diff --git a/evaluation/detection.py b/evaluation/detection.py
new file mode 100644
index 0000000..4fd1deb
--- /dev/null
+++ b/evaluation/detection.py
@@ -0,0 +1,167 @@
+import cv2
+import torch
+import onnxruntime
+import numpy as np
+import pandas as pd
+import albumentations as A
+
+from torchvision import transforms, ops
+from albumentations.pytorch import ToTensorV2
+
+from utils.conversions import scale_bboxes
+from utils.manipulations import get_cutout
+
+def detect(img_path: str, yolo_path: str, resnet_path: str):
+    """Load an image, detect individual plants and label them as
+    healthy or wilted.
+
+    :param str img_path: path to image
+    :param str yolo_path: path to yolo weights
+    :param str resnet_path: path to resnet weights
+    :returns: tuple of recent image and dict of bounding boxes and
+    their predictions
+
+    """
+    img = cv2.imread(img_path)
+
+    # Get bounding boxes from object detection model
+    box_coords = get_boxes(yolo_path, img.copy())
+
+    box_coords.sort_values(by=['xmin'], ignore_index=True, inplace=True)
+
+    predictions = []
+    for _, row in box_coords.iterrows():
+        xmin, xmax = int(row['xmin']), int(row['xmax'])
+        ymin, ymax = int(row['ymin']), int(row['ymax'])
+
+        # Get tensor of ROI in BGR
+        cropped_image = get_cutout(img.copy(), xmin, xmax, ymin, ymax)
+
+        # Classify ROI in RGB
+        predictions.append(classify(resnet_path, cropped_image[..., ::-1]))
+
+    # Gather top class and confidence values
+    cls = []
+    cls_conf = []
+    for pred in predictions:
+        ans, index = torch.topk(pred, 1)
+        cls.append(index.int().item())
+        cls_conf.append(round(ans.double().item(), 6))
+
+    # Add predicted classes and confidence values to pandas dataframe
+    box_coords['cls'] = cls
+    box_coords['cls_conf'] = cls_conf
+
+    return box_coords
+
+
+def classify(resnet_path, img):
+    """Classify img with object classification model.
+
+    :param model: object classification model
+    :param img: opencv2 image object in RGB
+    :returns: tensor of class predictions
+    """
+
+    # Transform image for ResNet
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                             std=[0.229, 0.224, 0.225]),
+        transforms.Resize((224, 224))
+    ])
+
+    img = transform(img.copy())
+    batch = img.unsqueeze(0)
+
+    # Do inference
+    session = onnxruntime.InferenceSession(resnet_path)
+    outname = [i.name for i in session.get_outputs()]
+    inname = [i.name for i in session.get_inputs()]
+    inp = {inname[0]: batch.numpy()}
+    out = torch.tensor(np.array(session.run(outname, inp)))[0]
+
+    # Apply softmax to get percentage confidence of classes
+    out = torch.nn.functional.softmax(out, dim=1)[0] * 100
+    return out
+
+
+def apply_nms(predictions,
+              confidence_threshold: float = 0.3,
+              nms_threshold: float = 0.65):
+    """Apply Non Maximum Suppression to a list of bboxes.
+
+    :param predictions List[Tensor[N, 7]]: predicted bboxes
+    :param confidence_threshold float: discard all bboxes with lower
+        confidence
+    :param nms_threshold float: discard all overlapping bboxes with
+        higher IoU
+    :returns List[Tensor[N, 7]]: filtered bboxes
+    """
+    preds_nms = []
+    for pred in predictions:
+        pred = pred[pred[:, 6] > confidence_threshold]
+
+        nms_idx = ops.batched_nms(
+            boxes=pred[:, 1:5],
+            scores=pred[:, 6],
+            idxs=pred[:, 5],
+            iou_threshold=nms_threshold,
+        )
+        preds_nms.append(pred[nms_idx])
+
+    return preds_nms
+
+
+def get_boxes(yolo_path, image):
+    """Run object detection model on an image and get the bounding box
+    coordinates of all matches.
