diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..80b5816
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,145 @@
+
+# Created by https://www.toptal.com/developers/gitignore/api/python
+# Edit at https://www.toptal.com/developers/gitignore?templates=python
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# End of https://www.toptal.com/developers/gitignore/api/python
\ No newline at end of file
diff --git a/cityscapes_gt_converter/cityscapes_panoptic_converter.py b/cityscapes_gt_converter/cityscapes_panoptic_converter.py
index 2bb6442..ad739cd 100644
--- a/cityscapes_gt_converter/cityscapes_panoptic_converter.py
+++ b/cityscapes_gt_converter/cityscapes_panoptic_converter.py
@@ -3,8 +3,7 @@
 from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
-import os, sys
-import json
+import os
 import glob
 import numpy as np
 import PIL.Image as Image
@@ -14,9 +13,10 @@
 try:
     # set up path for cityscapes scripts
     # sys.path.append('./cityscapesScripts/')
-    from cityscapesscripts.helpers.labels import labels, id2label
+    from cityscapesscripts.helpers.labels import labels
 except Exception:
-    raise Exception("Please load Cityscapes scripts from https://github.com/mcordts/cityscapesScripts")
+    raise Exception(
+        "Please load Cityscapes scripts from https://github.com/mcordts/cityscapesScripts")
 
 original_format_folder = './gtFine/val/'
 # folder to store panoptic PNGs
@@ -24,6 +24,7 @@
 # json with segmentations information
 out_file = './cityscapes_data/cityscapes_panoptic_val.json'
 
+
 def panoptic_converter(original_format_folder, out_folder, out_file):
 
     if not os.path.isdir(out_folder):
@@ -42,7 +43,8 @@ def panoptic_converter(original_format_folder, out_folder, out_file):
 
     categories_dict = {cat['id']: cat for cat in categories}
 
-    file_list = sorted(glob.glob(os.path.join(original_format_folder, '*/*_gtFine_instanceIds.png')))
+    file_list = sorted(glob.glob(os.path.join(
+        original_format_folder, '*/*_gtFine_instanceIds.png')))
 
     images = []
     annotations = []
@@ -54,17 +56,17 @@ def panoptic_converter(original_format_folder, out_folder, out_file):
 
         file_name = f.split('/')[-1]
         image_id = file_name.rsplit('_', 2)[0]
-        image_filename= '{}_leftImg8bit.png'.format(image_id)
+        image_filename = '{}_leftImg8bit.png'.format(image_id)
         # image entry, id for image is its filename without extension
         images.append({"id": image_id,
                        "width": original_format.shape[1],
                        "height": original_format.shape[0],
                        "file_name": image_filename})
 
-        pan_format = np.zeros((original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8)
+        pan_format = np.zeros(
+            (original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8)
         id_generator = IdGenerator(categories_dict)
 
-        idx = 0
         l = np.unique(original_format)
         segm_info = []
         for el in l:
@@ -82,7 +84,7 @@ def panoptic_converter(original_format_folder, out_folder, out_file):
             segment_id, color = id_generator.get_id_and_color(semantic_id)
             pan_format[mask] = color
 
-            area = np.sum(mask) # segment area computation
+            area = np.sum(mask)  # segment area computation
 
             # bbox computation for a segment
             hor = np.sum(mask, axis=0)
@@ -110,9 +112,10 @@ def panoptic_converter(original_format_folder, out_folder, out_file):
     d = {'images': images,
          'annotations': annotations,
          'categories': categories,
-        }
+         }
 
     save_json(d, out_file)
 
+
 if __name__ == "__main__":
     panoptic_converter(original_format_folder, out_folder, out_file)
diff --git a/converters/2channels2panoptic_coco_format.py b/converters/2channels2panoptic_coco_format.py
index f559de0..bc701c6 100644
--- a/converters/2channels2panoptic_coco_format.py
+++ b/converters/2channels2panoptic_coco_format.py
@@ -20,13 +20,12 @@
 from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
-import os, sys
+import os
 import argparse
 import numpy as np
 import json
 import time
 import multiprocessing
-import itertools
 
