main.ipynb

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      "Collecting pycocotools\r\n",
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      "Building wheels for collected packages: pycocotools\r\n",
      "  Building wheel for pycocotools (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \bdone\r\n",
      "\u001b[?25h  Created wheel for pycocotools: filename=pycocotools-2.0.2-cp37-cp37m-linux_x86_64.whl size=272259 sha256=0f6b8adea703800297ceaebb595e77615f0ec2b7ef929a51fb8e8000096058a5\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/bc/cf/1b/e95c99c5f9d1648be3f500ca55e7ce55f24818b0f48336adaf\r\n",
      "Successfully built pycocotools\r\n",
      "Installing collected packages: pycocotools\r\n",
      "Successfully installed pycocotools-2.0.2\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Collecting segmentation_models\r\n",
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      "Installing collected packages: keras-applications, image-classifiers, efficientnet, segmentation-models\r\n",
      "Successfully installed efficientnet-1.0.0 image-classifiers-1.0.0 keras-applications-1.0.8 segmentation-models-1.0.1\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "env: SM_FRAMEWORK=tf.keras\n"
     ]
    }
   ],
   "source": [
    "# Install the necessary libraries\n",
    "!pip install pycocotools\n",
    "!pip install segmentation_models\n",
    "%env SM_FRAMEWORK=tf.keras"
   ]
  },
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     "text": [
      "Segmentation Models: using `tf.keras` framework.\n"
     ]
    }
   ],
   "source": [
    "# Import the necessary libraries\n",
    "from pycocotools.coco import COCO\n",
    "import os\n",
    "import random\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import imgaug.augmenters as iaa\n",
    "import matplotlib.pyplot as plt\n",
    "import segmentation_models as sm"
   ]
  },
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   "source": [
    "# Utilised functions\n",
    "def getCOCO(anns_path):\n",
    "    \n",
    "    # Initialise the COCO api object\n",
    "    coco = COCO(anns_path)\n",
    "    \n",
    "    # Load the categories\n",
    "    catIDs = coco.getCatIds()\n",
    "    cats = coco.loadCats(catIDs)\n",
    "    \n",
    "    # Print the number of categories\n",
    "    nms = [cat['name'] for cat in cats]\n",
    "    \n",
    "    # Create a dataframe of the count of each category\n",
    "    df = pd.DataFrame(columns=['Category', 'ID', 'Count'])\n",
    "    \n",
    "    # Add each category and its count row by row\n",
    "    for i, catID in enumerate(catIDs):\n",
    "        imgIds = coco.getImgIds(catIds=catID)\n",
    "        df.loc[i] = [nms[i]] + [catID] + [len(imgIds)]\n",
    "    \n",
    "    return coco, df\n",
    "\n",
    "def getMask(image_id, coco, classes, image_size):\n",
    "    \n",
    "    # Create a zero array with the given size and number of classes\n",
    "    mask = np.zeros((image_size[0], image_size[1], 274))\n",
    "\n",
    "    annIds = coco.getAnnIds(int(image_id))\n",
    "    anns = coco.loadAnns(annIds)\n",
    "\n",
    "    for i, ann in enumerate(anns):\n",
    "        \n",
    "        # Get the binary mask for the annotation\n",
    "        binary = cv2.resize(coco.annToMask(ann), image_size, interpolation=cv2.INTER_AREA)\n",
    "\n",
    "        # Get the channel index for the annotation\n",
    "        channel = classes[classes.ID == ann['category_id']].index[0] + 1\n",
    "\n",
    "        # Update the channel of the annotation\n",
    "        mask[:, :, channel] = binary\n",
    "\n",
    "        # Update the background channel of the annotation\n",
    "        if i == 0:\n",
