diff --git a/kangoroo.py b/kangoroo.py
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+++ b/kangoroo.py
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+# fit a mask rcnn on the kangaroo dataset
+from os import listdir
+from xml.etree import ElementTree
+from numpy import zeros
+from numpy import asarray
+from mrcnn.utils import Dataset
+from mrcnn.config import Config
+from mrcnn.model import MaskRCNN
+import tensorflow as tf
+import os
+
+# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+# class that defines and loads the kangaroo dataset
+class KangarooDataset(Dataset):
+	# load the dataset definitions
+	def load_dataset(self, dataset_dir, is_train=True):
+		# define one class
+		self.add_class("dataset", 1, "kangaroo")
+		# define data locations
+		images_dir = dataset_dir + '/images/'
+		annotations_dir = dataset_dir + '/annots/'
+		# find all images
+		for filename in listdir(images_dir):
+			# extract image id
+			image_id = filename[:-4]
+			# skip bad images
+			if image_id in ['00090']:
+				continue
+			# skip all images after 150 if we are building the train set
+			if is_train and int(image_id) >= 150:
+				continue
+			# skip all images before 150 if we are building the test/val set
+			if not is_train and int(image_id) < 150:
+				continue
+			img_path = images_dir + filename
+			ann_path = annotations_dir + image_id + '.xml'
+			# add to dataset
+			self.add_image('dataset', image_id=image_id, path=img_path, annotation=ann_path)
+
+	# extract bounding boxes from an annotation file
+	def extract_boxes(self, filename):
+		# load and parse the file
+		tree = ElementTree.parse(filename)
+		# get the root of the document
+		root = tree.getroot()
+		# extract each bounding box
+		boxes = list()
+		for box in root.findall('.//bndbox'):
+			xmin = int(box.find('xmin').text)
+			ymin = int(box.find('ymin').text)
+			xmax = int(box.find('xmax').text)
+			ymax = int(box.find('ymax').text)
+			coors = [xmin, ymin, xmax, ymax]
+			boxes.append(coors)
+		# extract image dimensions
+		width = int(root.find('.//size/width').text)
+		height = int(root.find('.//size/height').text)
+		return boxes, width, height
+
+	# load the masks for an image
+	def load_mask(self, image_id):
+		# get details of image
+		info = self.image_info[image_id]
+		# define box file location
+		path = info['annotation']
+		# load XML
+		boxes, w, h = self.extract_boxes(path)
+		# create one array for all masks, each on a different channel
+		masks = zeros([h, w, len(boxes)], dtype='uint8')
+		# create masks
+		class_ids = list()
+		for i in range(len(boxes)):
+			box = boxes[i]
+			row_s, row_e = box[1], box[3]
+			col_s, col_e = box[0], box[2]
+			masks[row_s:row_e, col_s:col_e, i] = 1
+			class_ids.append(self.class_names.index('kangaroo'))
+		return masks, asarray(class_ids, dtype='int32')
+
+	# load an image reference
+	def image_reference(self, image_id):
+		info = self.image_info[image_id]
+		return info['path']
+
+# define a configuration for the model
+class KangarooConfig(Config):
+	# define the name of the configuration
+	NAME = "kangaroo_cfg"
+	# number of classes (background + kangaroo)
+	NUM_CLASSES = 1 + 1
+	# number of training steps per epoch
+	STEPS_PER_EPOCH = 131
+	IMAGES_PER_GPU = 1
+	# BACKBONE = "resnet50"
+
+# prepare train set
+train_set = KangarooDataset()
+train_set.load_dataset('kangaroo', is_train=True)
+train_set.prepare()
+print('Train: %d' % len(train_set.image_ids))
+# prepare test/val set
+test_set = KangarooDataset()
+test_set.load_dataset('kangaroo', is_train=False)
+test_set.prepare()
+print('Test: %d' % len(test_set.image_ids))
+# prepare config
+config = KangarooConfig()
+config.display()
+# define the model
+model = MaskRCNN(mode='training', model_dir='./', config=config)
+# load weights (mscoco) and exclude the output layers
+model.load_weights('mask_rcnn_coco.h5', by_name=True, exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",  "mrcnn_bbox", "mrcnn_mask"])
+# train weights (output layers or 'heads')
+model.train(train_set, test_set, learning_rate=config.LEARNING_RATE, epochs=5, layers='heads')
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