# # Combined validation and test sets    \n",
    "# !gsutil -m cp -r val_test gs://airbnb-amenity-detection/open-images-data\n",
    "    \n",
    "# # 10% of combined validation and test sets\n",
    "# !gsutil -m cp -r val_test_10 gs://airbnb-amenity-detection/open-images-data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check distributions of images\n",
    "Figure out the distributions of different classes of images in my datasets.\n",
    "* Do they match Airbnb's distributions?\n",
    "* Are the train/test splits (the 10%) representative of what's going on in the full datasets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from preprocessing import format_annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classes of amenities Airbnb mostly cares about\n",
    "subset = ['Toilet',\n",
    "         'Swimming pool',\n",
    "         'Bed',\n",
    "         'Billiard table',\n",
    "         'Sink',\n",
    "         'Fountain',\n",
    "         'Oven',\n",
    "         'Ceiling fan',\n",
    "         'Television',\n",
    "         'Microwave oven',\n",
    "         'Gas stove',\n",
    "         'Refrigerator',\n",
    "         'Kitchen & dining room table',\n",
    "         'Washing machine',\n",
    "         'Bathtub',\n",
    "         'Stairs',\n",
    "         'Fireplace',\n",
    "         'Pillow',\n",
    "         'Mirror',\n",
    "         'Shower',\n",
    "         'Couch',\n",
    "         'Countertop',\n",
    "         'Coffeemaker',\n",
    "         'Dishwasher',\n",
    "         'Sofa bed',\n",
    "         'Tree house',\n",
    "         'Towel',\n",
    "         'Porch',\n",
    "         'Wine rack',\n",
    "         'Jacuzzi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "                 ImageID  Source  LabelName  Confidence      XMin      XMax  \\\n",
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       "\n",
       "            YMin      YMax  IsOccluded  IsTruncated  IsGroupOf  IsDepiction  \\\n",
       "281915  0.470067  1.000000           0            0          0            0   \n",
       "154433  0.427083  0.997917           1            1          0            0   \n",
       "167727  0.545833  1.000000           0            1          0            0   \n",
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       "75788   0.251748  1.000000           0            1          0            0   \n",
       "\n",
       "        IsInside ClassName  ClassID  \n",
       "281915         0   Bathtub        0  \n",
       "154433         0   Bathtub        0  \n",
       "167727         0   Bathtub        0  \n",
       "257033         0   Bathtub        0  \n",
       "75788          0   Bathtub        0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_annots = format_annotations(image_folder=\"validation\", \n",
    "                                  annotation_file=\"validation-annotations-bbox.csv\", \n",
    "                                  target_classes=subset)\n",
    "valid_annots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "valid_annots.ClassName.value_counts().sort_values().plot(kind=\"barh\", title=\"Validation classes distribution\", figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_annots.to_csv(\"subset-valid-annotations.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>ImageID</th>\n",
       "      <th>Source</th>\n",
       "      <th>LabelName</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>XMin</th>\n",
       "      <th>XMax</th>\n",
       "      <th>YMin</th>\n",
       "      <th>YMax</th>\n",
       "      <th>IsOccluded</th>\n",
       "      <th>IsTruncated</th>\n",
       "      <th>IsGroupOf</th>\n",
       "      <th>IsDepiction</th>\n",
       "      <th>IsInside</th>\n",
       "      <th>ClassName</th>\n",
       "      <th>ClassID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3454445</th>\n",
       "      <td>38bbd240e3dd6d2b</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.997917</td>\n",
       "      <td>0.362500</td>\n",
       "      <td>0.998438</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9051090</th>\n",
       "      <td>9a2f2850b2b6895e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.823750</td>\n",
       "      <td>0.382500</td>\n",
       "      <td>0.999167</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6401293</th>\n",
       "      <td>6be60d5453217a0e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.999375</td>\n",
       "      <td>0.361163</td>\n",
       "      <td>0.917448</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1158763</th>\n",
       "      <td>1123ab5e8d902bcb</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.999375</td>\n",
       "      <td>0.391714</td>\n",
       "      <td>0.999058</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533268</th>\n",
       "      <td>06e19a68abf5dde4</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.515625</td>\n",
       "      <td>0.930625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ImageID  Source  LabelName  Confidence  XMin      XMax  \\\n",
       "3454445  38bbd240e3dd6d2b  xclick  /m/03dnzn           1  0.00  0.997917   \n",
       "9051090  9a2f2850b2b6895e  xclick  /m/03dnzn           1  0.00  0.823750   \n",
       "6401293  6be60d5453217a0e  xclick  /m/03dnzn           1  0.00  0.999375   \n",
       "1158763  1123ab5e8d902bcb  xclick  /m/03dnzn           1  0.00  0.999375   \n",
       "533268   06e19a68abf5dde4  xclick  /m/03dnzn           1  0.24  0.630000   \n",
       "\n",
       "             YMin      YMax  IsOccluded  IsTruncated  IsGroupOf  IsDepiction  \\\n",
       "3454445  0.362500  0.998438           1            1          0            0   \n",
       "9051090  0.382500  0.999167           1            1          0            0   \n",
       "6401293  0.361163  0.917448           1            1          0            0   \n",
       "1158763  0.391714  0.999058           1            1          0            0   \n",
       "533268   0.515625  0.930625           1            0          0            0   \n",
       "\n",
       "         IsInside ClassName  ClassID  \n",
       "3454445         0   Bathtub        0  \n",
       "9051090         0   Bathtub        0  \n",
       "6401293         0   Bathtub        0  \n",
       "1158763         0   Bathtub        0  \n",
       "533268          0   Bathtub        0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_annots = format_annotations(image_folder=\"train\", \n",
    "                                  annotation_file=\"train-annotations-bbox.csv\", \n",
    "                                  target_classes=subset)\n",
    "train_annots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_annots.ClassName.value_counts().sort_values().plot(kind=\"barh\", title=\"Train classes distribution\", figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stairs                         5981\n",
       "Couch                          4259\n",
       "Swimming pool                  3881\n",
       "Porch                          3854\n",
       "Television                     3789\n",
       "Fountain                       3691\n",
       "Bed                            3563\n",
       "Pillow                         3508\n",
       "Countertop                     3113\n",
       "Kitchen & dining room table    2127\n",
       "Sink                           1648\n",
       "Mirror                         1572\n",
       "Sofa bed                       1501\n",
       "Toilet                         1099\n",
       "Billiard table                  912\n",
       "Fireplace                       711\n",
       "Washing machine                 655\n",
       "Oven                            637\n",
       "Refrigerator                    592\n",
       "Bathtub                         545\n",
       "Gas stove                       526\n",
       "Microwave oven                  485\n",
       "Ceiling fan                     478\n",
       "Towel                           338\n",
       "Coffeemaker                     323\n",
       "Wine rack                       254\n",
       "Shower                          235\n",
       "Tree house                      110\n",
       "Jacuzzi                         103\n",
       "Dishwasher                       92\n",
       "Name: ClassName, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_annots.ClassName.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save formatted and subset of annotations to CSV\n",
    "train_annots.to_csv(\"subset-train-annotations.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>ImageID</th>\n",
       "      <th>Source</th>\n",
       "      <th>LabelName</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>XMin</th>\n",
       "      <th>XMax</th>\n",
       "      <th>YMin</th>\n",
       "      <th>YMax</th>\n",
       "      <th>IsOccluded</th>\n",
       "      <th>IsTruncated</th>\n",
       "      <th>IsGroupOf</th>\n",
       "      <th>IsDepiction</th>\n",
       "      <th>IsInside</th>\n",
       "      <th>ClassName</th>\n",
       "      <th>ClassID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>431305</th>\n",
       "      <td>ad2e3b563bf9eef5</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611091</th>\n",
       "      <td>f9f6d0ce7325d286</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.012309</td>\n",
       "      <td>0.933887</td>\n",
       "      <td>0.781339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400753</th>\n",
       "      <td>a108e2b6835d757c</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.999913</td>\n",
       "      <td>0.142911</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388381</th>\n",
       "      <td>9bbe5902409bfdca</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.641643</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.539640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56610</th>\n",
       "      <td>1574c030df3accac</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.089474</td>\n",
       "      <td>0.423140</td>\n",
       "      <td>0.450507</td>\n",
       "      <td>0.830147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ImageID    Source  LabelName  Confidence      XMin      XMax  \\\n",
       "431305  ad2e3b563bf9eef5  freeform  /m/03dnzn           1  0.000035  1.000000   \n",