    "            mask[:, :, 0] = np.logical_or(mask[:, :, 0], np.logical_not(binary))\n",
    "        else:\n",
    "            mask[:, :, 0] = np.logical_and(mask[:, :, 0], np.logical_not(binary))\n",
    "\n",
    "    return mask\n",
    "\n",
    "def getGenerator(path, coco, classes, image_size, batch_size, augment=False):\n",
    "    \n",
    "    # Initialise the current count and image list\n",
    "    c = 0\n",
    "    n = os.listdir(path)\n",
    "    random.shuffle(n)\n",
    "    \n",
    "    # Initialise the augmentator object if required\n",
    "    if augment == True:\n",
    "        geo_aug = iaa.Fliplr(1.0)\n",
    "    \n",
    "    while (True):\n",
    "        \n",
    "        # Create the initial image and mask batches\n",
    "        img_batch = np.zeros((batch_size, image_size[0], image_size[1], 3)).astype('int')\n",
    "        mask_batch = np.zeros((batch_size, image_size[0], image_size[1], 274)).astype('float')\n",
    "\n",
    "        for i in range(c, c + batch_size):\n",
    "            \n",
    "            # Read the image from the image folder\n",
    "            img = cv2.imread(path + '/' + n[i])\n",
    "            \n",
    "            # Resize the image according to the given image size\n",
    "            img =  cv2.resize(img, image_size, interpolation=cv2.INTER_AREA)\n",
    "            \n",
    "            # Convert the image from BGR to RGB\n",
    "            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "            \n",
    "            # Get the multi-channel mask\n",
    "            mask = getMask(n[i][1:-4], coco, classes, image_size)\n",
    "            \n",
    "            # Apply augmentations if required\n",
    "            if augment == True:\n",
    "                if random.random() < 0.5:\n",
    "                    img = geo_aug(image=img)\n",
    "                    mask = geo_aug(image=mask)            \n",
    "            \n",
    "            # Add the image and mask to the image and mask batches\n",
    "            img_batch[i-c] = img\n",
    "            mask_batch[i-c] = mask\n",
    "        \n",
    "        # If current count exceeds the total images, reset to 0 and shuffle image list\n",
    "        c += batch_size\n",
    "        if(c + batch_size >= len(os.listdir(path))):\n",
    "            c = 0\n",
    "            random.shuffle(n)\n",
    "\n",
    "        yield img_batch, mask_batch\n",
    "        \n",
    "def visualiseSamples(path, coco, classes, image_size, batch_size, augment=False):\n",
    "    temp_gen = getGenerator(path, coco, classes, image_size, batch_size, augment)\n",
    "    images, masks = next(temp_gen)\n",
    "    \n",
    "    for i in range(batch_size):\n",
    "        plt.figure(figsize=(8, 16))\n",
    "        \n",
    "        plt.subplot(1, 2, 1)\n",
    "        plt.title('Image')\n",
    "        plt.imshow(images[i])\n",
    "        plt.axis('off')\n",
    "        \n",
    "        plt.subplot(1, 2, 2)\n",
    "        plt.title('True Mask')\n",
    "        \n",
    "        true_mask = masks[i]\n",
    "        for c in range(274):\n",
    "            true_mask[:, :, c] = true_mask[:, :, c] * (c+1)\n",
    "            \n",
    "        plt.imshow(np.max(true_mask, axis=2))\n",
    "        plt.axis('off')\n",
    "        \n",
    "        plt.show()\n",
    "        \n",
    "def showPredictions(generator, model):\n",
    "    images, masks = next(generator)\n",
    "    prediction = model.predict(images)\n",
    "    \n",
    "    for i in range(5):\n",
    "        plt.figure(figsize=(10, 16))\n",
    "        \n",
    "        plt.subplot(1, 3, 1)\n",
    "        plt.title('Image')\n",