+
+    :param model: path to onnx object detection model (YOLO)
+    :param img: opencv2 image object
+    :returns: pandas dataframe of matches
+    """
+    # Convert from BGR to RGB
+    img = image[..., ::-1].copy()
+
+    resized_hw = (640, 640)
+    original_hw = (image.shape[0], image.shape[1])
+
+    transform = [
+        A.LongestMaxSize(max(resized_hw)),
+        A.PadIfNeeded(
+            resized_hw[0],
+            resized_hw[1],
+            border_mode=0,
+            value=(114, 114, 114),
+        ),
+        A.ToFloat(max_value=255),
+        ToTensorV2(transpose_mask=True),
+    ]
+
+    # Pad (letterbox) and transform image to correct dims
+    transform = A.Compose(transform)
+    img = transform(image=img)
+
+    # Add batch dimension
+    img['image'] = img['image'].unsqueeze(0)
+
+    # Do inference
+    session = onnxruntime.InferenceSession(yolo_path)
+    outname = [i.name for i in session.get_outputs()]
+    inname = [i.name for i in session.get_inputs()]
+    inp = {inname[0]: img['image'].numpy()}
+    out = torch.tensor(np.array(session.run(outname, inp)))[0]
+
+    # Apply NMS to results
+    preds_nms = apply_nms([out])[0]
+
+    # Convert boxes from resized img to original img
+    xyxy_boxes = preds_nms[:, [1, 2, 3, 4]]  # xmin, ymin, xmax, ymax
+    bboxes = scale_bboxes(xyxy_boxes, resized_hw, original_hw).int().numpy()
+
+    # Construct DataFrame with bboxes and their confidence
+    box_coords = pd.DataFrame(np.c_[bboxes, preds_nms[:, 6]])
+    box_coords.columns = ['xmin', 'ymin', 'xmax', 'ymax', 'box_conf']
+
+    return box_coords
diff --git a/evaluation/evaluation-end2end.ipynb b/evaluation/evaluation-end2end.ipynb
new file mode 100644
index 0000000..27cadeb
--- /dev/null
+++ b/evaluation/evaluation-end2end.ipynb
@@ -0,0 +1,469 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3fe8177c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fiftyone as fo\n",
+    "from PIL import Image\n",
+    "from detection import detect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "32f0f8ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "name = \"dataset-small\"\n",
+    "dataset_dir = \"/home/zenon/Documents/master-thesis/evaluation/dataset-small\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6343aa55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 100% |█████████████████| 401/401 [633.3ms elapsed, 0s remaining, 633.2 samples/s]      \n"
+     ]
+    }
+   ],
+   "source": [
+    "# The splits to load\n",
+    "splits = [\"val\"]\n",
+    "\n",
+    "# Load the dataset, using tags to mark the samples in each split\n",
+    "dataset = fo.Dataset(name)\n",
+    "for split in splits:\n",
+    "    dataset.add_dir(\n",
+    "        dataset_dir=dataset_dir,\n",
+    "        dataset_type=fo.types.YOLOv5Dataset,\n",
+    "        split=split,\n",
+    "        tags=split,\n",
+    "    )\n",
+    "\n",
+    "classes = dataset.default_classes\n",
+    "predictions_view = dataset.view()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "29827e3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 100% |█████████████████| 401/401 [5.4m elapsed, 0s remaining, 1.4 samples/s]      \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Do detections with model and save bounding boxes\n",
+    "with fo.ProgressBar() as pb:\n",
+    "    for sample in pb(predictions_view):\n",
+    "        image = Image.open(sample.filepath)\n",
+    "        w, h = image.size\n",
+    "        pred = detect(sample.filepath, 'yolo.onnx', 'resnet.onnx')\n",
+    "\n",
+    "        detections = []\n",
+    "        for _, row in pred.iterrows():\n",
+    "            xmin, xmax = int(row['xmin']), int(row['xmax'])\n",
+    "            ymin, ymax = int(row['ymin']), int(row['ymax'])\n",
+    "            rel_box = [\n",
+    "                xmin / w, ymin / h, (xmax - xmin) / w, (ymax - ymin) / h\n",
+    "            ]\n",
+    "            detections.append(\n",
+    "                fo.Detection(label=classes[int(row['cls'])],\n",
+    "                             bounding_box=rel_box,\n",
+    "                             confidence=int(row['cls_conf'])))\n",
+    "\n",
+    "        sample[\"yolo_resnet\"] = fo.Detections(detections=detections)\n",
+    "        sample.save()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ad67806",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluating detections...\n",