       "611091  f9f6d0ce7325d286  freeform  /m/03dnzn           1  0.012309  0.933887   \n",
       "400753  a108e2b6835d757c  freeform  /m/03dnzn           1  0.000104  0.999913   \n",
       "388381  9bbe5902409bfdca  freeform  /m/03dnzn           1  0.641643  1.000000   \n",
       "56610   1574c030df3accac  freeform  /m/03dnzn           1  0.089474  0.423140   \n",
       "\n",
       "            YMin      YMax  IsOccluded  IsTruncated  IsGroupOf  IsDepiction  \\\n",
       "431305  0.000007  1.000000           0            0          0            0   \n",
       "611091  0.781339  1.000000           0            0          0            0   \n",
       "400753  0.142911  0.999998           0            0          0            0   \n",
       "388381  0.539640  1.000000           0            1          0            0   \n",
       "56610   0.450507  0.830147           0            0          0            0   \n",
       "\n",
       "        IsInside ClassName  ClassID  \n",
       "431305         0   Bathtub        0  \n",
       "611091         0   Bathtub        0  \n",
       "400753         0   Bathtub        0  \n",
       "388381         0   Bathtub        0  \n",
       "56610          0   Bathtub        0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_annots = format_annotations(image_folder=\"test\", \n",
    "                                     annotation_file=\"test-annotations-bbox.csv\", \n",
    "                                     target_classes=subset)\n",
    "test_annots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_annots.ClassName.value_counts().sort_values().plot(kind=\"barh\", title=\"Test set classes distribution\", figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Swimming pool                  354\n",
       "Bed                            328\n",
       "Pillow                         264\n",
       "Kitchen & dining room table    157\n",
       "Sofa bed                       150\n",
       "Countertop                     143\n",
       "Television                     124\n",
       "Stairs                         122\n",
       "Washing machine                121\n",
       "Sink                           119\n",
       "Couch                          104\n",
       "Porch                          100\n",
       "Billiard table                  85\n",
       "Mirror                          84\n",
       "Refrigerator                    82\n",
       "Fountain                        63\n",
       "Fireplace                       62\n",
       "Toilet                          61\n",
       "Bathtub                         59\n",
       "Oven                            53\n",
       "Coffeemaker                     50\n",
       "Gas stove                       49\n",
       "Microwave oven                  46\n",
       "Ceiling fan                     40\n",
       "Shower                          32\n",
       "Towel                           29\n",
       "Jacuzzi                         28\n",
       "Tree house                      19\n",
       "Wine rack                       18\n",
       "Dishwasher                       3\n",
       "Name: ClassName, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_annots.ClassName.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10% of training data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>ImageID</th>\n",
       "      <th>Source</th>\n",
       "      <th>LabelName</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>XMin</th>\n",
       "      <th>XMax</th>\n",
       "      <th>YMin</th>\n",
       "      <th>YMax</th>\n",
       "      <th>IsOccluded</th>\n",
       "      <th>IsTruncated</th>\n",
       "      <th>IsGroupOf</th>\n",
       "      <th>IsDepiction</th>\n",
       "      <th>IsInside</th>\n",
       "      <th>ClassName</th>\n",
       "      <th>ClassID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1158763</th>\n",
       "      <td>1123ab5e8d902bcb</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.999375</td>\n",
       "      <td>0.391714</td>\n",
       "      <td>0.999058</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11151925</th>\n",
       "      <td>bf1659a6948fd6f6</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.139167</td>\n",
       "      <td>0.999167</td>\n",
       "      <td>0.344375</td>\n",
       "      <td>0.999375</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10509875</th>\n",
       "      <td>b3da48968f56ccbe</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.999375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.999167</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270645</th>\n",
       "      <td>13067d0ea6ee89f8</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.109375</td>\n",
       "      <td>0.999375</td>\n",
       "      <td>0.196813</td>\n",
       "      <td>0.999063</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9310493</th>\n",
       "      <td>9ec96a976764024a</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.999375</td>\n",
       "      <td>0.446667</td>\n",
       "      <td>0.999167</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ImageID  Source  LabelName  Confidence      XMin      XMax  \\\n",
       "1158763   1123ab5e8d902bcb  xclick  /m/03dnzn           1  0.000000  0.999375   \n",
       "11151925  bf1659a6948fd6f6  xclick  /m/03dnzn           1  0.139167  0.999167   \n",
       "10509875  b3da48968f56ccbe  xclick  /m/03dnzn           1  0.000000  0.999375   \n",
       "1270645   13067d0ea6ee89f8  xclick  /m/03dnzn           1  0.109375  0.999375   \n",
       "9310493   9ec96a976764024a  xclick  /m/03dnzn           1  0.000000  0.999375   \n",
       "\n",
       "              YMin      YMax  IsOccluded  IsTruncated  IsGroupOf  IsDepiction  \\\n",
       "1158763   0.391714  0.999058           1            1          0            0   \n",
       "11151925  0.344375  0.999375           1            1          0            0   \n",
       "10509875  0.000000  0.999167           1            1          0            0   \n",
       "1270645   0.196813  0.999063           1            1          0            0   \n",
       "9310493   0.446667  0.999167           1            1          0            0   \n",
       "\n",
       "          IsInside ClassName  ClassID  \n",
       "1158763          0   Bathtub        0  \n",
       "11151925         0   Bathtub        0  \n",
       "10509875         0   Bathtub        0  \n",
       "1270645          0   Bathtub        0  \n",
       "9310493          0   Bathtub        0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_10_annots = format_annotations(image_folder=\"train_10\", \n",
    "                                     annotation_file=\"train-annotations-bbox.csv\", \n",
    "                                     target_classes=subset)\n",
    "train_10_annots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_10_annots.ClassName.value_counts().sort_values().plot(kind=\"barh\", title=\"Random 10% of train classes distribution\", figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stairs                         561\n",
       "Pillow                         433\n",
       "Couch                          419\n",
       "Television                     392\n",
       "Porch                          379\n",
       "Bed                            375\n",
       "Swimming pool                  370\n",
       "Fountain                       357\n",
       "Countertop                     321\n",
       "Kitchen & dining room table    243\n",
       "Sink                           154\n",
       "Mirror                         136\n",
       "Sofa bed                       134\n",
       "Toilet                         106\n",
       "Billiard table                  85\n",
       "Oven                            64\n",
       "Bathtub                         60\n",
       "Washing machine                 59\n",
       "Fireplace                       59\n",
       "Refrigerator                    59\n",
       "Ceiling fan                     53\n",
       "Microwave oven                  47\n",
       "Coffeemaker                     47\n",
       "Wine rack                       45\n",
       "Gas stove                       41\n",
       "Towel                           31\n",
       "Shower                          30\n",
       "Jacuzzi                         14\n",
       "Tree house                      12\n",
       "Dishwasher                       8\n",
       "Name: ClassName, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_10_annots.ClassName.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save train_10 annotations to CSV\n",
    "train_10_annots.to_csv(\"subset-train-10-annotations.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine Test & Validation Labels\n",
    "\n",
    "Since I'm using a data split called val_test (combination of validation and test labels), I need to combine them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>ImageID</th>\n",
       "      <th>Source</th>\n",
       "      <th>LabelName</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>XMin</th>\n",
       "      <th>XMax</th>\n",
       "      <th>YMin</th>\n",
       "      <th>YMax</th>\n",
       "      <th>IsOccluded</th>\n",
       "      <th>IsTruncated</th>\n",
       "      <th>IsGroupOf</th>\n",
       "      <th>IsDepiction</th>\n",
       "      <th>IsInside</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001eeaf4aed83f9</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0cmf2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.022673</td>\n",
       "      <td>0.964200</td>\n",
       "      <td>0.071038</td>\n",
       "      <td>0.800546</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000595fe6fee6369</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/02wbm</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000595fe6fee6369</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/02xwb</td>\n",
       "      <td>1</td>\n",
       "      <td>0.141384</td>\n",
       "      <td>0.179676</td>\n",
       "      <td>0.676275</td>\n",
       "      <td>0.731707</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000595fe6fee6369</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/02xwb</td>\n",
       "      <td>1</td>\n",
       "      <td>0.213549</td>\n",
       "      <td>0.253314</td>\n",
       "      <td>0.299335</td>\n",