    "        plt.imshow(images[i])\n",
    "        plt.axis('off')\n",
    "        \n",
    "        plt.subplot(1, 3, 2)\n",
    "        plt.title('True Mask')\n",
    "        \n",
    "        full_mask = masks[i]\n",
    "        for c in range(274):\n",
    "            full_mask[:, :, c] = full_mask[:, :, c] * (c+1)\n",
    "            \n",
    "        plt.imshow(np.max(full_mask, axis=2))\n",
    "        plt.axis('off')\n",
    "        \n",
    "        pred_mask = np.round(prediction[i])\n",
    "        for c in range(274):\n",
    "            pred_mask[:, :, c] = pred_mask[:, :, c] * (c+1)\n",
    "            \n",
    "        plt.subplot(1, 3, 3)\n",
    "        plt.title('Predicted Mask')\n",
    "        plt.imshow(np.max(pred_mask, axis=2))\n",
    "        plt.axis('off')\n",
    "        \n",
    "        plt.show()"
   ]
  },
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   "source": [
    "# Data Preparation"
   ]
  },
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   "source": [
    "# Set the hyperparameters\n",
    "IMAGE_SIZE = (48, 48)\n",
    "BATCH_SIZE = 300\n",
    "EPOCHS = 10"
   ]
  },
  {
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   "source": [
    "# Define the image and annotation paths\n",
    "train_imgs_path = '../input/food-recognition-images/train-v0.4/train/images'\n",
    "train_anns_path = '../input/food-recognition-images/train-v0.4/train/annotations.json'\n",
    "\n",
    "val_imgs_path = '../input/food-recognition-images/val-v0.4/val/images'\n",
    "val_anns_path = '../input/food-recognition-images/val-v0.4/val/annotations.json'"
   ]
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    {
     "name": "stdout",
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     "text": [
      "loading annotations into memory...\n",
      "Done (t=3.81s)\n",
      "creating index...\n",
      "index created!\n",
      "loading annotations into memory...\n",
      "Done (t=0.17s)\n",
      "creating index...\n",
      "index created!\n"
     ]
    }
   ],
   "source": [
    "# Load the annotations of the image dataset\n",
    "train_coco, train_classes = getCOCO(train_anns_path)\n",
    "val_coco, val_classes = getCOCO(val_anns_path)"
   ]
  },
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     "iopub.status.busy": "2021-11-05T11:24:12.966906Z",
     "iopub.status.idle": "2021-11-05T11:24:13.937372Z",
     "shell.execute_reply": "2021-11-05T11:24:13.936813Z",
     "shell.execute_reply.started": "2021-11-05T08:27:06.761761Z"
    },
    "papermill": {
     "duration": 1.001565,
     "end_time": "2021-11-05T11:24:13.937586",
     "exception": false,
     "start_time": "2021-11-05T11:24:12.936021",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training images: 24119\n",
      "Number of validation images: 1269\n"
     ]
    }
   ],
   "source": [
    "# Get the training and validation generators\n",
    "train_gen = getGenerator(train_imgs_path, train_coco, train_classes, IMAGE_SIZE, BATCH_SIZE, True)\n",
    "train_length = len(os.listdir(train_imgs_path))\n",
    "print('Number of training images: {}'.format(train_length))\n",
    "\n",
    "val_gen = getGenerator(val_imgs_path, val_coco, val_classes, IMAGE_SIZE, BATCH_SIZE)\n",
    "val_length = len(os.listdir(val_imgs_path))\n",
    "print('Number of validation images: {}'.format(val_length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6dab163f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T11:24:13.998389Z",
     "iopub.status.busy": "2021-11-05T11:24:13.997386Z",
     "iopub.status.idle": "2021-11-05T11:24:14.253535Z",
     "shell.execute_reply": "2021-11-05T11:24:14.252903Z",