+      " 100% |█████████████████| 401/401 [1.2s elapsed, 0s remaining, 339.9 samples/s]         \n",
+      "Performing IoU sweep...\n",
+      " 100% |█████████████████| 401/401 [1.4s elapsed, 0s remaining, 288.5 samples/s]         \n"
+     ]
+    }
+   ],
+   "source": [
+    "results = predictions_view.evaluate_detections(\n",
+    "    \"yolo_resnet\",\n",
+    "    gt_field=\"ground_truth\",\n",
+    "    eval_key=\"eval\",\n",
+    "    compute_mAP=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b180420b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     Healthy       0.80      0.81      0.81       430\n",
+      "    Stressed       0.77      0.72      0.75       315\n",
+      "\n",
+      "   micro avg       0.79      0.77      0.78       745\n",
+      "   macro avg       0.79      0.77      0.78       745\n",
+      "weighted avg       0.79      0.77      0.78       745\n",
+      "\n",
+      "0.6336217415940075\n"
+     ]
+    },
+    {
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+       "               'xaxis': {'constrain': 'domain',\n",
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+       "               'yaxis': {'constrain': 'domain',\n",
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+       "})"
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+       "                          0.        , 0.        , 0.        , 0.        , 0.        ])},\n",
+       "             {'customdata': array([99. , 99. , 99. , 99. , 99. , 99. , 99. , 99. , 99. , 99. , 98.7, 98. ,\n",
+       "                                   98. , 98. , 97.9, 97.7, 97. , 96.9, 96.7, 96.5, 95.9, 95.6, 94.9, 94.5,\n",
+       "                                   94. , 93. , 91.6, 91. , 90.1, 89.3, 89. , 88.2, 87.6, 87.2, 86.5, 86.1,\n",
+       "                                   85.4, 84.8, 84.2, 83.1, 82.4, 81.7, 80.6, 79.5, 78.9, 77.4, 76.5, 74.7,\n",
+       "                                   74. , 73.3, 72.3, 71.7, 71. , 70.2, 69.3, 68.6, 67.8, 67.4, 66.2, 65.3,\n",
+       "                                   64.3, 62.7, 61.6, 60.5, 54.3, 53.1, 51.9, 45.1, 38.9, 38.2, 32. , 20.8,\n",
+       "                                   10.1,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,\n",
+       "                                    0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,\n",
+       "                                    0. ,  0. ,  0. ,  0. ,  0. ]),\n",
+       "              'hovertemplate': ('<b>class: %{text}</b><br>recal' ... 'customdata:.3f}<extra></extra>'),\n",
+       "              'line': {'color': '#DC3912'},\n",
+       "              'mode': 'lines',\n",
+       "              'name': 'Stressed (AP = 0.593)',\n",
+       "              'text': array(['Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed',\n",
+       "                             'Stressed', 'Stressed', 'Stressed', 'Stressed', 'Stressed'], dtype='<U8'),\n",
+       "              'type': 'scatter',\n",
+       "              'uid': '01082c55-ea56-426c-8cfb-a433926eed03',\n",
+       "              'x': array([0.  , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 , 0.11,\n",
+       "                          0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21, 0.22, 0.23,\n",
+       "                          0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32, 0.33, 0.34, 0.35,\n",
+       "                          0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47,\n",
+       "                          0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59,\n",
+       "                          0.6 , 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 , 0.71,\n",
+       "                          0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 , 0.81, 0.82, 0.83,\n",
+       "                          0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95,\n",
+       "                          0.96, 0.97, 0.98, 0.99, 1.  ]),\n",
+       "              'y': array([1.        , 1.        , 1.        , 1.        , 1.        , 1.        ,\n",
+       "                          1.        , 1.        , 1.        , 1.        , 0.97379592, 0.94476123,\n",
+       "                          0.94476123, 0.94476123, 0.93427565, 0.91445476, 0.85844913, 0.85844913,\n",