 import PIL.Image as Image
 
@@ -34,21 +33,26 @@
 
 OFFSET = 1000
 
+
 @get_traceback
 def convert_single_core(proc_id, image_set, categories, source_folder, segmentations_folder, VOID=0):
     annotations = []
     for working_idx, image_info in enumerate(image_set):
         if working_idx % 100 == 0:
-            print('Core: {}, {} from {} images converted'.format(proc_id, working_idx, len(image_set)))
+            print('Core: {}, {} from {} images converted'.format(
+                proc_id, working_idx, len(image_set)))
 
         file_name = '{}.png'.format(image_info['file_name'].rsplit('.')[0])
         try:
-            original_format = np.array(Image.open(os.path.join(source_folder, file_name)), dtype=np.uint32)
+            original_format = np.array(Image.open(
+                os.path.join(source_folder, file_name)), dtype=np.uint32)
         except IOError:
-            raise KeyError('no prediction png file for id: {}'.format(image_info['id']))
+            raise KeyError(
+                'no prediction png file for id: {}'.format(image_info['id']))
 
         pan = OFFSET * original_format[:, :, 0] + original_format[:, :, 1]
-        pan_format = np.zeros((original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8)
+        pan_format = np.zeros(
+            (original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8)
 
         id_generator = IdGenerator(categories)
 
@@ -70,7 +74,8 @@ def convert_single_core(proc_id, image_set, categories, source_folder, segmentat
                             'file_name': file_name,
                             "segments_info": segm_info})
 
-        Image.fromarray(pan_format).save(os.path.join(segmentations_folder, file_name))
+        Image.fromarray(pan_format).save(
+            os.path.join(segmentations_folder, file_name))
     print('Core: {}, all {} images processed'.format(proc_id, len(image_set)))
     return annotations
 
@@ -93,7 +98,8 @@ def converter(source_folder, images_json_file, categories_json_file,
     if segmentations_folder is None:
         segmentations_folder = predictions_json_file.rsplit('.', 1)[0]
     if not os.path.isdir(segmentations_folder):
-        print("Creating folder {} for panoptic segmentation PNGs".format(segmentations_folder))
+        print("Creating folder {} for panoptic segmentation PNGs".format(
+            segmentations_folder))
         os.mkdir(segmentations_folder)
 
     print("CONVERTING...")
@@ -106,7 +112,8 @@ def converter(source_folder, images_json_file, categories_json_file,
     print('\n')
     cpu_num = multiprocessing.cpu_count()
     images_split = np.array_split(images, cpu_num)
-    print("Number of cores: {}, images per core: {}".format(cpu_num, len(images_split[0])))
+    print("Number of cores: {}, images per core: {}".format(
+        cpu_num, len(images_split[0])))
     workers = multiprocessing.Pool(processes=cpu_num)
     processes = []
     for proc_id, image_set in enumerate(images_split):
diff --git a/converters/detection2panoptic_coco_format.py b/converters/detection2panoptic_coco_format.py
index 5bd4bbc..55d2eea 100644
--- a/converters/detection2panoptic_coco_format.py
+++ b/converters/detection2panoptic_coco_format.py
@@ -8,7 +8,7 @@
 from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
-import os, sys
+import os
 import argparse
 import numpy as np
 import json
@@ -22,10 +22,11 @@
 try:
     # set up path for pycocotools
     # sys.path.append('./cocoapi-master/PythonAPI/')
-    from pycocotools import mask as COCOmask
     from pycocotools.coco import COCO as COCO
 except Exception:
-    raise Exception("Please install pycocotools module from https://github.com/cocodataset/cocoapi")
+    raise Exception(
+        "Please install pycocotools module from https://github.com/cocodataset/cocoapi")
+
 