     "shell.execute_reply.started": "2021-11-05T08:27:08.162375Z"
    },
    "papermill": {
     "duration": 0.289124,
     "end_time": "2021-11-05T11:24:14.253696",
     "exception": false,
     "start_time": "2021-11-05T11:24:13.964572",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dO70/ZfoAveF+i7apqkOmY12Q5BmbUuS0zweram1Vvaeq9p7W/+Z0jguq6v9U1U5VdVCSlyQ5pqrOr6odNzjWyVX1yaq6sKr+bFr3MwtGUP6pqvZapIZF21TVD1XVe6vqkqr6qyT/oS+mdntW1Znr21XV5VW1xzQqdFlV/U2Si5Pco6r+YLquC6vqpAXHeEpVfXy6rv9dVdtN679RVf9t6odzFqsfAAC2JpsyNeuxSb7Y3Qd29wOTvDvJK7v7IdP7HZP89CL7LdXmr5M8s7sP3JQCq+qOSV6R5EndfUiS1yf5b9Pmv5/OcWCSS5P8enefn+T5SU7v7oO6+9sLjvVDSZ6Q5AHd/aAkL5o2fSjJw7r7wUnenOS5i5SyVJs/TfKh7n5AkrcnuecSl/KnSd43tXvbBu32S/Kqadv9pvcPTXJQkkOq6tFV9aNJnpzkkd19UJKbkhw77X/nJOdM/XBWkt9cogYAANgqbMrUrIuS/M+qenGSd3b32VX1c1X13CQ7JblrkkuS/OMG+x2xYZuqOjvJ7t191tTmb5McdQvnv1+SByY5cxpU2S7JldO2B1bVi5LsnmTnJO+5hWNdm+Q7SV5XVe9M8s5p/d2TnD6NtNwpyecX2XepNo9O8sQk6e4zqurrS5z7UZmFoHT3uzdod3l3nzMtHzm9PjG93zmzYPKgJIckOXfqhx2TXDW1+d6Ca1mb5CcXK6Cqjk9yfJJst+ueS5QJAADL7xZHRLr7X5IcnFkgeVFVPT/JqzIboTggyWuT7LBwn6ra4ZbabKiq/nqacvSuDTcluWQa3Tiouw/o7iOnbW9I8jvTOU5a7BzTVK7zq+qvuvvGzEYa3pbZCM27p2avyGwE54Akv7VErZvSZuF5nzGd9/yqutvG2ib55gbX+z8WXO99uvt10/o3Llh/v+5+wbTPDd3d0/JNWSJgdvcp3X1odx+63U673UJJAACwfDblGZG7JflWd78pyUszCyVJ8tWq2jnJYt9stcNibbr7miTXVNWjpu3rpxalu582fcB+3AbHuizJnlX18KmeO1bVA6ZtuyS5cpq+dWwW0d0/NR33N6ZaduvudyX5vSTrp4ftluSKafm4JbpiqTZnJfnlqbajktxlOu9fLggNX0zy4SS/MLU7cn27Rbwnya9Ntaaq9qmqH07yz0meNC2nqu5aVfsucQwAANiqbcrUrAOSvLSqbk5yQ5LfTvL4zB6s/lKSczfcobuvqarXLtHmaUleX1Wd5L23dPLu/l7Nvsb35VW121TzyzKbDvYnST6W5CvTz11u4XC7JPmHacSmkjxnWv+CJG+dpku9L8m9Ftl3qTYnJTmtqi5J8pEk/7rEude3+5UkH82sX67PbOrVwut97/Q8yEenKVjfSPKU7v5kVT0vyXur6g6Z/Vk8I8nlt3DNAACw1anvz+hhOVXV9klu6u4bp9GdV08Pnc/F9nvv13sf97J5nR6AFWTdyUfPuwRgTqpqbXcfuti22+v/I7I1umeSt0yjGd+Lb7YCAGAFE0QG6e5PJ3nwvOsAAICtwab8PyIAAABblCACAAAMJ4gAAADDCSIAAMBwgggAADCcIAIAAAwniAAAAMMJIgAAwHCCCAAAMJwgAgAADCeIAAAAwwkiAADAcIIIAAAwnCACAAAMJ4gAAADDCSIAAMBwgggAADDcqnkXwHwcsM9uWXPy0fMuAwCAFcqICAAAMJwgAgAADCeIAAAAwwkiAADAcIIIAAAwnCACAAAMJ4gAAADDCSIAAMBwgggAADCcIAIAAAwniAAAAMMJIgAAwHCr5l0A83HRFddm9YlnzLsMAG6DdScfPe8SAG41IyIAAMBwgggAADCcIAIAAAwniAAAAMMJIgAAwHCCCAAAMJwgAgAADCeIAAAAwwkiAADAcIIIAAAwnCACAAAMJ4gAAADDCSIAAMBwgggAADCcIAIAAAwniAAAAMMJIgAAwHCCCAAAMJwgAgAADCeIAAAAwwkiW1BV7V5VT1/mcxxeVY9YznMAAMByE0S2rN2TLGsQSXJ4EkEEAIBt2qp5F3A7c3KSe1fV+UnOnNYdlaSTvKi7T6+qw5OclOSaJAckeUuSi5I8K8mOSR7f3Z+tqp9J8rwkd0pydZJjp+0nJLmpqp6S5JlJvpDk9Un2SPKVJE/r7n9d7gsFAIDbwojIlnViks9290FJzklyUJIDkzwmyUurau+p3YGZBYofTfIrSe7b3Q9N8leZhYsk+VCSh3X3g5O8Oclzu3tdktck+YvuPqi7z07yiiRv7O4HJTk1ycuXKq6qjq+qNVW15qZvXbvlrhoAADaTEZHl86gkp3X3TUm+XFUfTPKQJNclObe7r0ySqvpskvdO+1yU5Ihp+e5JTp/Cy52SfH6J8zw8yROn5b9N8pKlCuruU5KckiTb771f38rrAgCA28yIyHx8d8HyzQve35zvh8NXJHlldx+Q5LeS7DCuPAAAWF6CyJZ1fZJdpuWzkzy5qrarqj2TPDrJxzfjWLsluWJaPm6JcyTJR5L84rR87HReAADYqgkiW1B3X53kw1V1cWZTpi5MckGS92X2jMeXNuNwL0jy1qpam+SrC9b/Y5InVNX5VXVYZs+UPK2qLszseZNn3fYrAQCA5VXdHhVYibbfe7/e+7iXzbsMAG6DdScfPe8SADaqqtZ296GLbTMiAgAADCeIAAAAwwkiAADAcIIIAAAwnCACAAAMJ4gAAADDCSIAAMBwgggAADCcIAIAAAwniAAAAMMJIgAAwHCCCAAAMJwgAgAADCeIAAAAwwkiAADAcIIIAAAwnCACAAAMJ4gAAADDCSIAAMBwq+ZdAPNxwD67Zc3JR8+7DAAAVigjIgAAwHCCCAAAMJwgAgAADCeIAAAAwwkiAADAcIIIAAAwnCACAAAMJ4gAAADDCSIAAMBwgggAADCcIAIAAAwniAAAAMOtmncBzMdFV1yb1SeeMe8yAFaMdScfPe8SALYqRkQAAIDhBBEAAGA4QQQAABhOEAEAAIYTRAAAgOEEEQAAYDhBBAAAGE4QAQAAhhNEAACA4QQRAABgOEEEAAAYThABAACGE0QAAIDhBBEAAGA4QQQAABhOEAEAAIYTRAAAgOEEEQAAYDhBBAAAGE4QAQAAhhNEBqiq1VV18Wa0P7yqHrHg/eOrav/lqQ4AAMYTRLZOhyd5xIL3j0+yWUGkqlZtwXoAAGCLEkTGWVVVp1bVpVX1tqraqarWVdUeSVJVh1bVB6pqdZITkvxeVZ1fVT+W5GeTvHR6f+/p9e6qWltVZ1fV/adjvKGqXlNVH0vyknldKAAA3BL/aj7O/ZL8end/uKpen+TpizXq7nVV9Zok3+juP0uSqnpHknd299um9/+c5ITu/nRV/eckr0ry49Mh7p7kEd1904bHrqrjkxyfJNvtuueWvToAANgMgsg4X+juD0/Lb0ryu7fmIFW1c2bTtt5aVetXb7+gyVsXCyFJ0t2nJDklSbbfe7++NecHAIAtQRAZZ8MP/p3kxnx/etwOm3icOyS5prsPWmL7Nze/NAAAGMszIuPcs6oePi3/cpIPJVmX5JBp3c8taHt9kl0We9/d1yX5fFX9fJLUzIHLWDcAAGxxgsg4lyV5RlVdmuQuSV6d5KQk/6uq1iRZOJ3qH5M8YXo4/bAkb07yB1X1iaq6d5Jjk/x6VV2Q5JIkx4y8EAAAuK1MzRqgu9cluf8im85Oct9F2v9LkgdtsHrDr+997CL7PfXWVQgAAGMZEQEAAIYTRAAAgOEEEQAAYDhBBAAAGE4QAQAAhhNEAACA4QQRAABgOEEEAAAYThABAACGE0QAAIDhBBEAAGA4QQQAABhOEAEAAIYTRAAAgOEEEQAAYDhBBAAAGE4QAQAAhhNEAACA4QQRAABguFXzLoD5OGCf3bLm5KPnXQYAACuUEREAAGA4QQQAABhOEAEAAIYTRAAAgOEEEQAAYDhBBAAAGE4QAQAAhhNEAACA4QQRAABgOEEEAAAYThABAACGE0QAAIDhBBEAAGA4QQQAABiuunveNTAHVXV9ksvmXcft1B5JvjrvIm6n9O3y0K/LR98uH327fPTt8lmJfbtvd++52IZVoythq3FZdx867yJuj6pqjb5dHvp2eejX5aNvl4++XT76dvno2x9kahYAADCcIAIAAAwniKxcp8y7gNsxfbt89O3y0K/LR98uH327fPTt8tG3C3hYHQAAGM6ICAAAMJwgssJU1WOr6rKq+kxVnTjverY1VXWPqnp/VX2yqi6pqmdN619QVVdU1fnT63EL9vnDqb8vq6qfml/1W7+qWldVF019uGZad9eqOrOqPj39vMu0vqrq5VPfXlhVB8+3+q1XVd1vwb15flVdV1XPdt/eOlX1+qq6qqouXrBus+/Tqjpuav/pqjpuHteytVmib19aVZ+a+u/tVbX7tH51VX17wf37mgX7HDL9LvnM1P81h8vZqizRt5v9O8DniP9oib49fUG/rquq86f17tuFuttrhbySbJfks0n+U5I7Jbkgyf7zrmtbeiXZO8nB0/IuSf4lyf5JXpDk9xdpv//Uz9snudfU/9vN+zq21leSdUn22GDdS5KcOC2fmOTF0/Ljkvy/JJXkYUk+Nu/6t4XX9HvgS0n2dd/e6j58dJKDk1y8YN1m3adJ7prkc9PPu0zLd5n3tc37tUTfHplk1bT84gV9u3phuw2O8/Gpv2vq/6PmfW3zfi3Rt5v1O8DniE3v2w22/88kz5+W3bcLXkZEVpaHJvlMd3+uu7+X5M1JjplzTduU7r6yu8+blq9PcmmSfTayyzFJ3tzd3+3uzyf5TGZ/Dmy6Y5K8cVp+Y5LHL1j/Nz1zTpLdq2rvOdS3rfmJJJ/t7ss30sZ9uxHdfVaSr22wenPv059KcmZ3f627v57kzCSPXfbit3KL9W13v7e7b5zenpPk7hs7xtS/u3b3OT37dPc3+f6fx4q1xH27lKV+B/gcsYiN9e00qvELSU7b2DFW6n0riKws+yT5woL3/5aNf4hmI6pqdZIHJ/nYtOp3pqkDr18/LSP6fHN1kvdW1dqqOn5at1d3XzktfynJXtOyvr11fjE/+Bei+3bL2Nz7VB/fOr+W2b8Ur3evqvpEVX2wqg6b1u2TWX+up283bnN+B7hvN99hSb7c3Z9esM59OxFE4Faoqp2T/J8kz+7u65K8Osm9kxyU5MrMhmHZfI/q7oOTHJXkGVX16IUbp38l8lV/t1JV3SnJzyZ567TKfbsM3KfLo6r+OMmNSU6dVl2Z5J7d/eAkz0nyd1W167zq20b5HbD8fik/+I8/7tsFBJGV5Yok91jw/u7TOjZDVd0xsxByanf/fZJ095e7+6buvjnJa/P9aSz6fDN09xXTz6uSvD2zfvzy+ilX08+rpub6dvMdleS87v5y4r7dwjb3PtXHm6Gqnprkp5McOwW9TNOGrp6W12b27MJ9M+vHhdO39O0SbsXvAPftZqiqVUmemOT09evctz9IEFlZzk2yX1Xda/qX0V9M8o4517RNmeZ6vi7Jpd395wvWL3w24QlJ1n9zxjuS/GJVbV9V90qyX2YPo7GBqrpzVe2yfjmzB1QvzqwP13+j0HFJ/mFafkeSX52+lehhSa5dMDWGxf3Av8y5b7eozb1P35PkyKq6yzQd5shpHRuoqscmeW6Sn+3uby1Yv2dVbTct/6fM7tPPTf17XVU9bPqd/av5/p8HC9yK3wE+R2yexyT5VHf/+5Qr9+0PWjXvAhinu2+sqt/J7C+77ZK8vrsvmXNZ25pHJvmVJBet/yq+JH+U5Jeq6qDMpmOsS/JbSdLdl1TVW5J8MrMpBc/o7psG17yt2CvJ26dvK1yV5O+6+91VdW6St1TVrye5PLOH/pLkXZl9I9FnknwrydPGl7ztmMLdT2a6Nycvcd9uvqo6LcnhSfaoqn9L8qdJTs5m3Kfd/bWq+q+ZfbBLkhd296Y+SHy7tUTf/mFm39505vT74ZzuPiGzbyp6YVXdkOTmJCcs6MOnJ3lDkh0ze6Zk4XMlK9ISfXv45v4O8DniP1qsb7v7dfmPz+Ql7tsf4H9WBwAAhjM1CwAAGE4QAQAAhhNEAACA4QQRAABgOEEEAAAYThABYMWpqh+pqjdX1Weram1Vvauq7rsFj394VT1iSx0P4PZIEAFgRZn+s7C3J/lAd9+7uw/J7P+q2GsLnubwJIIIwEYIIgCsNEckuaG7X7N+RXdfkORDVfXSqrq4qi6qqicn/z668c71bavqlVX11Gl5XVWdVFXnTfvcv6pWJzkhye9V1flVddjIiwPYVvif1QFYaR6YZO0i65+Y5KAkBybZI8m5VXXWJhzvq919cFU9Pcnvd/dvVNVrknyju/9sSxUNcHtjRAQAZh6V5LTuvqm7v5zkg0kesgn7/f30c22S1ctUG8DtjiACwEpzSZJDNqP9jfnBvy932GD7d6efN8VMA4BNJogAsNK8L8n2VXX8+hVV9aAk1yR5clVtV1V7Jnl0ko8nuTzJ/lW1fVXtnuQnNuEc1yfZZUsXDnB74l9uAFhRurur6glJXlZV/yXJd5KsS/LsJDsnuSBJJ3lud38pSarqLUkuTvL5JJ/YhNP8Y5K3VdUxSZ7Z3Wdv6esA2NZVd8+7BgAAYIUxNQsAABhOEAEAAIYTRAAAgOEEEQAAYDhBBAAAGE4QAQAAhhNEAACA4QQRAABguP8Pm2Y2Vxw0pD0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the top 5 training classes by count\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "top_5 = train_classes.sort_values(by=['Count'], ascending=False)[:5]\n",
    "top_5_keys = list(top_5.Category)\n",
    "top_5_values = list(top_5.Count)\n",
    "\n",
    "top_5_keys.reverse()\n",
    "top_5_values.reverse()\n",
    "plt.barh(top_5_keys, top_5_values)\n",
    "plt.title('Top 5 Classes')\n",