       "      <td>0.354767</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000595fe6fee6369</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/02xwb</td>\n",
       "      <td>1</td>\n",
       "      <td>0.232695</td>\n",
       "      <td>0.288660</td>\n",
       "      <td>0.490022</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ImageID  Source LabelName  Confidence      XMin      XMax  \\\n",
       "0  0001eeaf4aed83f9  xclick  /m/0cmf2           1  0.022673  0.964200   \n",
       "1  000595fe6fee6369  xclick  /m/02wbm           1  0.000000  1.000000   \n",
       "2  000595fe6fee6369  xclick  /m/02xwb           1  0.141384  0.179676   \n",
       "3  000595fe6fee6369  xclick  /m/02xwb           1  0.213549  0.253314   \n",
       "4  000595fe6fee6369  xclick  /m/02xwb           1  0.232695  0.288660   \n",
       "\n",
       "       YMin      YMax  IsOccluded  IsTruncated  IsGroupOf  IsDepiction  \\\n",
       "0  0.071038  0.800546           0            0          0            0   \n",
       "1  0.000000  1.000000           0            0          1            0   \n",
       "2  0.676275  0.731707           0            0          0            0   \n",
       "3  0.299335  0.354767           1            0          0            0   \n",
       "4  0.490022  0.545455           1            0          0            0   \n",
       "\n",
       "   IsInside  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_annotations = pd.read_csv(\"validation-annotations-bbox.csv\")\n",
    "test_annotations = pd.read_csv(\"test-annotations-bbox.csv\")\n",
    "val_annotations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(929262, 929262)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine test & validation annotations into 1 file\n",
    "val_test_annotations = val_annotations.append(test_annotations)\n",
    "len(val_test_annotations), len(val_annotations) + len(test_annotations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to CSV for later use\n",
    "# val_test_annotations.to_csv(\"val-test-annotations.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>ImageID</th>\n",
       "      <th>Source</th>\n",
       "      <th>LabelName</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>XMin</th>\n",
       "      <th>XMax</th>\n",
       "      <th>YMin</th>\n",
       "      <th>YMax</th>\n",
       "      <th>IsOccluded</th>\n",
       "      <th>IsTruncated</th>\n",
       "      <th>IsGroupOf</th>\n",
       "      <th>IsDepiction</th>\n",
       "      <th>IsInside</th>\n",
       "      <th>ClassName</th>\n",
       "      <th>ClassID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>788026</th>\n",
       "      <td>c4b1d21e32c98e4e</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011874</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.328250</td>\n",
       "      <td>0.778549</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752419</th>\n",
       "      <td>b512048868e6318e</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>0.999713</td>\n",
       "      <td>0.029815</td>\n",
       "      <td>0.999462</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692361</th>\n",
       "      <td>9bbe5902409bfdca</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.641643</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.539640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142263</th>\n",
       "      <td>789cc0283c0e18af</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.039711</td>\n",
       "      <td>0.949458</td>\n",
       "      <td>0.119134</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501580</th>\n",
       "      <td>4c1649f8d5b677d3</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.984500</td>\n",
       "      <td>0.319811</td>\n",
       "      <td>0.999994</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117526</th>\n",
       "      <td>62fd8411facd03db</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0271qf7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.047128</td>\n",
       "      <td>0.372607</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.995575</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Wine rack</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117527</th>\n",
       "      <td>62fd8411facd03db</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0271qf7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.374080</td>\n",
       "      <td>0.533137</td>\n",
       "      <td>0.632743</td>\n",
       "      <td>0.995575</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Wine rack</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204890</th>\n",
       "      <td>abe9ff8763cdcc5d</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0271qf7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Wine rack</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190452</th>\n",
       "      <td>a0630cc0bccb0ff4</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0271qf7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Wine rack</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656131</th>\n",
       "      <td>8ca2aaaf1c23b66c</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/0271qf7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.029686</td>\n",
       "      <td>0.966679</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.999937</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Wine rack</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4212 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ImageID    Source   LabelName  Confidence      XMin  \\\n",
       "788026  c4b1d21e32c98e4e  freeform   /m/03dnzn           1  0.011874   \n",
       "752419  b512048868e6318e  freeform   /m/03dnzn           1  0.000303   \n",
       "692361  9bbe5902409bfdca  freeform   /m/03dnzn           1  0.641643   \n",
       "142263  789cc0283c0e18af    xclick   /m/03dnzn           1  0.039711   \n",
       "501580  4c1649f8d5b677d3  freeform   /m/03dnzn           1  0.000000   \n",
       "...                  ...       ...         ...         ...       ...   \n",
       "117526  62fd8411facd03db    xclick  /m/0271qf7           1  0.047128   \n",
       "117527  62fd8411facd03db    xclick  /m/0271qf7           1  0.374080   \n",
       "204890  abe9ff8763cdcc5d    xclick  /m/0271qf7           1  0.000000   \n",
       "190452  a0630cc0bccb0ff4    xclick  /m/0271qf7           1  0.000000   \n",
       "656131  8ca2aaaf1c23b66c  freeform  /m/0271qf7           1  0.029686   \n",
       "\n",
       "            XMax      YMin      YMax  IsOccluded  IsTruncated  IsGroupOf  \\\n",
       "788026  1.000000  0.328250  0.778549           0            0          0   \n",
       "752419  0.999713  0.029815  0.999462           0            1          0   \n",
       "692361  1.000000  0.539640  1.000000           0            1          0   \n",
       "142263  0.949458  0.119134  1.000000           0            0          0   \n",
       "501580  0.984500  0.319811  0.999994           0            1          0   \n",
       "...          ...       ...       ...         ...          ...        ...   \n",
       "117526  0.372607  0.000000  0.995575           0            1          1   \n",
       "117527  0.533137  0.632743  0.995575           0            0          1   \n",
       "204890  1.000000  0.000000  1.000000           0            0          0   \n",
       "190452  1.000000  0.000000  1.000000           0            1          0   \n",
       "656131  0.966679  0.000000  0.999937           0            0          0   \n",
       "\n",
       "        IsDepiction  IsInside  ClassName  ClassID  \n",
       "788026            0         0    Bathtub        0  \n",
       "752419            0         0    Bathtub        0  \n",
       "692361            0         0    Bathtub        0  \n",
       "142263            0         0    Bathtub        0  \n",
       "501580            0         0    Bathtub        0  \n",
       "...             ...       ...        ...      ...  \n",
       "117526            0         0  Wine rack       29  \n",
       "117527            0         0  Wine rack       29  \n",
       "204890            0         0  Wine rack       29  \n",
       "190452            0         0  Wine rack       29  \n",
       "656131            0         0  Wine rack       29  \n",
       "\n",
       "[4212 rows x 15 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_test_annots = format_annotations(image_folder=\"val_test\",\n",
    "                                     annotation_file=\"val-test-annotations.csv\",\n",
    "                                     target_classes=subset)\n",
    "val_test_annots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_test_annots.ClassName.value_counts().sort_values().plot(kind=\"barh\", title=\"Val-test classes distribution\", figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Swimming pool                  515\n",
       "Bed                            453\n",
       "Pillow                         340\n",
       "Kitchen & dining room table    233\n",
       "Countertop                     211\n",
       "Sofa bed                       211\n",
       "Sink                           176\n",
       "Television                     168\n",
       "Stairs                         167\n",
       "Couch                          165\n",
       "Washing machine                161\n",
       "Porch                          152\n",
       "Mirror                         117\n",
       "Billiard table                 117\n",
       "Refrigerator                   108\n",
       "Fireplace                      103\n",
       "Toilet                          98\n",
       "Oven                            89\n",
       "Fountain                        87\n",
       "Bathtub                         77\n",
       "Microwave oven                  76\n",
       "Gas stove                       72\n",
       "Coffeemaker                     71\n",
       "Ceiling fan                     51\n",
       "Jacuzzi                         44\n",
       "Shower                          41\n",
       "Towel                           38\n",
       "Wine rack                       35\n",
       "Tree house                      30\n",
       "Dishwasher                       6\n",