 @get_traceback
 def convert_detection_to_panoptic_coco_format_single_core(
@@ -55,8 +56,9 @@ def convert_detection_to_panoptic_coco_format_single_core(
             if ann['category_id'] not in categories:
                 raise Exception('Panoptic coco categories file does not contain \
                     category with id: {}'.format(ann['category_id'])
-                )
-            segment_id, color = id_generator.get_id_and_color(ann['category_id'])
+                                )
+            segment_id, color = id_generator.get_id_and_color(
+                ann['category_id'])
             mask = coco_detection.annToMask(ann)
             overlaps_map += mask
             pan_format[mask == 1] = color
@@ -66,11 +68,13 @@ def convert_detection_to_panoptic_coco_format_single_core(
             segments_info.append(ann)
 
         if np.sum(overlaps_map > 1) != 0:
-            raise Exception("Segments for image {} overlap each other.".format(img_id))
+            raise Exception(
+                "Segments for image {} overlap each other.".format(img_id))
         panoptic_record['segments_info'] = segments_info
         annotations_panoptic.append(panoptic_record)
 
-        Image.fromarray(pan_format).save(os.path.join(segmentations_folder, file_name))
+        Image.fromarray(pan_format).save(
+            os.path.join(segmentations_folder, file_name))
 
     print('Core: {}, all {} images processed'.format(proc_id, len(img_ids)))
     return annotations_panoptic
@@ -85,7 +89,8 @@ def convert_detection_to_panoptic_coco_format(input_json_file,
     if segmentations_folder is None:
         segmentations_folder = output_json_file.rsplit('.', 1)[0]
     if not os.path.isdir(segmentations_folder):
-        print("Creating folder {} for panoptic segmentation PNGs".format(segmentations_folder))
+        print("Creating folder {} for panoptic segmentation PNGs".format(
+            segmentations_folder))
         os.mkdir(segmentations_folder)
 
     print("CONVERTING...")
@@ -106,7 +111,8 @@ def convert_detection_to_panoptic_coco_format(input_json_file,
 
     cpu_num = multiprocessing.cpu_count()
     img_ids_split = np.array_split(img_ids, cpu_num)
-    print("Number of cores: {}, images per core: {}".format(cpu_num, len(img_ids_split[0])))
+    print("Number of cores: {}, images per core: {}".format(
+        cpu_num, len(img_ids_split[0])))
     workers = multiprocessing.Pool(processes=cpu_num)
     processes = []
     for proc_id, img_ids in enumerate(img_ids_split):
diff --git a/converters/panoptic2detection_coco_format.py b/converters/panoptic2detection_coco_format.py
index fdd9390..2d38c61 100644
--- a/converters/panoptic2detection_coco_format.py
+++ b/converters/panoptic2detection_coco_format.py
@@ -12,7 +12,7 @@
 from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
-import os, sys
+import os
 import argparse
 import numpy as np
 import json
@@ -28,7 +28,9 @@
     # sys.path.append('./cocoapi-master/PythonAPI/')
     from pycocotools import mask as COCOmask
 except Exception:
-    raise Exception("Please install pycocotools module from https://github.com/cocodataset/cocoapi")
+    raise Exception(
+        "Please install pycocotools module from https://github.com/cocodataset/cocoapi")
+
 
 @get_traceback
 def convert_panoptic_to_detection_coco_format_single_core(
@@ -47,7 +49,8 @@ def convert_panoptic_to_detection_coco_format_single_core(
                 Image.open(os.path.join(segmentations_folder, file_name)), dtype=np.uint32
             )
         except IOError:
-            raise KeyError('no prediction png file for id: {}'.format(annotation['image_id']))
+            raise KeyError('no prediction png file for id: {}'.format(
+                annotation['image_id']))
         pan = rgb2id(pan_format)
 
         for segm_info in annotation['segments_info']:
@@ -62,7 +65,8 @@ def convert_panoptic_to_detection_coco_format_single_core(
             segm_info['segmentation'] = rle
             annotations_detection.append(segm_info)
 
-    print('Core: {}, all {} images processed'.format(proc_id, len(annotations_set)))
+    print('Core: {}, all {} images processed'.format(
+        proc_id, len(annotations_set)))
     return annotations_detection
 