+       "                          0.85790639, 0.85788924, 0.85788924, 0.85747126, 0.85663314, 0.85452586,\n",
+       "                          0.85298487, 0.84306319, 0.8375    , 0.8375    , 0.8375    , 0.8375    ,\n",
+       "                          0.8375    , 0.8375    , 0.8375    , 0.8375    , 0.8375    , 0.8375    ,\n",
+       "                          0.83717347, 0.83658868, 0.83565817, 0.8336283 , 0.82847628, 0.82488659,\n",
+       "                          0.82126275, 0.8175916 , 0.81473127, 0.80427951, 0.8016311 , 0.80115198,\n",
+       "                          0.8008129 , 0.80053685, 0.79883857, 0.79853619, 0.79853619, 0.79782171,\n",
+       "                          0.79757033, 0.79595576, 0.79417872, 0.79338307, 0.79116989, 0.78813658,\n",
+       "                          0.78474129, 0.78273994, 0.77996131, 0.77679573, 0.70534431, 0.70114991,\n",
+       "                          0.69945778, 0.62345091, 0.54988677, 0.54689953, 0.46774707, 0.31001791,\n",
+       "                          0.1547619 , 0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "                          0.        , 0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "                          0.        , 0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "                          0.        , 0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "                          0.        , 0.        , 0.        , 0.        , 0.        ])}],\n",
+       "    'layout': {'margin': {'b': 0, 'l': 0, 'r': 0, 't': 30},\n",
+       "               'shapes': [{'line': {'dash': 'dash'}, 'type': 'line', 'x0': 0, 'x1': 1, 'y0': 1, 'y1': 0}],\n",
+       "               'template': '...',\n",
+       "               'xaxis': {'constrain': 'domain', 'range': [0, 1], 'title': {'text': 'Recall'}},\n",
+       "               'yaxis': {'constrain': 'domain',\n",
+       "                         'range': [0, 1],\n",
+       "                         'scaleanchor': 'x',\n",
+       "                         'scaleratio': 1,\n",
+       "                         'title': {'text': 'Precision'}}}\n",
+       "})"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the 10 most common classes in the dataset\n",
+    "counts = dataset.count_values(\"ground_truth.detections.label\")\n",
+    "classes_top10 = sorted(counts, key=counts.get, reverse=True)\n",
+    "\n",
+    "# Print a classification report for the top-10 classes\n",
+    "results.print_report(classes=classes_top10)\n",
+    "\n",
+    "print(results.mAP())\n",
+    "\n",
+    "# Plot confusion matrix\n",
+    "matrix = results.plot_confusion_matrix(classes=classes)\n",
+    "matrix.show()\n",
+    "\n",
+    "pr_curves = results.plot_pr_curves(classes=[\"Healthy\", \"Stressed\"])\n",
+    "pr_curves.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d1137788",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Session launched. Run `session.show()` to open the App in a cell output.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "window.open('http://localhost:5151/');"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "session = fo.launch_app(dataset, auto=False)\n",
+    "session.view = predictions_view\n",
+    "session.open_tab()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "535003f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "session.plots.attach(matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3ba32f0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/evaluation/evaluation.py b/evaluation/evaluation.py
new file mode 100644
index 0000000..dc15c6e
--- /dev/null
+++ b/evaluation/evaluation.py
@@ -0,0 +1,64 @@
+import fiftyone as fo
+from PIL import Image
+from evaluate import detect
+
+name = "dataset-small"
+dataset_dir = "/home/zenon/Documents/master-thesis/evaluation/dataset-small"
+
+# The splits to load
+splits = ["val"]
+
+# Load the dataset, using tags to mark the samples in each split
+dataset = fo.Dataset(name)
+for split in splits:
+    dataset.add_dir(
+        dataset_dir=dataset_dir,
+        dataset_type=fo.types.YOLOv5Dataset,
+        split=split,
+        tags=split,
+    )
+
+classes = dataset.default_classes
+predictions_view = dataset.view()
+
+with fo.ProgressBar() as pb:
+    for sample in pb(predictions_view):