       "Name: ClassName, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_test_annots.ClassName.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample 10% of the data & check distributions \n",
    "val_test_10_annots = val_test_annots.sample(frac=0.1)\n",
    "val_test_10_annots.ClassName.value_counts().sort_values().plot(kind=\"barh\", title=\"Val-test 10% classes distribution\", figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['1296dd4bddd64817',\n",
       "  'af951685c7c5278f',\n",
       "  'c6e690a9fc7f5ef8',\n",
       "  '757a1631131f4ba3',\n",
       "  '6a9ce7a7b5f39787',\n",
       "  'dd06af30e45e4b19',\n",
       "  '771a44e8d370d94a',\n",
       "  '0f731adcd7458c2b',\n",
       "  '409019c958741906',\n",
       "  'a427ff0d310ad27a'],\n",
       " 401)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save val_test_10_image_ids to list\n",
    "val_test_10_image_ids = list(set(val_test_10_annots.ImageID.tolist()))\n",
    "val_test_10_image_ids[:10], len(val_test_10_image_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Move val_test_10_image_ids images to val_test_10 (replace the ones which are there)\n",
    "# from shutil import copy\n",
    "\n",
    "# # Move 10% of val_test images into their own folder\n",
    "# for img in val_test_10_image_ids:\n",
    "#     #print(f\"Copying: {img}\")\n",
    "#     copy(\"val_test/\"+img+\".jpg\", \"val_test_10\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10% of val-test image distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>ImageID</th>\n",
       "      <th>Source</th>\n",
       "      <th>LabelName</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>XMin</th>\n",
       "      <th>XMax</th>\n",
       "      <th>YMin</th>\n",
       "      <th>YMax</th>\n",
       "      <th>IsOccluded</th>\n",
       "      <th>IsTruncated</th>\n",
       "      <th>IsGroupOf</th>\n",
       "      <th>IsDepiction</th>\n",
       "      <th>IsInside</th>\n",
       "      <th>ClassName</th>\n",
       "      <th>ClassID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>501580</th>\n",
       "      <td>4c1649f8d5b677d3</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.984500</td>\n",
       "      <td>0.319811</td>\n",
       "      <td>0.999994</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635183</th>\n",
       "      <td>83f459662fc4fb12</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009929</td>\n",
       "      <td>0.999955</td>\n",
       "      <td>0.430541</td>\n",
       "      <td>0.999577</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915071</th>\n",
       "      <td>f9f6d0ce7325d286</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.012309</td>\n",
       "      <td>0.933887</td>\n",
       "      <td>0.781339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879031</th>\n",
       "      <td>eb1abf6933be7682</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.301573</td>\n",
       "      <td>0.996929</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784065</th>\n",
       "      <td>c2ddf0678c9cfe8c</td>\n",
       "      <td>freeform</td>\n",
       "      <td>/m/03dnzn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.244279</td>\n",
       "      <td>0.887022</td>\n",
       "      <td>0.497204</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bathtub</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ImageID    Source  LabelName  Confidence      XMin      XMax  \\\n",
       "501580  4c1649f8d5b677d3  freeform  /m/03dnzn           1  0.000000  0.984500   \n",
       "635183  83f459662fc4fb12  freeform  /m/03dnzn           1  0.009929  0.999955   \n",
       "915071  f9f6d0ce7325d286  freeform  /m/03dnzn           1  0.012309  0.933887   \n",
       "879031  eb1abf6933be7682  freeform  /m/03dnzn           1  0.000503  1.000000   \n",
       "784065  c2ddf0678c9cfe8c  freeform  /m/03dnzn           1  0.244279  0.887022   \n",
       "\n",
       "            YMin      YMax  IsOccluded  IsTruncated  IsGroupOf  IsDepiction  \\\n",
       "501580  0.319811  0.999994           0            1          0            0   \n",
       "635183  0.430541  0.999577           0            1          0            0   \n",
       "915071  0.781339  1.000000           0            0          0            0   \n",
       "879031  0.301573  0.996929           1            1          0            0   \n",
       "784065  0.497204  1.000000           0            0          0            0   \n",
       "\n",
       "        IsInside ClassName  ClassID  \n",
       "501580         0   Bathtub        0  \n",
       "635183         0   Bathtub        0  \n",
       "915071         0   Bathtub        0  \n",
       "879031         0   Bathtub        0  \n",
       "784065         0   Bathtub        0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_test_10_annots = format_annotations(image_folder=\"val_test_10\", \n",
    "                                        annotation_file=\"val-test-annotations.csv\", \n",
    "                                        target_classes=subset)\n",
    "val_test_10_annots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sP2P7EeAFSUvWcxNtP1Rnuu+iFO+Na0Z10e859etNTW02Ac6or+EWYFo3L/P0pq8b1ccbAb+rj39T++vueFuSdq8z7ZNmPfdUd00jIiIiBoUUzz2wPcv2BNvfA/YEPlJPXQVsSwkN+TOlkNyEEiDSzov166tNjxvPF2hp09quuU1X/c5qaqNuXlIrd/G4qza9OV5O2ifZHmt77LBFR87GkCIiIiIGphTP3ZC0mqRVmg6NpoSCQCmS9wGuqzPISwOrAzP6d5RtXQ18DEDSGpRlIF3ZuenrdfXxtcAu9fG42l93xyMiIiKGhOy20b0RwDF1WcUrwF8pSzigrG1ejtdnmqcBD3tgpM4cB5wqaRowhTK2rtZNLCzpBsovUh+vx/YGTpa0H2W5ymd6ON6jxHNHREREJ0jCYAeSNAxY0PYLdZeOSykfPnyppd29wFjbj87rMSVhMCIiIgaL7hIGM/PcmRYFLpe0IGX985daC+eIiIiImH0pnjuQ7WeAtr8ttbQbNe9HExEREdE5evzAoKSZTY8/KOkvklZujomWtJukFXroZzdJx879kNv2/R5JN9cwkVNn47oJjfAQSX9s2jKuq/bzJHK7P0haUtKXe9FulKRbujj32vsVERERMRT1euZZ0lbAMcA2tv8GNMdE70YJCnmwT0fXe4cC+9i+XNLb5qQD2x/sRZt5ErktaZjtWfOi7yZLUsJTjpvH92kr8dwRMVgkMjsiutOrrepq7PPPgQ/ZvqseO1DSvjWCeixwmqSpkoZLWl/StXU2+EZJi9euVpB0UZ29/p+m/rep0dWTJZ0paUQ9fq+kg+rx6ZJW72KILwH/DmD7nm5ex3BJZzRiq4HhTefulbRMnXm9TdLPJc2QdImk4bVNa+T2m8YmaVlJf6rHT5R0n6Rl2oxlZp3JvgHYSNJWkqbUvk6WtHBt19XxHiO/WxwOvKN+j46QNEIlYrsx/u2b2i4g6dT6Pp0ladE242/7PYuIiIjoZL0pnhcG/gDsYPv21pO2zwImAeNsj6aEdYwHvmp7XWBr4PnafDRlP+G1gZ0lrVQLy28DW9ter/b19aZbPFqPHw/s28UY7wJ+0IslBV8Cnqvx0ofSdWz1KsDPbK8JPMnrwSit2o3te8Bl9fi5wMpdXLsYcIvtDSmv+RRgZ9trU/4i8CVJi7Q73tRHbyK/G/YH7rI92vZ+wAvAjnWcWwA/ktQIV1kNOKm+T09TZqxf04vvWURERERH6k3x/DIlHONzvexzNeAh2xMBbD9d46wBLrX9lO0XgFuBt1KKvTWAayRNBXatxxvaxU+/ps6YjqSk/f1O0ip19ndim7FtBvy2jmsaXcdW32N7anf37WZszdHYFwFPdHHtLODs+ni1es876/NT61i7Ot7Qm8jvrgg4TGUv6D8DK1L2rYZSlF9TH/+WN8dw9/Q9KzdIPHdERER0mN6seX6Vklb3Z0nfsn1YD+1F17HNzfHTjThpAX+y/fH2l7SNn272H5SifLqkz1Fmyc+kzH6305uNrVvHObyHdnMSjf1C0zrnrq7pqa/eRH53ZRywLDDG9ssqez4vUs+1vketz3v6npWL7JOAkwAWXn6VbCgeERERg16vPjBo+zlJ2wFXSfqn7V+2NHkGaKxrvp2ytnl92xPreufn6dr1wM8kvdP2X+v62n9vmm3tyRTKEpAzbF8l6VzgANrMhFLSAMdR9kBeC1inl/eYHY1o7B9K2gb4l15cczswqvEeAJ8Crujm+Jxo/h5Bma1/uBbOW/DG92tlSRvZvo6SOtgawz3b37MkDEZEREQn6NUHBgFsPw58APh2y4fLoKy3PaH+CX8YZV3zMZJuBv7E6zOa7fp9hLJbx+l1CcH1QFcfDGznl5RlC1MlTQKWp6w/bvdBt+OBEfU+3wRunI379NZBwDaSJlOWkjxEKVy7VJexfAY4U9J0yszxCV0dn5NB2X6MssziFklHAKcBY+t7No5SqDfcBuxa36elKO9bc19z+z2LiIiIGJQSz93H6m4Ys2y/Imkj4Pj6QcohLfHcERERMVgo8dz9amXg95LeQtlC7wvzeTwRERER0UdSPPcx238B3j2/xxERERERfa/Xa56jM0k6oIbBTKsBKhtK+oWkNXq47rXAmIiIiIihIjPPQ1hdk70dsJ7tF2v4yUK2P9/X90o8d0Qk9joiOkFmnoe25SkpiS8C2H7U9oOSJjTSGmuM+KEqUevXS1qutRNJh9SZ6Pw8RUREREdLsTO0XQKsJOlOScdJel+bNosB19eo9Stp+QCkpP8B/hX4jO1X5/mIIyIiIuajFM9DmO2ZwBhgd+ARYLyk3VqavQRcUB+3RpV/B1jS9hfdZs/DxHNHREREp8ma5yGuRoRPACbUIJZdW5q83FQYt0akTwTGSFqqhui09p147oiIiOgoKZ6HMEmrAa/W7fUARgP3AWv1souLgIuBCyVtY7vLJMXEc0dEREQnSPE8tI2gxKgvCbwC/JWyhOOs3nZg+0xJiwPnS/qg7efnzVAjIiIi5r/Ec0e/SDx3REREDBbdxXPnA4MREREREb2U4jkiIiIiopdSPM8Hkv5N0hmS7pJ0q6Q/Slq1D/vfXNLGc3DdaEkf7KtxRERERHSafGCwn0kScC5wqu1d6rHRwHLAnX10m82BmcC1szGuBSi7bYwF/thH43hN4rkjOldityNiKMnMc//bgrJ38gmNA7anAldLOkLSLZKmS9oZXptFboSUIOnYRpCJpHslHSRpcr1mdUmjgD2Ar0maKmlTSctKOlvSxPrvvfX6AyWdJOkS4NfAwcDO9bqdJS0l6TxJ02o09zpN1/1G0mWS/iLpDamDEREREZ0qM8/9by1KUl+rD1NmftcFlgEmSrqyF/09ans9SV8G9rX9eUknADNtHwkg6XfAT2xfLWllyt7M76rXjwE2sf18LcrH2t6zXncMMMX2DpK2pBTYo+t16wDvocR3T5F0oe0HmwcmaXfK1ncMW2LZXryUiIiIiIEtxfPAsQlwek38+6ekK4D1gad7uO6c+vUmSgHeztbAGmXFCABL1L2ZAc7vZm/mTYCPANi+TNLSkkbWc3+o1z0v6XJgA+C85ouTMBgRERGdJsVz/5sB7NTmuNocgxJe0ry8ZpGW8y/Wr63R2c3eAmzUWiTXYvrZbsbabkxu+dp6PCIiIqJjpXjuf5cBh0n6gu2fA0haH3iCst74VGApYDNgP2BByqzxwpTCeSvg6h7u8QywRNPzS4A9gSPq/UbXddbtrlu86fmVwDjgEEmbU5aIPF2L7u0l/YCybGNzYP/uBpR47oiIiOgE+cBgP3OJdNwReH/dqm4GcCDwO2AacDOlwP6m7X/Yvh/4fT13GjClF7f5X2DHxgcGgb2BsfWDf7dSPlDYzuWUQn1q/cDigY3rgMOBXZva3ghcCFwPHNK63jkiIiKiEyWeO2abpANp+kBibySeOyIiIgaLxHNHRERERPSBrHmO2Wb7wPk9hoiIiIj5ITPPg4CkAyTNqGuWp0rasIf2m9b2UyUN7+U9Zs7lGOfq+oiIiIjBIDPPA5ykjYDtgPVsvyhpGWChHi4bBxxp+1fzfIC9lHjuiHkn8dgREf0nM88D3/KULeJeBLD9aGNnC0lbSZpSo7lPlrSwpM8DHwO+K+k0SSMkXdoU4b19VzeS9KPa7lJJy9Zj75B0kaSbJF0lafV6/G2Srqtx34fM83chIiIiYgBI8TzwXQKsJOlOScdJeh+ApEWAU4Cdba9N+SvCl2z/Ajgf2M/2OOAFYEfb6wFbAD9SU9Rgk8WAybXdFcD36vGTgL1sjwH2BY6rx38KHG97feAf7QYuaXdJkyRNmvXcU3P5NkRERETMfymeBzjbM4ExwO7AI8B4SbsBqwH32L6zNj2VEqzSSpRQlmnAn4EVgeXatHsVGF8f/xbYRNIIYGPgTElTgRMpM+EA7wVOr49/08XYT7I91vbYYYuObNckIiIiYlDJmudBwPYsYAIwQdJ0SlhJu4TAdsYBywJjbL8s6V7eHPHd9raUX66etD26mzYRERERQ0aK5wFO0mrAq7b/Ug+NBu4DbgdGSXqn7b8Cn6Ist2g1Eni4Fs5bAG/t4lZvAXYCzgA+AVxdo7jvkfRR22fW5R7r2L4ZuAbYhTJLPa6n15F47oiIiOgEWbYx8I0ATpV0a116sQZwoO0XgM9QllRMpyy7OKHN9adRIrYnUYrc27u4z7PAmpJuArYEDq7HxwGfk3QzMANofODwq8BXJE2kFOgRERERHS/x3NEvEs8dERERg0XiuSMiIiIi+kCK54iIiIiIXsoHBjuUpH8DjgLWB14E7gX2adrabm763g0Ya3vP3l6ThMGIImmAERGDW2aeO1DdFeNcYILtd9heA/gW7fd3joiIiIheSvHcmbYAXrb92u4btqcCV0s6QtItNap7ZwBJm0u6oNFW0rF1dhlJ60u6VtLNkm6UtHhttkKN7f6LpP/pv5cWERERMf9k2UZnWgu4qc3xD1P2iV4XWAaYKOnKrjqRtBAldXBn2xMlLQE8X0+PBt5NWRJyh6RjbN/fcv3ulGREhi2x7Ny9ooiIiIgBIDPPQ8smwOm2Z9n+JyVUZf1u2q8GPGR7IoDtp22/Us9davuput/0rbQJX0k8d0RERHSaFM+daQYwps1xddH+Fd74s9CI7xZdR3C/2PR4FvkrRkRERAwBKXg602XAYZK+YPvnUNYuA08AO0s6FVgK2AzYD1gQWEPSwpTCeSvgakoa4QqS1q/LNhbn9WUbsyXx3BEREdEJUjx3INuWtCNwlKT9gReoW9VR4r5vpswof9P2PwAk/R6YBvwFmFL7eal+qPAYScMphfPW/fxyIiIiIgaMxHNHv0g8d0RERAwWieeOiIiIiOgDKZ4jIiIiInopa56HEEn/DvwMWIPyi9MFwH62X5rX904899CVOOqIiOgkmXkeImpk9znAebZXAValfHjw0Pk6sIiIiIhBJMXz0LEl8ILtXwHYngV8DfispImS1mw0lDRB0hhJi0k6uZ6fImn7en43SecknjsiIiKGmhTPQ8eatER2234a+Btl+cbHACQtD6xg+ybgAOAy2+sDWwBHSFqsXj4a2BlYm7J39EqtN5S0u6RJkibNeu6pefSyIiIiIvpPiueho6u0QAETgI/W5x8DzqyPtwH2lzS1tlkEWLmeSzx3REREDDn5wODQMQP4SPMBSUsAKwETgcckrUOZTf5iownwEdt3tFy3IYnnjoiIiCEoBc/QcSlwuKRP2/61pGHAj4BTbD8n6Qzgm8BI29PrNRcDe0naq6YWvtv2lDm5eeK5IyIiohNk2cYQ4RIluSPwUUl/Ae6kxHZ/qzY5C9gF+H3TZYcACwLTJN1Sn0dEREQMWYnnjn6ReO6IiIgYLBLPHRERERHRB1I8DwGSDpA0Q9I0SVMlbShpH0mL9uLaX0haoz/GGRERETHQZdlGh5O0EfBjYHPbL0paBlgIuBYYa/vROex3WA1a6ZWFl1/Fy+961JzcKga5xHNHRMRgk2UbQ9vywKO2XwSoxfJOwArA5ZIuB5B0fA00mSHpoMbFNW1wbH08U9LBkm4ANpJ0uKRb64z2kf3+yiIiIiL6WYrnzncJsJKkOyUdJ+l9to8GHgS2sL1FbXdA/Q1rHeB9dc/nVosBt9jekBKMsiOwpu11gO/P+5cSERERMX+leO5wtmcCY4DdgUeA8ZJ2a9P0Y5ImA1MoUd7t1jnPAs6uj5+mbHX3C0kfBp5rbZx47oiIiOg0CUkZAura5AnABEnTgV2bz0t6G7AvsL7tJySdQonibvVCY52z7VckbQBsRdkfek9gy5b7ngScBGXNc1++poiIiIj5ITPPHU7SapJWaTo0GrgPeAZYvB5bAngWeErScsC2veh3BCWN8I/APrXfiIiIiI6WmefONwI4RtKSwCvAXylLOD4O/J+kh2xvIWkKMAO4G7imF/0uDvxB0iKAgK911zjx3BEREdEJslVd9IskDEZERMRgka3qIiIiIiL6QIrniIiIiIheSvHcwSRZ0m+ani8g6RFJF9Tn/yVp//k3woiIiIjBJR8Y7GzPAmtJGm77eeD9wAONk7bPB85vvUjSArZf6ep5V7prN/2Bp1SMGcEAACAASURBVBi1/4Vz8hpiAErkdkREDFUpnjvf/wEfAs6i7LBxOrApQA1LGWt7z7q38+PAu4HJkp6hRHiPAh6V9FngeGAsZdeOr9u+vPbxIcq+0IvRstdzRERERCfJso3OdwawS91Sbh3ghm7argpsbfsb9fkYYHvbnwC+AmB7bUoRfmrtE2AjYFfbKZwjIiKio6V47nC2p1Fmjz8O/LGH5mc2EgSr8+tyD4BNgN/UPm+nBK2sWs/9yfbjrZ0lnjsiIiI6TYrnoeF84EjKko3uPNvNc83GdUCJ57Y91vbYYYuO7HmUEREREQNciueh4WTgYNvT56KPK4FxAJJWBVYG7uiDsUVEREQMGvnA4BBg++/AT+eym+OAEyRNp3xgcDfbL0rdTUi/LvHcERER0QkSzx39IvHcERERMVgknjsiIiIiog+keI6IiIiI6KUUzy0SaR0RERERXckHBt9sjiKt21H5NJ1svzpPRjqIJJ57cEjsdkRERPcy89xeI9IaXo+0BkqktaRj6+PlJJ0r6eb6b2NJoyTdJuk4YDKwkqSPS5ou6RZJP6zXfkzSj+vjr0q6uz5+h6Sr6+PvSppYrztJxbsk3dg0nlGSptXHYyRdIekmSRdLWr71hUl6q6RLJU2rX1eWNFLSvZLeUtssKul+SQvW8VxU+7xK0uq1zSmSjpZ0raS7Je3Ux9+DiIiIiAEnxXN7vY20Phq4wva6wHrAjHp8NeDXtt8NvAz8ENgSGA2sL2kHyr7Jm9b2mwKPSVqRkuR3VT1+rO31ba8FDAe2s30bsJCkt9c2OwO/l7QgcAywk+0xlL2dD20z5mPr2NYBTgOOtv0UcDPwvtrmP4GLbb8MnATsVfvcl7JlXcPydbzbAYd38R5FREREdIwUz23MRqT1lsDx9ZpZtQgFuM/29fXx+sAE24/YfoVSsG5m+x/ACEmLAysBvwM2oxTSjeJ5C0k31L2VtwTWrMd/D3ysPt4ZGE8p2NcC/iRpKvBt4N/bjHmjei8ocdub1Mfja18AuwDjJY0ANgbOrH2eSCmYG86z/artW4HlWm+UeO6IiIjoNFnz3LVGpPXmwNKzeW1vY62vAz5DSeq7Cvgspbj9Rp31Pg4Ya/t+SQcCi9TrxlMK2nMA2/6LpLWBGbY3ms2xNjb6Ph/4gaSlgDHAZcBiwJO2R3dx7YvdvU7bJ1Fmrll4+VWyoXhEREQMeimeu3Yy8JTt6ZI276LNpcCXgKMkDaMUm61uAH4qaRngCcps9jH13JXAwfXfFGAL4HnbT0lasrZ5tM4A7wScBWD7LkmzgO9QCmkoBfiykjayfV1dxrGq7cZSkoZrKTPLv6HEbV9d+5xZ11L/FLjA9izgaUn3SPqo7TPrByDXsX1zT29eqyQMRkRERCfIso0u2P677Z4irb9KWVoxHbiJ15dVNPfzEPD/gMsp64on2/5DPX0VZcnGlbVYvZ/Xi9kngZ8D04HzgIktXY8HPklZwoHtlygF9g8l3QxMpSy5aLU38Jn6IcNP1dfQ2uf4pmPjgM/VPmcA23f/lkRERER0rsRzR79IPHdEREQMFonnjoiIiIjoAymeIyIiIiJ6acgUz5J+ImmfpucXS/pF0/MfSfr6HPR7b/0wYOvxAR3jLelASft2ce7a/h5PRERExGAwlHbbuBb4KGVnjLcAywBLNJ3fGNin3YVzYnZivAca2+0+aDhXEs89sCSGOyIiYs4MmZln4Bpe331iTeAW4BlJ/yJpYeBdwBRJI2ps9eQaqb09gKTFJF1YY7hvkbRzU997NbVvxFc3x3i3jbKW9BZJx0maIekCSX9sF3MtaUKdOb9SJfp7fUnnSPqLpO83tTuvxmjPkLR70/EP1PHdLOnSpq7XqH3fLWnvpvYz69fN6/mzJN0u6bS6XV2vosAjIiIiOs2QmXm2/aCkVyStTCmirwNWpISSPAVMs/2SpFeBHW0/XZdjXC/pfOADwIO2PwQgaWRT94/aXk/SlykR1p9vM4RGlPXqlBnps4APU5IM1wb+FbiNsr90Oy/Z3kzSV4E/UIJMHgfukvQT248Bn7X9uKThwERJZ1N+Qfo5JdXwnhqC0rA6ZW/pxYE7JB1fI7mbvZvyy8aDlF9A3ivpBspe1dvbfqT+InEoJeQlIiIiomMNmeK5asw+bwz8mFI8b0wpnhvrfAUcJmkz4NXaZjnKfstHSvohJUTkqqZ+z6lfb6IUxO2cZ/tV4FZJjSjrTYAz6/F/SLq8m7E3loBMpyQJPgQg6W7KXtGPAXtL2rG2WwlYBViWso/0PQC2H2/q80LbLwIvSnq4vs6/t9z3Rtt/r/eaSin2n+T1KHCAYcBDrQOus9+7AwxbYtluXlpERETE4DDUiudrKcXy2pRlG/cD3wCe5vUZ33GUgnOM7Zcl3QssYvtOSWOAD1JirC+xfXC9phFTPYuu39N2UdbdRXd3df2rLX29CixQUxC3Bjay/ZykCZQ4b/F6BHd3Y+pq7O3aiF5EgSeeOyIiIjrNUCuer6EUy3fXRL/Hawz2msAXapuRwMO1cN4CeCuApBWAx23/tq4J3q0PxnM1sKukUykF++bA7+awr5HAE7VwXh14Tz1+HfAzSW9rLNtomX2eE72NAn9N4rkjIiKiEwy14nk6ZZeN37UcG2H70fr8NOB/JU2iRFzfXo+vDRxR10S/DHypD8ZzNrAVZRb8TuAGyhKSOXERsEeN3b4DuB6grkneHTin7jLyMPD+uRl0XRu+E3B0Xfu9AHAUJb47IiIiomMlnns+kzTC9kxJSwM3Au+1/Y/5Pa6+lnjuiIiIGCy6i+ceajPPA9EFdenIQsAhnVg4R0RERHSKFM/zme3N5/cYIiIiIqJ3hlJISr+RNEvS1BpKMlnSxvX4CpLOqo83l3RBfdwcqLKHpE/P5f27igz/Vi+vn9nF8VPahbhEREREDBWZeZ43nrc9GkDSfwA/AN5n+0Gg2+LT9gmzcyNJC9h+pZfNvwUcNjv995XEcw8sieeOiIiYM5l5nveWAJ4AkDRK0i3dNZZ0oKR96+MvSJpYZ7DPlrRoPX6KpB/XUJUfSlpa0iWSpkg6kTb7R0s6HBheZ8RPq8faxnnXcz+qs+aXSnpTwkniuSMiImIoSvE8bzSK1NuBXwCHzGE/59he3/a6lOjuzzWdWxXY2vY3gO8BV9t+NyWJcOXWjmzvT50Rtz2uHv6s7THAWEo64dL1+GLAZNvrAVfU/l9T93U+BtipXn8yJZ6blna7S5okadKs5+Z0B76IiIiIgSPLNuaN5mUbGwG/lrTWHPSzlqTvA0sCI4CLm86dWYNeADajxoLbvlDSE73sv12c92OU1MLx9fhveT1+vGE1ehHPnYTBiIiI6DQpnuexmsC3DCVBcHadAuxg+2ZJu1ESCBuebb3V7HTcTZx3O6199yqeOyIiIqLTpHiex2pU9jDKjO6is3n54sBDdZnEOOCBLtpdWc9/X9K2wL900e5lSQvafpmu47yhLOfZCTgD+AQlRrxZ4rkjIiJiSErxPG8MlzS1Phawq+1ZdYnD7PgOJbL7PkqM+OJdtDsIOF3SZMoa5b910e4kYFpt91naxHlXzwJrSrqJEhe+c3MnieeOiIiIoSrx3NEvEs8dERERg0V38dzZbSMiIiIiopdSPEdERERE9FKK5w7XFBV+i6QzG0Erc9nna0EuEREREUNJPjDY+Zr3nD4N2AP4cU8XqXy6UbZf7YtBJJ57/kkUd0RERN/JzPPQchXwTgBJX6+z0bdI2qceGyXpNknHAZOBlSR9oMZ03yzp0qa+1pA0QdLdkvbu/5cSERER0f8y8zxESFoA2Ba4SNIY4DPAhpSt9G6QdAXwBCU98DO2vyxpWeDnwGa275G0VFOXqwNbULbPu0PS8XX/6OZ77g7sDjBsiTnJiImIiIgYWDLz3Pkae05Pouz//EtgE+Bc28/ankmJ3960tr/PdmPP5/cAV9q+B8D24039Xmj7RduPAg8Dy7Xe2PZJtsfaHjts0ZHz5MVFRERE9KfMPHe+19Y8N6j7tJbm2G/Rdez3i02PZ5GfpYiIiBgCUvAMTVcCp0g6nFIg7wh8qk2764CfSXpbY9lGy+xzryWeOyIiIjpBiuchyPZkSacAN9ZDv7A9RdKolnaP1HXL50h6C2V5xvv7c6wRERERA0niuaNfJJ47IiIiBovEc0dERERE9IEUzxERERERvZQ1zx1O0tJAI9zk3yg7YzxSn29g+6WW9sOACbY3lfRO4KzW3Tpa2r+99nNGd+NIwmDfS3JgRERE/0vx3OFsPwY04rkPBGbaPrKb9rN4fc/n3ng7sAvQbfEcERER0QmybGMIk/TNpojuveqxBSQ92abtApJ+LOlGSdMkfb6eOhzYQtLUxHRHREREp8vM8xAlaQNgHLABMAy4sUZ039rFJbsDD9veQNLCwPWSLgH2B/a0vUObeySeOyIiIjpKiueha1PgbNvPAUg6jxLb3VXxvA3wLkm71OcjgVW6u4Htk4CTABZefpXsiRgRERGDXornoau7iO6u2n/Z9qVvOCht3XdDioiIiBjYUjwPXVcCJ0o6grJsY3tg527aXwx8WdIVtl+RtBrwN+AZYPGebpZ47oiIiOgEKZ6HKNs3SjodmFgPHW97uqSufiZOBFYGpkqCEtW9PTAFGCbpZuCXto+ex0OPiIiImG8Szx39IvHcERERMVgknjsiIiIiog+keI6IiIiI6KWseR5AJC0H/AR4D/AE8BLwP7bPnQf32gG403ZXW9P1qcRz973Ec0dERPS/zDwPECqfwjsPuNL2222PocRe//s8uuUOwBrzqO+IiIiIjpTieeDYEnjJ9gmNA7bvs30MgKRRkq6SNLn+27geX17SlTUe+xZJm7Z2LOlwSbfWWO0j67X/BRxRr3uHpNGSrq9tzpX0L5LeJenGpn5GSZpWH4+RdIWkmyRdLGn5efz+RERERMx3WbYxcKwJTO7m/MPA+22/IGkV4HRgLPAJ4GLbh0oaBizafJGkpYAdgdVtW9KStp+UdD5wge2zartpwF62r5B0MPA92/tIWkjS223fTdkH+veSFgSOAba3/YiknYFDgc+23Dvx3BEREdFRUjwPUJJ+RonLfsn2+sCCwLGSRgOzgFVr04nAybWgPc/21JaungZeAH4h6ULggjb3GgksafuKeuhU4Mz6+PfAx4DDKcXzzsBqwFrAn+qez8OAh1r7TTx3REREdJos2xg4ZgDrNZ7Y/gqwFdCYsv0a8E9gXcqM80K13ZXAZsADwG8kfbq5U9uvABsAZ1PWOV80m+MaD3xM0qqlO/+FEtU9w/bo+m9t29vMZr8RERERg05mngeOy4DDJH3J9vH1WPMSjJHA322/KmlXymwvkt4KPGD755IWoxTgv25cJGkEsKjtP0q6HvhrPfVarLbtpyQ9IWlT21cBnwKuqOfukjQL+A6lkAa4A1hW0ka2r6uz3qvantHVi0s8d0RERHSCFM8DRF2PvAPwE0nfBB4BngX+uzY5Djhb0keBy+s5gM2B/SS9DMwE3jDzTCmQ/yBpEcqM8dfq8TOAn0vaG9gJ2BU4QdKiwN3AZ5r6GA8cAbytjvUlSTsBR9clHwsAR1FmzyMiIiI6VuK5o18knjsiIiIGi8RzR0RERET0gRTPA4Ckpet+y1Ml/UPSA03PF+rimr9LWnIO7jVM0lU9tLlY0uKz23dEREREp8uyjQFG0oHATNtH9tDu78Batp/sl4HNpYWXX8XL73rU/B7GgJfI7YiIiPkvyzYGMUm7SrqxzkIfJ+lN37N2bSTtJemwpjafl/QTSQtIerIeW1HS1U3phI3UwtdmtSV9s567RdJe9dg76/NfSpoh6f/qBxIjIiIiOlqK5wFM0lqUdMCNbY+m7GqxSy/bnEnZRaNhZ17faq7hk8D/1uvWBaa19L0BMI6yT/RGwJclrVNPrwYcZXtN4HnKHtIRERERHS1b1Q1sWwPrA5Nqkt9w4P7etLH9uzqDPBb4G2WbuRuo+0NXE4ET66zxebZvbul7U+Bs288BSDqPknp4CfBX29Nru5uAUa2DTzx3REREdJoUzwObgJNtf2cO24ynRGvfSymCXQtsAGxfJmlz4EPAaZJ+YPu0lr678mLT41m0+VlKPHdERER0mizbGNj+TInGXgZe25Vj5dlocxbwYcoyjtYlG410wn/UIvcU4N0tTa4EdpQ0vCYVbg90u1NHRERERCfLzPMAZnu6pIOAP9cPCr4M7EFZhtFjG9uPSfor8A7bk9vcYivg603phJ9suf+Nkk6nLO8AOL7e752z+1oSzx0RERGdIFvVRb9IwmBEREQMFtmqLiIiIiKiD6R4joiIiIjopRTPg4CkWU1x3VMljerj/neQtEYv2u0h6dN9ee+IiIiIwSQfGBwcnq9BJvPKDsAFwK3dNbJ9wpzeYPoDTzFq/wvn9PKOlCjuiIiIwSczz4OUpEUk/UrSdElTJG1Rj+8m6dimdhfUvZyRNFPSoZJulnS9pOVqJPd/AUfUWe13SPqCpIm13dmSFq3XHyhp3/p4gqQf1ljwOyVt2u9vQkREREQ/S/E8OAxvWrJxbj32FQDbawMfB06tSYHdWQy43va6lD2cv2D7WuB8YD/bo23fBZxje/3a7jbgc130t4DtDYB9gO/N1SuMiIiIGASybGNwaLdsYxPgGADbt0u6D1i1h35eoizPgBKp/f4u2q0l6fvAksAI4OIu2p3T1Neo1pOJ546IiIhOk5nnwaur6OxXeOP3tXk2+mW/vrF320jt6hRgzzqrfVBLH80aEd1dxnPbHmt77LBFR3bRRURERMTgkZnnwetKYBxwmaRVgZWBO4AlgC/XtMEVgQ160dczwOJNzxcHHpK0YL3HA3M72CQMRkRERCfIzPPgdRwwTNJ0YDywm+0XgWuAe4DpwJFAu1juVmcA+9UPHr4D+A5wA/An4PZ5MfiIiIiIwSjx3NEvEs8dERERg0XiuSMiIiIi+kCK54iIiIiIXkrxPIC1i+WWNFbS0X14j3slLdNX/UVERER0suy2MbC129/5XuBNi4clLWD7lX4Z1RxIPPebJZ47IiJi8MnM8yAjaXNJF9THB0o6SdIlwK8lDZN0RI3Wnibpi03XXCnpXEm3SjqhbmXX2vd5km6SNKMGnDSOf0DS5BrXfWk9tpikk+u9pkjavp/egoiIiIj5JjPPA9twSVPr43ts79imzRhgE9vP14L3KdvrS1oYuKYW1lD2e14DuA+4CPgwcFZLX5+1/bik4cBESWdTfsH6ObCZ7XskLVXbHgBcZvuzkpYEbpT0Z9vP9tFrj4iIiBhwUjwPbO2WbbQ63/bz9fE2wDqSdqrPRwKrUGK5b7R9N4Ck0ynx3q3F896SGgX6SvXaZYErbd8DYPvxpnv9l6R96/NFKEEttzU6Szx3REREdJoUz4Nf80yvgL1sX9zcQNLmQOuG3m7TZmtgI9vPSZpAKYjV5trGvT5i+46uBmb7JOAkgIWXXyUbikdERMSgl+K5s1wMfEnSZbZfrrHdjWjtDSS9jbJsY2dqUdtkJPBELZxXB95Tj18H/EzS2xrLNurs88XAXpL2sm1J77Y9pauBJZ47IiIiOkGK587yC2AUMFmSgEeAHeq564DDgbWBK4FzW669CNhD0jTgDuB6ANuP1OUX59QPGT4MvB84BDgKmFbvdS+w3Tx7ZREREREDQOK5h4C6JGNf2/OtuE08d0RERAwWieeOiIiIiOgDWbYxBNieAEyYz8OIiIiIGPQy8zyPNUVs3yLpf+ueyD1ds7ek2ySd1uZcn8ZzdzOG3SStMK/vExERETGYZM3zPCZppu0R9fGpwJ22D+3hmtuBbRt7Kzcd79MIbknDbM/q4twEyjrpXi9U7q6/hZdfxcvvetScDbRDJZ47IiJiYMqa54HjOmDFxhNJ+zVFaR9Uj50AvB04X9LX2kRwN8dzLyvpTzU6+0RJ90lapp77pKQb66z3iZKG1eMzJR0s6QZgI0nfrWO4pd5HNWRlLHBavX64pK1qDPf0Gsu9cO3v3trH1cBH+++tjIiIiOh/KZ77SS1etwLOr8+3oST4bQCMBsZI2sz2HsCDwBa2f1IvHwNsb/sTLd1+jxKRvR5l67mVa9/vouzl/N6aUDgLGFevWQy4xfaGtq8GjrW9vu21gOHAdrbPAiYB4+r1Bk4Bdra9NmWt/JeaxvGC7U1snzH371RERETEwJXied4bLmkq8BiwFPCnenyb+m8KMBlYnVJMt9Mcwd1sE+AMANsXAU/U41tRCu6J9d5bUWazoRTSZzf1sYWkGyRNB7YE1mxzn9WAe2zfWZ+fCmzWdH58u0FL2l3SJEmTZj33VBcvLSIiImLwyG4b897ztkdLGglcAHwFOJoSb/0D2yf2oo9nuziubo6favv/tTn3QmNdsqRFgOOAsbbvl3QgJZK7t/fpdnyJ546IiIhOk+K5n9h+StLewB8kHU+Jtz5E0v9v786j7KzqrI9/t4EoECTMMmlEo0xqkEADAoLSKEg30qLSLQqKoC0ovIg2rTjghIoNMigIKlMjophGRAWRKSGQMCVkQCYjKIOEACJhCBD2+8c511zKquQGq+oO2Z+1surec5/h3HrWE345nOfsc2zPl7Qe8IztuUtx2KuB9wDfqNNAVq3tl9XzHGd7rqTVgJVt391n/0ahPE/SKGAv4Pza9hiwcn19KzBG0qtt3wm8H7hqKfqZeO6IiIjoCSmeh5HtaZJuBva2fXadm3xtSbdmPrAPJf66VUcB50p6L6WYvR94zPY8SUcCv6mR2s9QRryfVzzb/ouk04CZlHjt65s+PgM4RdKTwDbAB4GfSlqubnfKUn35iIiIiB6Qpeq6WF3xYqHtZyVtA5xcH/DrOInnjoiIiG6xuKXqMvLc3V4O/KSOLj8NHNDm/kRERET0tBTPXcz2HcDm7e5HRERExLIiS9X1KEmr14CT6ZL+LOnepvcjB/lcV0vqyOkiEREREYMpI889yvZDlPAV6hJ0821/q139mXnvo4w54pftOv2QSLx2RETEsicjz8sgSZ+ucdyzJH28tn1G0sfq6xNrHDiS3ibpjPp6V0nX1jjw8ySt1LYvEREREdEGKZ6XMZK2okR1b0VZgu5jkl4PTAS2r5u9ERhdl6XbDpgkaS3gCOCtNQ58BnDIEs6VhMGIiIjoKZm2sezZHviZ7ScAJF1AKZB/AGwpaTRlzek7KQ8jbg+cDWwLbAJcU9elHkkJaRlQEgYjIiKi16R4Xvb0G7Vte4Gk+4APAJOB24G3Ai+3fbukTYGLbb9/+LoaERER0VlSPC97JgLfk3QMMALYA3hv02eHUwroO4BjgCn1s2uA4yVtaHtOne+8bl0ub4kSzx0RERG9IHOelzG2rwPOpURsT6GkEs6sH08C1gam2r6XEus9qe73ALA/cF6NGL8GeM0wdz8iIiKirRLPHcMi8dwRERHRLRYXz52R54iIiIiIFqV4joiIiIhoUYrnNpL0Mkk/lvR7SbdI+pWkxc4jlnRN/TlG0qz6erykEwapT2tKmippmqTtl7xHRERExLIjq220icpiyf8HnGl779o2jvLA3u0D7Wd7237abgAGa0LxW4Fbbe87SMcDuj+eO1HcERERARl5bqedgGdsn9JosD3d9iQASZ+SdL2kGZKOamwjaX7fA0naUdJF9fUXJf1Q0pWS5kj6RNN2n5N0q6RLJZ0r6fA+xxkHfBPYTdJ0SStIOrmmBM7u04+7JB1Vo7pnStpoEH83ERERER0pxXP7bAbc2N8HknYBxlIitMcBW0jaYSmOvRHwtrr/FyQtL2k88C5KauC/AX/3BKnt6cDngfNsj7P9JPDZ+rTp64E31yjvhnk1qvtkyvrQfb9H4rkjIiKip6R47ky71D/TgJsoxfDYpdj/l7YX2J4HzKVMBdkO+LntJ20/BvyixWO9R9JNtS+bUiK6GybUnzcCY/ruaPtU2+Ntjx+x4ipL0f2IiIiIzpQ5z+0zG9hrgM8EHG37ey/w2AuaXi+kXOd+Y7kXR9IrKSPKW9p+RNIZwEv6OU/jHBERERE9LQVP+1wOfE3SAbZPA5C0JbAicAnwZUnn2J4vaT3K/Oi5/8D5rqbEch9Nue7vAE5bwj4vBR4HHpW0NrArcOULOXniuSMiIqIXpHhuE9uWtCfwbUlHAE8BdwGH2r5D0sbAtWVRDuYD+1CmYLzQ810v6ULgZuBuyuoci52IbPtmSdMoo+RzgMkv9PwRERERvSDx3MsQSaPqSPaKwETgQNs3Dce5E88dERER3WJx8dwZeV62nCppE8q85TOHq3COiIiI6BUpnpchtv+j3X2IiIiI6GYpnjuEpOOAu21/u76/BPiT7Q/X9/8D3Av8GDjB9kArdQwbSfNtj2pl2yQMRkRERC/IOs+d4xpgWwBJLwLWoKyr3LAtMNn2fUNROEvKP6QiIiIiliDFc+eYTC2eKUXzLOAxSatKejGwMTBN0hhJswAk7SdpgqSLJd0h6ZuNg0naRdK1NT77p5L+boS4Rnh/TdJVwCGS/kXSVEnTJP22Lk+HpFGSTq8x3DMkvavPcdao58rwbERERPS0jDZ2CNv3SXpW0sspRfS1wHrANpQl5WbYfrouXddsHCVyewFwm6QTgSeBI4GdbT8u6b+Aw4Av9XPq0bbfDCBpVWDruozeh4FPA58EPgc8avt1TdtRX68NXAgcafvS5gNLOhA4EGDES9d8gb+ZiIiIiM6R4rmzNEaftwWOpRTP21KK52sG2Ocy248CSLoFeAUwmhKjPbkW2yMpxXh/zmt6vT5wnqR16j5/qO07A3s3NrL9SH25PHAZcJDtq/oe2PapwKkAL15nbNZEjIiIiK6XaRudpTHv+XWUaRtTKCPP2zJwQMlAUdyX2h5X/2xie/8B9n+86fWJwEl1hPkjLIriFtBf8fsscCPwtiV9FKZcSQAAEulJREFUsYiIiIhekJHnzjKZMk1iju2FwMOSRlPmQB+wFMeZAnxH0qtt31lDUda3ffsS9luFsqIHwL5N7b8BDgYOhTJto44+G/gQ8FNJR9j++kAHTjx3RERE9IKMPHeWmZRVNqb0aXvU9rxWD2L7QWA/4FxJM+rxNmph1y9SCuFJQPP5vgKsKmmWpJuBnZrOtZAypWMnSR9rtY8RERER3Sjx3DEsEs8dERER3WJx8dwZeY6IiIiIaFGK54iIiIiIFuWBwS4kaSFlLrQoK2wcbHugpeyoDx3+h+3v1vc7Aofb3r2fbQ8FTrX9xBL6cAZwke3zW+lz4rkjIiKiF2TkuTs9WZegewPw38DRS9h+NNDqw3yHAiv+I52LiIiI6FUpnrvfS4FH4G8x2pfVSO6Zkvao23wdeJWk6ZKOqW2jJJ0v6VZJ56j4BLAucIWkK+ox5zdOJGmvOuLcsLOkSZJul/R3o9gRERERvSbTNrrTCpKmU0JM1gHeUtufAva0/VdJawBTJF0IHAFsZnsc/G3axuaU9aPvo6wv/SbbJ0g6DNipxaXxxgBvBl5FKbhfbfupxoeJ546IiIhek5Hn7tSYtrER8HbgLJUcbgFfq2s7/5YS7732AMe4zvY9tp8DplMK4aX1E9vP2b4DmEOftaRtn2p7vO3xI1Zc5QUcPiIiIqKzZOS5y9m+to4yrwnsVn9uYfsZSXexKGK7r/5ivfs9RdPrvsfqu0h4Fg2PiIiInpbiuctJ2ggYATxEideeWwvnnYBX1M0eA1Zu8ZCNbRvTNh6QtDFwG7Bn/bzh3ZLOBF4JbFi36VfiuSMiIqIXpHjuTo05z1Cmauxre6Gkc4BfSLqBMhXjVgDbD0maLGkW8GtgcWvGnQr8WtL9tneizJe+CPgTMAsY1bTtbcBVlKkhH22e7xwRERHRixLPHcMi8dwRERHRLRLPHRERERExCFI895DmNZmH+by/qimGERERET0tc57jH2Z7tyVt003x3InijoiIiIFk5LnHLCZlEEkfkDRD0s2Szq5tZ0jaq2mb+fXnl2oi4XRJ90o6XdJHm9r+0JRCeFddLi8iIiKip2XkufcMlDK4CfBZSpLgPEmrLe4gtj8PfF7SKsAk4CTbNwKnSFoeuBw4dki/SURERESHSfHcexopgzsAz7EoZfAtwPmN2G3bDy/xQCW18BzguFo4NxwPXG77F0vYP/HcERER0VNSPPee99F/yqDoPwHwWer0nVosj2z67IvAPbZPbzRI2o8SvnLwkjpi+1TKutG8eJ2xWRMxIiIiul7mPPeegVIGLwPeI2l1gKZpG3cBW9TXewDL1893B/4Z+ETjwJK2AA4H9rH93BB/j4iIiIiOk5HnHiFpOWABZZpFfymDsyV9FbhK0kJgGrAfcBrwc0nXUQrsx+shPwmsC1xXBqS5ENgAWA24orbdYPvDrfQv8dwRERHRC5Iw2CMkvQE4zfZW7e5Lf5IwGBEREd0iCYM9TtJHgXOBI9vdl4iIiIhelmkbPcD2KcAp7e5HRERERK/LyHOXk/RZSbNr+Ml0Sf+U0JKIiIiIoZGR5y4maRtgd+CNthfUgnnkEnYbqr6MsL1woM+HM5478doRERExVDLy3N3WAebZXgBge57t++pnH2+K6N4IyvJ0ki6oo9RTJL2+ts+UNFrFQ5I+UNvPlrSzpBGSjpF0fd33I/XzHSVdIelHwMxh//YRERERwyzFc3f7DbCBpNslfVfSm5s+m2f7jcDJlLWZAY4Cptl+PfAZ4KzaPhl4E7ApMAfYvrZvDUwB9gcetb0lsCVwgKRX1m22Aj5re5Mh+YYRERERHSTFcxezPZ8ScHIg8CBwXk0ABJhQf94IjKmvtwPOrvteDqwuaRVgErBD/XMy8DpJ6wEP13PsAnxA0nRgKrA6MLYe8zrbf+ivf5IOlHSDpBsWPvHo4HzpiIiIiDbKnOcuV+cZXwlcKWkmsG/9aEH9uZBF11n9HQKYCBwEvBz4LLAnsBelqG7s93HblzTvKGlHFoWq9Ne3xHNHRERET0nx3MUkvRZ4zvYdtWkccDfwugF2mQi8D/hyLXzn2f4r8NfGw4a250i6mjLV4+C63yXAf0q6vMZ+vwa4d2n6moTBiIiI6AUpnrvbKOBESaOBZ4E7KVM4dh9g+y8Cp0uaATzBolFqKNMxRtTXk4Cjgavr++9Tpn7cpJLL/SDwzkH7FhERERFdIvHcMSwSzx0RERHdIvHcERERERGDIMVzRERERESLMud5mElaHbisvn0ZZTWMB+v7rWw/PUjn+V/gfNsXDMbxIiIiIiLF87Cz/RBlVQwkfRGYb/tbzdvUh/Jk+7nh7+HQSDx3RERE9IJM2+gQkl4taZakU4CbgHUk7Srp2hqzfZ6kleq2W0q6StKNkn4tae0BDruTpGskzZG0Z933RZKOreeaKWmv2r6zpL+NUks6RdI+9fUxkm6p0dzfqG1rS5pQQ1Cuk7T1EP56IiIiIjpCiufOsgnwA9ubA88ARwBvrTHbM4BDJL0YOB54l+0tgP8FvjzA8daixG6/k7L0HMC763neAPwzcJyktQbqUC3MdwM2rbHejeOcAHyzPon6HspydhERERE9LdM2OsvvbV9fX29LKXKvKbM4GElZd3ljYFPgt7V9BHDPAMe7wGUtwhk1bhtKRPePajLhn2sgynhgoLnWDwPPAadJ+iVwUW3fGXht7QPAqpJWsP1ko0HSgZR1pxnx0jVb+w1EREREdLAUz52lOepawMW239+8gaTNgRm2t2/heAuaXqvPz76e5fn/J+IlADVRcDxllHpv4D+BXepxFvuAY+K5IyIiotekeO5c1wDHS9qwRmavBKwL3AKsJ2kr29dJGgmMtT27xeNOBPaTdA6wBmVaxyHASsCm9XgrAW+hjG6vDLzE9kWSptbzA/wWOAg4DkDSONvTBzpp4rkjIiKiF6R47lC2H5C0P3BeLWgBPmP7jvqQ3wm1sF0O+B+g1eL5fGBr4GbAwGG25wLUBwZnArdTHloEWAWYUOdavwg4rLYfBJws6YO1D1fUtoiIiIielXjuGBaJ546IiIhukXjuiIiIiIhBkOI5IiIiIqJFKZ6HgKSXSfqxpN/XcJFfSXrNYrY/RtLs+nNNSVMlTZPUyooag0rS/OE+Z0RERES3yAODg6xGa/8fcKbtvWvbOGBtyoN4/fkIsKbtBZL2Bm61ve+wdHiQLClSfCjiuRPDHREREcMtI8+DbyfgGdunNBrqEm5X15HlRiz2ewEkXUhZGm6qpP8CvgnsJmm6pBUk7dIU0f1TSaPqfls0RXRfImmd2n6lpOMkTZT0uxrlPUHSHZK+0uiTpAvqvrNrmMnzSFqjnvcd9f2nJF1fI7qPqm1j6jm+S1mdY4Oh+ZVGREREdIaMPA++zYAb+2n/N2AcJRZ7DeB6SRNt/6uk+bbHAUh6ABhv+2BJawBHAjvbfrwW14dJOho4EdjD9oO1EP8q8KF6rqdt7yDpEODnwBaUpMDfSzrO9kPAh2w/LGmF2pef1fZGJPeFwJG2L5W0CzAW2IoSjnKhpB2APwKvBT5o+2N9v3ASBiMiIqLXpHgePtsB59ZY7AckXQVsSSlSB7I1JaJ7clNE97WUgnUz4NKmiO77m/ZrHHMmMNv2/QCS5lBGhx8CPiFpz7rdBpTi+CFgeeAy4CDbV9XPd6l/ptX3o+r2fwTutj2lv84nYTAiIiJ6TYrnwTcb2Kuf9oFisRdHwKW2//15jdLrKEXxNgPs14jlfo7nR3Q/BywnaUdgZ2Ab209IupIax02J6b4ReBvQKJ4FHG37e336MYbnR4pHRERE9LQUz4PvcuBrkg6wfRqApC2BR4D3SjoTWA3YAfjUEo41BfiOpFfbvlPSisD6wG3AmpK2sX2tpOWB1yxFRPcqwCO1cN6IMsLdYMr0j59KOsL214FLgC9LOsf2fEnrAc+0eC4g8dwRERHRG1I8DzLbrtMhvi3pCOAp4C7gUMp0h0Ys9qdt/3kJx3pQ0n7AuTUeG8o85NubIrpXoVzHb9N6RPfFwEclzaAU4s+bdmF7YV314xeS/mr7u5I2Bq6t00TmA/sAC1s8X0RERERPSDx3DIvEc0dERES3SDx3RERERMQgSPEcEREREdGiFM/DRNLCGnwyW9LNkg6T9KL62XhJJyxm3x0lXTQMfdxP0klDfZ6IiIiIbpUHBofPk01BKGsBP6KsevEF2zcAXT8hWNKIuo7131maeO7EbkdERESnyshzG9ieS0neO1jF30aWJb25jlBPlzRN0sp1t1GSzpd0q6Rz6n5bSZpQ99tD0pOSRkp6SQ1EQdIBNVb7Zkk/q8vdIendKlHhN0ua2NS9dSVdXOO8v9lo1MAx4XdJ+rykq4F3D/kvLyIiIqKNMvLcJrbn1Gkba/X56HBKut/kWqA+Vds3BzYF7gMmA2+iLDG3ef18e2AWJbVwOWBqbZ/QtN70V4D9KdHenwfeZvteSaObzj+uHnMBcJukE4En6ScmHPhS3ecp29v1/Y6J546IiIhek+K5vfpLHZwMHCvpHErhe09dW/k62/cASJoOjLF9taQ76xrMWwHHUsJXRgCT6vE2q0XzaMo605c0necMST8BJjSd/zLbj9bz3AK8ou7bX0x4w3n9fbnEc0dERESvSfHcJpI2pISMzAU2brTb/rqkXwK7AVMk7Vw/ao7ZXsiiazcJ2JWS+Pdb4AxK8Xx4/fwM4J22b66BKzvW83xU0j8B7wCmSxq3mPP0GxPeJBHdERERsUxI8dwGktYETgFOqomEzZ+9yvZMYKakbYCNgL8s5nATgbOAs2oi4erAy1iUNrgycH+N8H4fcG/TeaYCUyX9C7DBYs7Rb0y47dtb/c6J546IiIhekOJ5+KxQp1ssDzwLnE2ZZtHXoZJ2ooz63gL8GthmMcedCqxNKaIBZgBzvSg68nN1m7uBmZRiGuAYSWMpo8qXUWLDG6PPzzNQTDjQcvEcERER0QsSzx3DIvHcERER0S0WF8+d4jmGhaTHgNva3Y9YojWAee3uRCxWrlHnyzXqfLlG3aGd1+kVtvtdKizTNmK43DbQv+Cic0i6Ideps+Uadb5co86Xa9QdOvU6JSQlIiIiIqJFKZ4jIiIiIlqU4jmGy6nt7kC0JNep8+Uadb5co86Xa9QdOvI65YHBiIiIiIgWZeQ5IiIiIqJFKZ5jyEl6u6TbJN0p6Yh29ydA0gaSrpD0O0mzJR1S21eTdKmkO+rPVdvd12WdpBGSpkm6qL5/paSp9RqdJ2lku/u4rJM0WtL5km6t99Q2uZc6i6T/V/+umyXpXEkvyb3UXpJ+KGmupFlNbf3eNypOqHXEDElvbF/PUzzHEJM0AvgOsCuwCfDvkjZpb6+CknL5SdsbA1sDB9XrcgRwme2xlOTJ/GOn/Q4Bftf0/hvAcfUaPQLs35ZeRbPjgYttbwS8gXK9ci91CEnrAZ8AxtveDBgB7E3upXY7A3h7n7aB7ptdgbH1z4HAycPUx36leI6hthVwp+05tp8Gfgzs0eY+LfNs32/7pvr6Mcp/7NejXJsz62ZnAu9sTw8DQNL6wDuA79f3At4CnF83yTVqM0kvBXYAfgBg+2nbfyH3UqdZDlhB0nLAisD95F5qK9sTgYf7NA903+wBnOViCjBa0jrD09O/l+I5htp6wJ+a3t9T26JDSBoDbA5MBda2fT+UAhtYq309C+DbwKeB5+r71YG/2H62vs/91H4bAg8Cp9fpNd+XtBK5lzqG7XuBbwF/pBTNjwI3knupEw1033RULZHiOYaa+mnLEi8dQtIo4GfAobb/2u7+xCKSdgfm2r6xubmfTXM/tddywBuBk21vDjxOpmh0lDpvdg/glcC6wEqUaQB95V7qXB31d1+K5xhq9wAbNL1fH7ivTX2JJpKWpxTO59ieUJsfaPyvsPpzbrv6F7wJ+FdJd1GmO72FMhI9uv6vZ8j91AnuAe6xPbW+P59STOde6hw7A3+w/aDtZ4AJwLbkXupEA903HVVLpHiOoXY9MLY+1TyS8pDGhW3u0zKvzp39AfA728c2fXQhsG99vS/w8+HuWxS2/9v2+rbHUO6by22/D7gC2KtulmvUZrb/DPxJ0mtr01uBW8i91En+CGwtacX6d1/jGuVe6jwD3TcXAh+oq25sDTzamN7RDglJiSEnaTfKiNkI4Ie2v9rmLi3zJG0HTAJmsmg+7Wco855/Aryc8h+cd9vu+0BHDDNJOwKH295d0oaUkejVgGnAPrYXtLN/yzpJ4ygPdY4E5gAfpAxO5V7qEJKOAt5LWWloGvBhypzZ3EttIulcYEdgDeAB4AvABfRz39R/9JxEWZ3jCeCDtm9oR78hxXNERERERMsybSMiIiIiokUpniMiIiIiWpTiOSIiIiKiRSmeIyIiIiJalOI5IiIiIqJFKZ4jIiIiIlqU4jkiIiIiokUpniMiIiIiWvT/AdF3mjgJLYmlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_test_10_annots.ClassName.value_counts().sort_values().plot(kind=\"barh\", title=\"10% of val-test classes distribution\", figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pillow                         101\n",
       "Bed                             72\n",
       "Swimming pool                   62\n",
       "Kitchen & dining room table     57\n",
       "Sink                            49\n",
       "Countertop                      47\n",
       "Sofa bed                        41\n",
       "Couch                           37\n",
       "Oven                            24\n",
       "Stairs                          24\n",
       "Mirror                          22\n",
       "Microwave oven                  20\n",
       "Billiard table                  19\n",
       "Washing machine                 19\n",
       "Porch                           18\n",
       "Toilet                          16\n",
       "Gas stove                       16\n",
       "Television                      14\n",
       "Fountain                        13\n",
       "Fireplace                       13\n",
       "Refrigerator                    13\n",
       "Towel                           10\n",
       "Ceiling fan                      9\n",
       "Wine rack                        9\n",
       "Bathtub                          9\n",
       "Jacuzzi                          8\n",
       "Shower                           5\n",
       "Tree house                       5\n",
       "Coffeemaker                      4\n",
       "Dishwasher                       2\n",
       "Name: ClassName, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_test_10_annots.ClassName.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save val_test_10% annotations to CSV\n",
    "val_test_10_annots.to_csv(\"subset-val-test-10-annotations.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create labels of images/image splits \n",
    "\n",
    "Create all JSON labels for all splits of data. Check to see if the preprocessing functions work for each dataset split. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from preprocessing import get_image_dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using validation-annotations-bbox.csv for annotations...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3it [00:00, 28.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On dataset: validation\n",
      "Classes we're using:\n",
      " Swimming pool                  161\n",
      "Bed                            125\n",
      "Kitchen & dining room table     76\n",
      "Pillow                          76\n",
      "Countertop                      68\n",
      "Couch                           61\n",