 
@@ -98,7 +102,8 @@ def convert_panoptic_to_detection_coco_format(input_json_file,
 
     cpu_num = multiprocessing.cpu_count()
     annotations_split = np.array_split(annotations_panoptic, cpu_num)
-    print("Number of cores: {}, images per core: {}".format(cpu_num, len(annotations_split[0])))
+    print("Number of cores: {}, images per core: {}".format(
+        cpu_num, len(annotations_split[0])))
     workers = multiprocessing.Pool(processes=cpu_num)
     processes = []
     for proc_id, annotations_set in enumerate(annotations_split):
diff --git a/converters/panoptic2semantic_segmentation.py b/converters/panoptic2semantic_segmentation.py
index a8315c2..2961a06 100755
--- a/converters/panoptic2semantic_segmentation.py
+++ b/converters/panoptic2semantic_segmentation.py
@@ -11,7 +11,7 @@
 from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
-import os, sys
+import os
 import argparse
 import numpy as np
 import json
@@ -28,10 +28,12 @@
     # sys.path.append('./cocoapi-master/PythonAPI/')
     from pycocotools import mask as COCOmask
 except Exception:
-    raise Exception("Please install pycocotools module from https://github.com/cocodataset/cocoapi")
+    raise Exception(
+        "Please install pycocotools module from https://github.com/cocodataset/cocoapi")
 
 OTHER_CLASS_ID = 183
 
+
 @get_traceback
 def extract_semantic_single_core(proc_id,
                                  annotations_set,
@@ -49,11 +51,13 @@ def extract_semantic_single_core(proc_id,
                                                                  len(annotations_set)))
         try:
             pan_format = np.array(
-                Image.open(os.path.join(segmentations_folder, annotation['file_name'])),
+                Image.open(os.path.join(
+                    segmentations_folder, annotation['file_name'])),
                 dtype=np.uint32
             )
         except IOError:
-            raise KeyError('no prediction png file for id: {}'.format(annotation['image_id']))
+            raise KeyError('no prediction png file for id: {}'.format(
+                annotation['image_id']))
 
         pan = rgb2id(pan_format)
         semantic = np.zeros(pan.shape, dtype=np.uint8)
@@ -72,7 +76,8 @@ def extract_semantic_single_core(proc_id,
                 RLE_per_category[cat_id].append(RLE)
 
         if save_as_png:
-            Image.fromarray(semantic).save(os.path.join(semantic_seg_folder, annotation['file_name']))
+            Image.fromarray(semantic).save(os.path.join(
+                semantic_seg_folder, annotation['file_name']))
         else:
             for cat_id, RLE_list in RLE_per_category.items():
                 if len(RLE_list) == 1:
@@ -87,7 +92,8 @@ def extract_semantic_single_core(proc_id,
                 semantic_seg_record["bbox"] = list(COCOmask.toBbox(RLE))
                 semantic_seg_record["iscrowd"] = 0
                 annotation_semantic_seg.append(semantic_seg_record)
-    print('Core: {}, all {} images processed'.format(proc_id, len(annotations_set)))
+    print('Core: {}, all {} images processed'.format(
+        proc_id, len(annotations_set)))
 
     return annotation_semantic_seg
 
@@ -124,9 +130,11 @@ def extract_semantic(input_json_file,
         else:
             save_as_png = True
             print("in PNG format:")
-            print("\tFolder with semnatic segmentations: {}".format(semantic_seg_folder))
+            print("\tFolder with semnatic segmentations: {}".format(
+                semantic_seg_folder))
             if not os.path.isdir(semantic_seg_folder):
-                print("Creating folder {} for semantic segmentation PNGs".format(semantic_seg_folder))
+                print("Creating folder {} for semantic segmentation PNGs".format(
+                    semantic_seg_folder))
                 os.mkdir(semantic_seg_folder)
     else:
         print("in COCO detection format:")
@@ -141,7 +149,8 @@ def extract_semantic(input_json_file,
 
