diff --git a/yolo-second-run/model.py b/yolo-second-run/model.py
new file mode 100644
index 0000000..3ed74a5
--- /dev/null
+++ b/yolo-second-run/model.py
@@ -0,0 +1,125 @@
+import argparse
+import cv2
+import torch
+from torchvision import transforms
+
+
+def load_models():
+    """Load the models for two-stage classification.
+
+    :returns: tuple of models
+
+    """
+    first_stage = torch.hub.load("WongKinYiu/yolov7",
+                                 "custom",
+                                 "runs/train/yolov7-custom7/weights/best.pt",
+                                 trust_repo=True)
+    second_stage = torch.load('resnet.pt')
+    return (first_stage, second_stage)
+
+
+def detect(img_path: str):
+    """Load an image, detect individual plants and label them as
+    healthy or wilted.
+
+    :param str img_path: path to image
+    :returns: tensor of confidence values per class
+
+    """
+    img = cv2.imread(img_path)
+    original = img.copy()
+    (first_stage, second_stage) = load_models()
+    box_coords = get_boxes(first_stage, img)
+    box_coords.sort_values(by=['xmin'], ignore_index=True, inplace=True)
+    print(box_coords)
+
+    for idx, 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
+        pred = classify(second_stage, cropped_image[..., ::-1])
+        print(pred)
+
+        # cv2.imshow('cropped ' + str(idx), cropped_image)
+        # cv2.waitKey(0)
+
+        # Draw bounding box and class on original image
+        original = cv2.rectangle(original, (xmin, ymin), (xmax, ymax),
+                                 (0, 255, 0), 2)
+        original = cv2.putText(original, str(idx), (xmin + 5, ymin + 25),
+                               cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 0), 4,
+                               cv2.LINE_AA)
+        original = cv2.putText(original, str(idx), (xmin + 5, ymin + 25),
+                               cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255),
+                               2, cv2.LINE_AA)
+
+        # cv2.waitKey(0)
+        # cv2.destroyAllWindows()
+
+    cv2.imshow('original with bounding box', original)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+def get_boxes(model, img):
+    """Run object detection model on an image and get the bounding box
+    coordinates of all matches.
+
+    :param model: object detection model (YOLO)
+    :param img: opencv2 image object
+    :returns: pandas dataframe of matches
+
+    """
+    box_coords = model(img[..., ::-1], size=640)
+    return box_coords.pandas().xyxy[0]
+
+
+def classify(model, 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
+    data_transforms = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    img = data_transforms(img.copy())
+    out = model(img.unsqueeze(0))
+    # Apply softmax to get percentage confidence of classes
+    out = torch.nn.functional.softmax(out, dim=1)[0] * 100
+    return out
+
+
+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 xmin: start of bounding box on x axis
+    :param xmax: end of bounding box on x axis
+    :param ymin: start of bounding box on y axis
+    :param 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
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--source', type=str, help='image file or webcam')
+    opt = parser.parse_args()
+
+    if opt.source:
+        with torch.no_grad():
+            detect(opt.source)