      "Sofa bed                        61\n",
      "Sink                            57\n",
      "Porch                           52\n",
      "Stairs                          45\n",
      "Television                      44\n",
      "Fireplace                       41\n",
      "Washing machine                 40\n",
      "Toilet                          37\n",
      "Oven                            36\n",
      "Mirror                          33\n",
      "Billiard table                  32\n",
      "Microwave oven                  30\n",
      "Refrigerator                    26\n",
      "Fountain                        24\n",
      "Gas stove                       23\n",
      "Coffeemaker                     21\n",
      "Bathtub                         18\n",
      "Wine rack                       17\n",
      "Jacuzzi                         16\n",
      "Ceiling fan                     11\n",
      "Tree house                      11\n",
      "Shower                           9\n",
      "Towel                            9\n",
      "Dishwasher                       3\n",
      "Name: ClassName, dtype: int64\n",
      "Total number of images: 859\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "859it [00:23, 35.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving labels to: validation/validation_labels.json...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Create validation image dictionaries\n",
    "val_img_dicts = get_image_dicts(\"validation\",\n",
    "                                \"validation-annotations-bbox.csv\",\n",
    "                                target_classes=subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using val-test-annotations.csv for annotations...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3it [00:00, 28.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On dataset: train\n",
      "Classes we're using:\n",
      " Swimming pool                  515\n",
      "Bed                            453\n",
      "Pillow                         340\n",
      "Kitchen & dining room table    233\n",
      "Countertop                     211\n",
      "Sofa bed                       211\n",
      "Sink                           176\n",
      "Television                     168\n",
      "Stairs                         167\n",
      "Couch                          165\n",
      "Washing machine                161\n",
      "Porch                          152\n",
      "Mirror                         117\n",
      "Billiard table                 117\n",
      "Refrigerator                   108\n",
      "Fireplace                      103\n",
      "Toilet                          98\n",
      "Oven                            89\n",
      "Fountain                        87\n",
      "Bathtub                         77\n",
      "Microwave oven                  76\n",
      "Gas stove                       72\n",
      "Coffeemaker                     71\n",
      "Ceiling fan                     51\n",
      "Jacuzzi                         44\n",
      "Shower                          41\n",
      "Towel                           38\n",
      "Wine rack                       35\n",
      "Tree house                      30\n",
      "Dishwasher                       6\n",
      "Name: ClassName, dtype: int64\n",
      "Total number of images: 2971\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2971it [01:13, 40.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving labels to: val_test/train_labels.json...\n"
     ]
    }
   ],
   "source": [
    "# Create val_test image dictionaries\n",
    "val_test_img_dicts = get_image_dicts(\"val_test\",\n",
    "                                     \"val-test-annotations.csv\",\n",
    "                                     target_classes=subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using train-annotations-bbox.csv for annotations...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On dataset: train\n",
      "Classes we're using:\n",
      " Stairs                         5981\n",
      "Couch                          4259\n",
      "Swimming pool                  3881\n",
      "Porch                          3854\n",
      "Television                     3789\n",
      "Fountain                       3691\n",
      "Bed                            3563\n",
      "Pillow                         3508\n",
      "Countertop                     3113\n",
      "Kitchen & dining room table    2127\n",
      "Sink                           1648\n",
      "Mirror                         1572\n",
      "Sofa bed                       1501\n",
      "Toilet                         1099\n",
      "Billiard table                  912\n",
      "Fireplace                       711\n",
      "Washing machine                 655\n",
      "Oven                            637\n",
      "Refrigerator                    592\n",
      "Bathtub                         545\n",
      "Gas stove                       526\n",
      "Microwave oven                  485\n",
      "Ceiling fan                     478\n",
      "Towel                           338\n",
      "Coffeemaker                     323\n",
      "Wine rack                       254\n",
      "Shower                          235\n",
      "Tree house                      110\n",
      "Jacuzzi                         103\n",
      "Dishwasher                       92\n",
      "Name: ClassName, dtype: int64\n",
      "Total number of images: 34835\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "34835it [27:27, 21.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving labels to: train/train_labels.json...\n"
     ]
    }
   ],
   "source": [
    "# Create train image dictionaries\n",
    "train_img_dicts = get_image_dicts(\"train\",\n",
    "                                  \"train-annotations-bbox.csv\",\n",
    "                                  target_classes=subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using train-annotations-bbox.csv for annotations...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1it [00:00,  9.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On dataset: train\n",
      "Classes we're using:\n",
      " Stairs                         561\n",
      "Pillow                         433\n",
      "Couch                          419\n",
      "Television                     392\n",
      "Porch                          379\n",
      "Bed                            375\n",
      "Swimming pool                  370\n",
      "Fountain                       357\n",
      "Countertop                     321\n",
      "Kitchen & dining room table    243\n",
      "Sink                           154\n",
      "Mirror                         136\n",
      "Sofa bed                       134\n",
      "Toilet                         106\n",
      "Billiard table                  85\n",
      "Oven                            64\n",
      "Bathtub                         60\n",
      "Washing machine                 59\n",
      "Fireplace                       59\n",
      "Refrigerator                    59\n",
      "Ceiling fan                     53\n",
      "Microwave oven                  47\n",
      "Coffeemaker                     47\n",
      "Wine rack                       45\n",
      "Gas stove                       41\n",
      "Towel                           31\n",
      "Shower                          30\n",
      "Jacuzzi                         14\n",
      "Tree house                      12\n",
      "Dishwasher                       8\n",
      "Name: ClassName, dtype: int64\n",
      "Total number of images: 3483\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3483it [01:36, 36.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving labels to: train_10/train_labels.json...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Create train 10% image dictionaries\n",
    "train_10_img_dicts = get_image_dicts(\"train_10\",\n",
    "                                     \"train-annotations-bbox.csv\",\n",
    "                                     target_classes=subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using val-test-annotations-bbox.csv for annotations...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "6it [00:00, 58.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On dataset: train\n",
      "Classes we're using:\n",
      " Pillow                         101\n",
      "Bed                             72\n",
      "Swimming pool                   62\n",
      "Kitchen & dining room table     57\n",
      "Sink                            49\n",
      "Countertop                      47\n",
      "Sofa bed                        41\n",
      "Couch                           37\n",
      "Oven                            24\n",
      "Stairs                          24\n",
      "Mirror                          22\n",
      "Microwave oven                  20\n",
      "Billiard table                  19\n",
      "Washing machine                 19\n",
      "Porch                           18\n",
      "Toilet                          16\n",
      "Gas stove                       16\n",
      "Television                      14\n",
      "Fountain                        13\n",
      "Fireplace                       13\n",
      "Refrigerator                    13\n",
      "Towel                           10\n",
      "Ceiling fan                      9\n",
      "Wine rack                        9\n",
      "Bathtub                          9\n",
      "Jacuzzi                          8\n",
      "Shower                           5\n",
      "Tree house                       5\n",
      "Coffeemaker                      4\n",
      "Dishwasher                       2\n",
      "Name: ClassName, dtype: int64\n",
      "Total number of images: 401\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "401it [00:07, 56.83it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving labels to: val_test_10/train_labels.json...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Create val_test 10% image dictionaries\n",
    "val_test_10_img_dicts = get_image_dicts(\"val_test_10\",\n",
    "                                        \"val-test-annotations-bbox.csv\",\n",