     cpu_num = multiprocessing.cpu_count()
     annotations_split = np.array_split(annotations, cpu_num)
-    print("Number of cores: {}, images per core: {}".format(cpu_num, len(annotations_split[0])))
+    print("Number of cores: {}, images per core: {}".format(
+        cpu_num, len(annotations_split[0])))
     workers = multiprocessing.Pool(processes=cpu_num)
     processes = []
     for proc_id, annotations_set in enumerate(annotations_split):
diff --git a/panoptic_coco_categories.json b/panoptic_coco_categories.json
index 673a19e..d77e634 100644
--- a/panoptic_coco_categories.json
+++ b/panoptic_coco_categories.json
@@ -1 +1,1465 @@
-[{"supercategory": "person", "color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, {"supercategory": "vehicle", "color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"}, {"supercategory": "vehicle", "color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"}, {"supercategory": "vehicle", "color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"}, {"supercategory": "vehicle", "color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"}, {"supercategory": "vehicle", "color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"}, {"supercategory": "vehicle", "color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"}, {"supercategory": "vehicle", "color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"}, {"supercategory": "vehicle", "color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"}, {"supercategory": "outdoor", "color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"}, {"supercategory": "outdoor", "color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"}, {"supercategory": "outdoor", "color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"}, {"supercategory": "outdoor", "color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"}, {"supercategory": "outdoor", "color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"}, {"supercategory": "animal", "color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"}, {"supercategory": "animal", "color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"}, {"supercategory": "animal", "color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"}, {"supercategory": "animal", "color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, {"supercategory": "animal", "color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"}, {"supercategory": "animal", "color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"}, {"supercategory": "animal", "color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"}, {"supercategory": "animal", "color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"}, {"supercategory": "animal", "color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"}, {"supercategory": "animal", "color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"}, {"supercategory": "accessory", "color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"}, {"supercategory": "accessory", "color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"}, {"supercategory": "accessory", "color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"}, {"supercategory": "accessory", "color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"}, {"supercategory": "accessory", "color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"}, {"supercategory": "sports", "color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"}, {"supercategory": "sports", "color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"}, {"supercategory": "sports", "color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"}, {"supercategory": "sports", "color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"}, {"supercategory": "sports", "color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"}, {"supercategory": "sports", "color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"}, {"supercategory": "sports", "color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"}, {"supercategory": "sports", "color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"}, {"supercategory": "sports", "color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"}, {"supercategory": "sports", "color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"}, {"supercategory": "kitchen", "color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"}, {"supercategory": "kitchen", "color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"}, {"supercategory": 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+        "supercategory": "food",
+        "color": [
+            0,
+            143,
+            149
+        ],
+        "isthing": 1,
+        "id": 56,
+        "name": "broccoli"
+    },
+    {
+        "supercategory": "food",
+        "color": [
+            179,
+            0,
+            194
+        ],
+        "isthing": 1,
+        "id": 57,
+        "name": "carrot"
+    },
+    {
+        "supercategory": "food",
+        "color": [
+            209,
+            99,
+            106
+        ],
+        "isthing": 1,
+        "id": 58,
+        "name": "hot dog"
+    },
+    {
+        "supercategory": "food",
+        "color": [
+            5,
+            121,
+            0
+        ],
+        "isthing": 1,
+        "id": 59,
+        "name": "pizza"
+    },
+    {
+        "supercategory": "food",
+        "color": [
+            227,
+            255,
+            205
+        ],
+        "isthing": 1,
+        "id": 60,
+        "name": "donut"
+    },
+    {
+        "supercategory": "food",
+        "color": [
+            147,
+            186,
+            208
+        ],