    "plt.xlabel('Count')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "293b130e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T11:24:14.341650Z",
     "iopub.status.busy": "2021-11-05T11:24:14.340074Z",
     "iopub.status.idle": "2021-11-05T11:24:14.541311Z",
     "shell.execute_reply": "2021-11-05T11:24:14.540250Z",
     "shell.execute_reply.started": "2021-11-05T08:27:08.484594Z"
    },
    "papermill": {
     "duration": 0.259644,
     "end_time": "2021-11-05T11:24:14.541456",
     "exception": false,
     "start_time": "2021-11-05T11:24:14.281812",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the bottom 5 training classes by count\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "bottom_5 = train_classes.sort_values(by=['Count'], ascending=True)[:5]\n",
    "bottom_5_keys = list(bottom_5.Category)\n",
    "bottom_5_values = list(bottom_5.Count)\n",
    "\n",
    "bottom_5_keys.reverse()\n",
    "bottom_5_values.reverse()\n",
    "plt.barh(bottom_5_keys, bottom_5_values)\n",
    "plt.title('Bottom 5 Classes')\n",
    "plt.xlabel('Count')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82d412b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T11:24:14.603651Z",
     "iopub.status.busy": "2021-11-05T11:24:14.602567Z",
     "iopub.status.idle": "2021-11-05T11:24:16.083459Z",
     "shell.execute_reply": "2021-11-05T11:24:16.082934Z",
     "shell.execute_reply.started": "2021-11-05T08:27:08.769288Z"
    },
    "papermill": {
     "duration": 1.513043,
     "end_time": "2021-11-05T11:24:16.083621",
     "exception": false,
     "start_time": "2021-11-05T11:24:14.570578",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/imgaug/augmenters/base.py:59: SuspiciousSingleImageShapeWarning: You provided a numpy array of shape (48, 48, 274) as a single-image augmentation input, which was interpreted as (H, W, C). The last dimension however has a size of >=32, which indicates that you provided a multi-image array with shape (N, H, W) instead. If that is the case, you should use e.g. augmenter(imageS=<your input>) or augment_imageS(<your input>). Otherwise your multi-image input will be interpreted as a single image during augmentation.\n",
      "  category=SuspiciousSingleImageShapeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise sample images with their backgrounds and objects\n",
    "visualiseSamples(train_imgs_path, train_coco, train_classes, IMAGE_SIZE, 5, augment=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f924d3ea",
   "metadata": {
    "papermill": {
     "duration": 0.031289,
     "end_time": "2021-11-05T11:24:16.147357",
     "exception": false,
     "start_time": "2021-11-05T11:24:16.116068",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Semantic Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "81994129",
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2021-11-05T11:24:16.219105Z",
     "iopub.status.busy": "2021-11-05T11:24:16.218290Z",
     "iopub.status.idle": "2021-11-05T11:24:38.843793Z",
     "shell.execute_reply": "2021-11-05T11:24:38.843055Z"
    },
    "papermill": {
     "duration": 22.66438,
     "end_time": "2021-11-05T11:24:38.843989",
     "exception": false,
     "start_time": "2021-11-05T11:24:16.179609",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-05 11:24:16.307853: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-05 11:24:16.424863: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-05 11:24:16.426058: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-05 11:24:16.427845: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-11-05 11:24:16.429219: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-05 11:24:16.430667: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-05 11:24:16.431852: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-05 11:24:18.654717: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-05 11:24:18.655852: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-05 11:24:18.656839: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-05 11:24:18.657752: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/Callidior/keras-applications/releases/download/efficientnet/efficientnet-b7_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5\n",