    "                                        target_classes=subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using test-annotations-bbox.csv for annotations...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2it [00:00, 15.69it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On dataset: train\n",
      "Classes we're using:\n",
      " Swimming pool                  354\n",
      "Bed                            328\n",
      "Pillow                         264\n",
      "Kitchen & dining room table    157\n",
      "Sofa bed                       150\n",
      "Countertop                     143\n",
      "Television                     124\n",
      "Stairs                         122\n",
      "Washing machine                121\n",
      "Sink                           119\n",
      "Couch                          104\n",
      "Porch                          100\n",
      "Billiard table                  85\n",
      "Mirror                          84\n",
      "Refrigerator                    82\n",
      "Fountain                        63\n",
      "Fireplace                       62\n",
      "Toilet                          61\n",
      "Bathtub                         59\n",
      "Oven                            53\n",
      "Coffeemaker                     50\n",
      "Gas stove                       49\n",
      "Microwave oven                  46\n",
      "Ceiling fan                     40\n",
      "Shower                          32\n",
      "Towel                           29\n",
      "Jacuzzi                         28\n",
      "Tree house                      19\n",
      "Wine rack                       18\n",
      "Dishwasher                       3\n",
      "Name: ClassName, dtype: int64\n",
      "Total number of images: 2112\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2112it [00:58, 36.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving labels to: test/train_labels.json...\n"
     ]
    }
   ],
   "source": [
    "# Create test labels in JSON\n",
    "test_img_dicts = get_image_dicts(\"test\",\n",
    "                                 \"test-annotations-bbox.csv\",\n",
    "                                 target_classes=subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying file://train_10/train_10_labels.json [Content-Type=application/json]...\n",
      "/ [1/1 files][751.6 KiB/751.6 KiB] 100% Done                                    \n",
      "Operation completed over 1 objects/751.6 KiB.                                    \n"
     ]
    }
   ],
   "source": [
    "# # Save JSON labels to GCP\n",
    "# !gsutil -m cp -r val_test_10/*.json gs://airbnb-amenity-detection/open-images-data\n",
    "# !gsutil -m cp -r val_test/*.json gs://airbnb-amenity-detection/open-images-data\n",
    "# !gsutil -m cp -r test/*.json gs://airbnb-amenity-detection/open-images-data\n",
    "# !gsutil -m cp -r validation/*.json gs://airbnb-amenity-detection/open-images-data\n",
    "# !gsutil -m cp -r train/*.json gs://airbnb-amenity-detection/open-images-data\n",
    "!gsutil -m cp -r train_10/*.json gs://airbnb-amenity-detection/open-images-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying file://output/model_0169999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/events.out.tfevents.1585457051.airbnb-amenity-detection-image-1.6086.0 [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0099999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0109999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0164999.pth [Content-Type=application/octet-stream]...\n",
      "==> NOTE: You are uploading one or more large file(s), which would run\n",
      "significantly faster if you enable parallel composite uploads. This\n",
      "feature can be enabled by editing the\n",
      "\"parallel_composite_upload_threshold\" value in your .boto\n",
      "configuration file. However, note that if you do this large files will\n",
      "be uploaded as `composite objects\n",
      "<https://cloud.google.com/storage/docs/composite-objects>`_,which\n",
      "means that any user who downloads such objects will need to have a\n",
      "compiled crcmod installed (see \"gsutil help crcmod\"). This is because\n",
      "without a compiled crcmod, computing checksums on composite objects is\n",
      "so slow that gsutil disables downloads of composite objects.\n",
      "\n",
      "Copying file://output/model_0089999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0124999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/last_checkpoint [Content-Type=application/octet-stream]...\n",
      "Copying file://output/events.out.tfevents.1585459422.airbnb-amenity-detection-image-1.1419.0 [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0009999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/config.yaml [Content-Type=application/octet-stream]...    \n",
      "Copying file://output/model_0069999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0049999.pth [Content-Type=application/octet-stream]...\n",
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      "Copying file://output/model_0139999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0129999.pth [Content-Type=application/octet-stream]...\n",
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      "Copying file://output/model_0104999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0159999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0094999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0134999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0044999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0149999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0074999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0029999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0119999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0004999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/events.out.tfevents.1585457813.airbnb-amenity-detection-image-1.9147.0 [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0054999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/log.txt [Content-Type=text/plain]...\n",
      "Copying file://output/model_0014999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0019999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0079999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0179999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0034999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0114999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/metrics.json [Content-Type=application/json]...           \n",
      "Copying file://output/model_0154999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0084999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0144999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0039999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_0024999.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/model_final.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/inference/val_test/instances_predictions.pth [Content-Type=application/octet-stream]...\n",
      "Copying file://output/inference/val_test/val_test_coco_format.json.lock [Content-Type=application/octet-stream]...\n",
      "Copying file://output/inference/val_test/coco_instances_results.json [Content-Type=application/json]...\n",
      "Copying file://output/inference/val_test/val_test_coco_format.json [Content-Type=application/json]...\n",
      "- [48/48 files][ 15.4 GiB/ 15.4 GiB] 100% Done 159.5 MiB/s ETA 00:00:00         \n",
      "Operation completed over 48 objects/15.4 GiB.                                    \n"
     ]
    }
   ],
   "source": [
    "!gsutil -m cp -r output gs://airbnb-amenity-detection/open-images-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated property [core/project].\n",
      "\u001b[1;33mWARNING:\u001b[0m You do not appear to have access to project [daniels-dl-playground] or it does not exist.\n",
      "\n",
      "\n",
      "To take a quick anonymous survey, run:\n",
      "  $ gcloud survey\n",
      "\n",
      "gs://airbnb-amenity-detection/\n"
     ]
    }
   ],
   "source": [
    "project_id = 'daniels-dl-playground'\n",
    "!gcloud config set project {project_id}\n",
    "!gsutil ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "You are running on a Google Compute Engine virtual machine.\n",
      "It is recommended that you use service accounts for authentication.\n",
      "\n",
      "You can run:\n",
      "\n",
      "  $ gcloud config set account `ACCOUNT`\n",
      "\n",
      "to switch accounts if necessary.\n",
      "\n",
      "Your credentials may be visible to others with access to this\n",
      "virtual machine. Are you sure you want to authenticate with\n",
      "your personal account?\n",
      "\n",
      "Do you want to continue (Y/n)?  ^C\n",
      "\n",
      "\n",
      "Command killed by keyboard interrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!gcloud auth login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/003b53edf0d0773c.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/0050f23b3c75999b.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/0052ea56ee869426.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/0056ffa38a8489b6.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/0093327faea501b8.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/007f71665b0812a7.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/00a55e292e46550a.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/00abaabafe892423.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/00cd932265611688.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/00ce2a25a3a64537.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/00d480139b4eff48.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/00ec4ba83d648c33.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/00f3dbbbf5bdf2cf.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/00ff185256c0dc67.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/0116b69b0eaedaaf.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/012570c9ec60f9c4.jpg...\n",
      "Copying gs://airbnb-amenity-detection/open-images-data/val_test/014ce4294afbd04e.jpg...\n",
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      "Operation completed over 3.0k objects/803.8 MiB.                                 \n"
     ]
    }
   ],
   "source": [
    "!gsutil -m cp -r gs://airbnb-amenity-detection/open-images-data/val_test ."
   ]
  }
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