+        image = Image.open(sample.filepath)
+        w, h = image.size
+        pred = detect(sample.filepath, 'yolo.onnx', 'resnet.onnx')
+
+        detections = []
+        for _, row in pred.iterrows():
+            xmin, xmax = int(row['xmin']), int(row['xmax'])
+            ymin, ymax = int(row['ymin']), int(row['ymax'])
+            rel_box = [
+                xmin / w, ymin / h, (xmax - xmin) / w, (ymax - ymin) / h
+            ]
+            detections.append(
+                fo.Detection(label=classes[int(row['cls'])],
+                             bounding_box=rel_box,
+                             confidence=int(row['cls_conf'])))
+
+        sample["yolo_resnet"] = fo.Detections(detections=detections)
+        sample.save()
+
+results = predictions_view.evaluate_detections(
+    "yolo_resnet",
+    gt_field="ground_truth",
+    eval_key="eval",
+    compute_mAP=True,
+)
+
+# Get the 10 most common classes in the dataset
+counts = dataset.count_values("ground_truth.detections.label")
+classes_top10 = sorted(counts, key=counts.get, reverse=True)[:10]
+
+# Print a classification report for the top-10 classes
+results.print_report(classes=classes_top10)
+
+plot = results.plot_pr_curves(classes=["Healthy", "Stressed"])
+plot.show()
+
+session = fo.launch_app(dataset)
+session.view = predictions_view
+session.wait()
diff --git a/evaluation/labeling.py b/evaluation/labeling.py
new file mode 100644
index 0000000..26f699c
--- /dev/null
+++ b/evaluation/labeling.py
@@ -0,0 +1,135 @@
+import logging
+import argparse
+import cv2
+import json
+import os
+
+from utils.conversions import convert_to_yolo
+from detection import detect
+
+template = [{
+    "data": {
+        "image": "/data/local-files/?d=evaluation/images/0.jpg"
+    },
+    "predictions": [{
+        "model_version":
+        "one",
+        "score":
+        0.0,
+        "result": [{
+            "id": "result1",
+            "type": "rectanglelabels",
+            "from_name": "label",
+            "to_name": "image",
+            "original_width": 474,
+            "original_height": 266,
+            "image_rotation": 0,
+            "confidence": 0,
+            "value": {
+                "rotation": 0,
+                "x": 19.62,
+                "y": 15.04,
+                "width": 55.06,
+                "height": 78.2,
+                "rectanglelabels": ["Stressed"]
+            }
+        }]
+    }]
+}]
+
+
+def create_json_annotation(image_path, yolo_path, resnet_path):
+    """Create a JSON representation of identified bounding boxes.
+
+    :param image_path str: path to image
+    :param yolo_path str: path to YOLO model in ONNX format
+    :param resnet_path str: path to ResNet model in ONNX format
+    :returns Dict: bounding boxes in labelstudio JSON format
+    """
+    template[0]['data'][
+        'image'] = "/data/local-files/?d=evaluation/" + image_path
+    img = cv2.imread(image_path)
+    (height, width) = img.shape[0], img.shape[1]
+    bboxes = detect(image_path, yolo_path, resnet_path)
+    result = template[0]['predictions'][0]['result']
+
+    results = []
+    for idx, row in bboxes.iterrows():
+        modified = convert_to_yolo(row, width, height)
+        json_result = {}
+        json_result['id'] = 'result' + str(idx + 1)
+        json_result['type'] = 'rectanglelabels'
+        json_result['from_name'] = 'label'
+        json_result['to_name'] = 'image'
+        json_result['original_width'] = width
+        json_result['original_height'] = height
+        json_result['image_rotation'] = 0
+        json_result['value'] = {}
+        json_result['value']['rotation'] = 0
+        json_result['value']['x'] = modified['xmin%']
+        json_result['value']['y'] = modified['ymin%']
+        json_result['value']['width'] = modified['width%']
+        json_result['value']['height'] = modified['height%']
+        if modified['cls'] == 0:
+            json_result['value']['rectanglelabels'] = ['Healthy']
+        else:
+            json_result['value']['rectanglelabels'] = ['Stressed']
+        results.append(json_result)
+
+    template[0]['predictions'][0]['result'] = results
+    return template
+
+
+def write_labels_to_disk(image_dir, output_dir, yolo_path, resnet_path):
+    """Read images from disk, classify them and output bounding boxes
+    in labelstudio JSON format.