      "258441216/258434480 [==============================] - 2s 0us/step\n",
      "258449408/258434480 [==============================] - 2s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# Define the model to be trained\n",
    "model = sm.PSPNet('efficientnetb7', encoder_weights='imagenet', encoder_freeze=True, \n",
    "                  input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3), downsample_factor=4, \n",
    "                  classes=274, activation='softmax')\n",
    "\n",
    "model.compile('Adam', loss=sm.losses.cce_dice_loss, \n",
    "              metrics=[sm.metrics.iou_score, sm.metrics.precision, sm.metrics.recall])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "96012918",
   "metadata": {
    "_kg_hide-output": false,
    "execution": {
     "iopub.execute_input": "2021-11-05T11:24:38.953959Z",
     "iopub.status.busy": "2021-11-05T11:24:38.952812Z",
     "iopub.status.idle": "2021-11-05T13:08:20.326456Z",
     "shell.execute_reply": "2021-11-05T13:08:20.325254Z"
    },
    "papermill": {
     "duration": 6221.43121,
     "end_time": "2021-11-05T13:08:20.327386",
     "exception": false,
     "start_time": "2021-11-05T11:24:38.896176",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-05 11:24:50.075656: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-05 11:25:00.371991: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80/80 [==============================] - 832s 10s/step - loss: 0.9827 - iou_score: 0.0156 - precision: 0.0307 - recall: 0.3901 - val_loss: 1.0109 - val_iou_score: 0.0052 - val_precision: 0.0326 - val_recall: 0.3687\n",
      "Epoch 2/10\n",
      "80/80 [==============================] - 635s 8s/step - loss: 0.9534 - iou_score: 0.0359 - precision: 0.0777 - recall: 0.4189 - val_loss: 0.9819 - val_iou_score: 0.0211 - val_precision: 0.0861 - val_recall: 0.4080\n",
      "Epoch 3/10\n",
      "80/80 [==============================] - 644s 8s/step - loss: 0.9051 - iou_score: 0.0816 - precision: 0.1739 - recall: 0.4283 - val_loss: 0.8517 - val_iou_score: 0.1444 - val_precision: 0.3442 - val_recall: 0.3982\n",
      "Epoch 4/10\n",
      "80/80 [==============================] - 617s 8s/step - loss: 0.7149 - iou_score: 0.2758 - precision: 0.5923 - recall: 0.4280 - val_loss: 0.7233 - val_iou_score: 0.2750 - val_precision: 0.6374 - val_recall: 0.4072\n",
      "Epoch 5/10\n",
      "80/80 [==============================] - 603s 8s/step - loss: 0.6767 - iou_score: 0.3116 - precision: 0.6696 - recall: 0.4419 - val_loss: 0.7036 - val_iou_score: 0.2915 - val_precision: 0.6614 - val_recall: 0.4099\n",
      "Epoch 6/10\n",
      "80/80 [==============================] - 589s 7s/step - loss: 0.6655 - iou_score: 0.3207 - precision: 0.6864 - recall: 0.4466 - val_loss: 0.7138 - val_iou_score: 0.2802 - val_precision: 0.6714 - val_recall: 0.4012\n",
      "Epoch 7/10\n",
      "80/80 [==============================] - 565s 7s/step - loss: 0.6576 - iou_score: 0.3275 - precision: 0.6959 - recall: 0.4503 - val_loss: 0.6927 - val_iou_score: 0.3014 - val_precision: 0.6869 - val_recall: 0.4047\n",
      "Epoch 8/10\n",