+
+    :param image_dir str: directory containing images to label
+    :param output_dir str: directory to save JSON files to
+    :param yolo_path str: path to YOLO model in ONNX format
+    :param resnet_path str: path to ResNet model in ONNX format
+    :returns: None
+    """
+    image_dir = os.path.join(image_dir, '')
+    for file in os.listdir(image_dir):
+        filename = os.fsdecode(file)
+        filename_wo_ext = os.path.splitext(filename)[0]
+        rel_output_path = os.path.join(output_dir, filename_wo_ext + '.json')
+        json_data = create_json_annotation(image_dir + filename, yolo_path,
+                                           resnet_path)
+        os.makedirs(os.path.dirname(os.path.join(output_dir, filename)),
+                    exist_ok=True)
+        logging.info('Writing json file for %s', filename)
+        with open(rel_output_path, 'w') as f:
+            json.dump(json_data, f)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--source',
+                        type=str,
+                        help='source folder with images',
+                        required=True)
+    parser.add_argument('--output',
+                        type=str,
+                        help='output folder for json files',
+                        required=True)
+    parser.add_argument('--yolo',
+                       type=str,
+                       help='path to YOLO model in ONNX format',
+                       required=True)
+    parser.add_argument('--resnet',
+                       type=str,
+                       help='path to ResNet model in ONNX format',
+                       required=True)
+    parser.add_argument(
+        '--log',
+        type=str,
+        help='log level (debug, info, warning, error, critical)',
+        default='warning')
+    opt = parser.parse_args()
+    numeric_level = getattr(logging, opt.log.upper(), None)
+    logging.basicConfig(format='%(levelname)s::%(asctime)s::%(message)s',
+                        datefmt='%Y-%m-%dT%H:%M:%S',
+                        level=numeric_level)
+    write_labels_to_disk(opt.source, opt.output, opt.yolo, opt.resnet)
diff --git a/evaluation/utils/conversions.py b/evaluation/utils/conversions.py
new file mode 100644
index 0000000..02039e2
--- /dev/null
+++ b/evaluation/utils/conversions.py
@@ -0,0 +1,42 @@
+def convert_to_yolo(bbox, width, height):
+    modified = bbox.copy()
+    modified['xmin%'] = round(bbox['xmin'] / width * 100, 2)
+    modified['ymin%'] = round(bbox['ymin'] / height * 100, 2)
+    modified['width%'] = round((bbox['xmax'] - bbox['xmin']) / width * 100, 2)
+    modified['height%'] = round((bbox['ymax'] - bbox['ymin']) / height * 100,
+                                2)
+    return modified
+
+
+def scale_bboxes(bboxes, resized_hw, original_hw):
+    """Scale bounding boxes from a padded and resized image to fit on
+    original image.
+
+    :param xyxy_boxes Tensor[N, 4]: tensor of xmin, ymin, xmax, ymax
+        per bounding box
+    :param resized_hw Tuple: height and width of the resized image
+    :param original_hw Tuple: height and width of the original image
+    :returns Tensor[N, 4]: tensor of xmin, ymin, xmax, ymax per
+        bounding box
+    """
+    scaled_boxes = bboxes.clone()
+    scale_ratio = resized_hw[0] / original_hw[0], resized_hw[1] / original_hw[1]
+
+    # Remove padding
+    pad_scale = min(scale_ratio)
+    padding = (resized_hw[1] - original_hw[1] * pad_scale) / 2, (
+        resized_hw[0] - original_hw[0] * pad_scale) / 2
+    scaled_boxes[:, [0, 2]] -= padding[0]  # x padding
+    scaled_boxes[:, [1, 3]] -= padding[1]  # y padding
+    scale_ratio = (pad_scale, pad_scale)
+
+    scaled_boxes[:, [0, 2]] /= scale_ratio[1]
+    scaled_boxes[:, [1, 3]] /= scale_ratio[0]
+
+    # Clip bounding xyxy bounding boxes to image shape (height, width)
+    scaled_boxes[:, 0].clamp_(0, original_hw[1])  # xmin
+    scaled_boxes[:, 1].clamp_(0, original_hw[0])  # ymin
+    scaled_boxes[:, 2].clamp_(0, original_hw[1])  # xmax
+    scaled_boxes[:, 3].clamp_(0, original_hw[0])  # ymax
+
+    return scaled_boxes
diff --git a/evaluation/utils/manipulations.py b/evaluation/utils/manipulations.py
new file mode 100644
index 0000000..78308e9
--- /dev/null
+++ b/evaluation/utils/manipulations.py
@@ -0,0 +1,31 @@
+import cv2
+
+
+def draw_boxes(image, bboxes):
+    img = image.copy()
+    for idx, bbox in enumerate(bboxes):
+        xmin, ymin, xmax, ymax = bbox
+        # Draw bounding box and number on original image
+        img = cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
+        img = cv2.putText(img, str(idx), (xmin + 5, ymin + 25),
+                          cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 0), 4,
+                          cv2.LINE_AA)
+        img = cv2.putText(img, str(idx), (xmin + 5, ymin + 25),
+                          cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255), 2,
+                          cv2.LINE_AA)
+    return img
+
+
+def get_cutout(img, xmin, xmax, ymin, ymax):
+    """Cut out a bounding box from an image and transform it for
+    object classification model.
+
+    :param img: opencv2 image object in BGR
+    :param int xmin: start of bounding box on x axis
+    :param int xmax: end of bounding box on x axis
+    :param int ymin: start of bounding box on y axis
+    :param int ymax: end of bounding box on y axis
+    :returns: tensor of cropped image in BGR
+    """
+    cropped_image = img[ymin:ymax, xmin:xmax]
+    return cropped_image