      "80/80 [==============================] - 561s 7s/step - loss: 0.6510 - iou_score: 0.3332 - precision: 0.7131 - recall: 0.4538 - val_loss: 0.6642 - val_iou_score: 0.3278 - val_precision: 0.6866 - val_recall: 0.4444\n",
      "Epoch 9/10\n",
      "80/80 [==============================] - 563s 7s/step - loss: 0.6427 - iou_score: 0.3407 - precision: 0.7188 - recall: 0.4620 - val_loss: 0.6867 - val_iou_score: 0.3071 - val_precision: 0.6820 - val_recall: 0.4322\n",
      "Epoch 10/10\n",
      "80/80 [==============================] - 564s 7s/step - loss: 0.6375 - iou_score: 0.3449 - precision: 0.7243 - recall: 0.4626 - val_loss: 0.6912 - val_iou_score: 0.3022 - val_precision: 0.6877 - val_recall: 0.4313\n"
     ]
    }
   ],
   "source": [
    "# Train the defined model on the dataset\n",
    "history = model.fit(train_gen, steps_per_epoch=train_length//BATCH_SIZE, \n",
    "                    validation_data=val_gen, validation_steps=val_length//BATCH_SIZE, \n",
    "                    epochs=EPOCHS)\n",
    "\n",
    "# Save the model weights to the output\n",
    "model.save_weights('weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "569a9d06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T13:08:21.019395Z",
     "iopub.status.busy": "2021-11-05T13:08:21.017058Z",
     "iopub.status.idle": "2021-11-05T13:08:22.132279Z",
     "shell.execute_reply": "2021-11-05T13:08:22.132898Z"
    },
    "papermill": {
     "duration": 1.474653,
     "end_time": "2021-11-05T13:08:22.133087",
     "exception": false,
     "start_time": "2021-11-05T13:08:20.658434",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 14))\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Loss against Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='upper left')\n",
    "\n",
    "# Plot training & validation iou values\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(history.history['iou_score'])\n",
    "plt.plot(history.history['val_iou_score'])\n",
    "plt.title('IoU against Epoch')\n",
    "plt.ylabel('IoU')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='upper left')\n",
    "\n",
    "# Plot training & validation precision values\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.plot(history.history['precision'])\n",
    "plt.plot(history.history['val_precision'])\n",
    "plt.title('Precision against Epoch')\n",
    "plt.ylabel('Precision')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='upper left')\n",
    "\n",
    "# Plot training & validation recall values\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.plot(history.history['recall'])\n",
    "plt.plot(history.history['val_recall'])\n",
    "plt.title('Recall against Epoch')\n",
    "plt.ylabel('Recall')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4e688cdb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T13:08:22.803123Z",
     "iopub.status.busy": "2021-11-05T13:08:22.801691Z",
     "iopub.status.idle": "2021-11-05T13:08:31.611473Z",
     "shell.execute_reply": "2021-11-05T13:08:31.610870Z"
    },
    "papermill": {
     "duration": 9.152067,
     "end_time": "2021-11-05T13:08:31.611643",
     "exception": false,
     "start_time": "2021-11-05T13:08:22.459576",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1152 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1152 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1152 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1152 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1152 with 3 Axes>"
      ]
     },
     "metadata": {
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     },
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    "# Visualise samples of the validation dataset and the corresponding predictions\n",
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