+        "isthing": 1,
+        "id": 61,
+        "name": "cake"
+    },
+    {
+        "supercategory": "furniture",
+        "color": [
+            153,
+            69,
+            1
+        ],
+        "isthing": 1,
+        "id": 62,
+        "name": "chair"
+    },
+    {
+        "supercategory": "furniture",
+        "color": [
+            3,
+            95,
+            161
+        ],
+        "isthing": 1,
+        "id": 63,
+        "name": "couch"
+    },
+    {
+        "supercategory": "furniture",
+        "color": [
+            163,
+            255,
+            0
+        ],
+        "isthing": 1,
+        "id": 64,
+        "name": "potted plant"
+    },
+    {
+        "supercategory": "furniture",
+        "color": [
+            119,
+            0,
+            170
+        ],
+        "isthing": 1,
+        "id": 65,
+        "name": "bed"
+    },
+    {
+        "supercategory": "furniture",
+        "color": [
+            0,
+            182,
+            199
+        ],
+        "isthing": 1,
+        "id": 67,
+        "name": "dining table"
+    },
+    {
+        "supercategory": "furniture",
+        "color": [
+            0,
+            165,
+            120
+        ],
+        "isthing": 1,
+        "id": 70,
+        "name": "toilet"
+    },
+    {
+        "supercategory": "electronic",
+        "color": [
+            183,
+            130,
+            88
+        ],
+        "isthing": 1,
+        "id": 72,
+        "name": "tv"
+    },
+    {
+        "supercategory": "electronic",
+        "color": [
+            95,
+            32,
+            0
+        ],
+        "isthing": 1,
+        "id": 73,
+        "name": "laptop"
+    },
+    {
+        "supercategory": "electronic",
+        "color": [
+            130,
+            114,
+            135
+        ],
+        "isthing": 1,
+        "id": 74,
+        "name": "mouse"
+    },
+    {
+        "supercategory": "electronic",
+        "color": [
+            110,
+            129,
+            133
+        ],
+        "isthing": 1,
+        "id": 75,
+        "name": "remote"
+    },
+    {
+        "supercategory": "electronic",
+        "color": [
+            166,
+            74,
+            118
+        ],
+        "isthing": 1,
+        "id": 76,
+        "name": "keyboard"
+    },
+    {
+        "supercategory": "electronic",
+        "color": [
+            219,
+            142,
+            185
+        ],
+        "isthing": 1,
+        "id": 77,
+        "name": "cell phone"
+    },
+    {
+        "supercategory": "appliance",
+        "color": [
+            79,
+            210,
+            114
+        ],
+        "isthing": 1,
+        "id": 78,
+        "name": "microwave"
+    },
+    {
+        "supercategory": "appliance",
+        "color": [
+            178,
+            90,
+            62
+        ],
+        "isthing": 1,
+        "id": 79,
+        "name": "oven"
+    },
+    {
+        "supercategory": "appliance",
+        "color": [
+            65,
+            70,
+            15
+        ],
+        "isthing": 1,
+        "id": 80,
+        "name": "toaster"
+    },
+    {
+        "supercategory": "appliance",
+        "color": [
+            127,
+            167,
+            115
+        ],
+        "isthing": 1,
+        "id": 81,
+        "name": "sink"
+    },
+    {
+        "supercategory": "appliance",
+        "color": [
+            59,
+            105,
+            106
+        ],
+        "isthing": 1,
+        "id": 82,
+        "name": "refrigerator"
+    },
+    {
+        "supercategory": "indoor",
+        "color": [
+            142,
+            108,
+            45
+        ],
+        "isthing": 1,
+        "id": 84,
+        "name": "book"
+    },
+    {
+        "supercategory": "indoor",
+        "color": [
+            196,
+            172,
+            0
+        ],
+        "isthing": 1,
+        "id": 85,
+        "name": "clock"
+    },
+    {
+        "supercategory": "indoor",
+        "color": [
+            95,
+            54,
+            80
+        ],
+        "isthing": 1,
+        "id": 86,
+        "name": "vase"
+    },
+    {
+        "supercategory": "indoor",
+        "color": [
+            128,
+            76,
+            255
+        ],
+        "isthing": 1,
+        "id": 87,
+        "name": "scissors"
+    },
+    {
+        "supercategory": "indoor",
+        "color": [
+            201,
+            57,
+            1
+        ],
+        "isthing": 1,
+        "id": 88,
+        "name": "teddy bear"
+    },
+    {
+        "supercategory": "indoor",
+        "color": [
+            246,
+            0,
+            122
+        ],
+        "isthing": 1,
+        "id": 89,
+        "name": "hair drier"
+    },
+    {
+        "supercategory": "indoor",
+        "color": [
+            191,
+            162,
+            208
+        ],
+        "isthing": 1,
+        "id": 90,
+        "name": "toothbrush"
+    },
+    {
+        "supercategory": "textile",
+        "color": [
+            255,
+            255,
+            128
+        ],
+        "isthing": 0,
+        "id": 92,
+        "name": "banner"
+    },
+    {
+        "supercategory": "textile",
+        "color": [
+            147,
+            211,
+            203
+        ],
+        "isthing": 0,
+        "id": 93,
+        "name": "blanket"
+    },
+    {
+        "supercategory": "building",
+        "color": [
+            150,
+            100,
+            100
+        ],
+        "isthing": 0,
+        "id": 95,
+        "name": "bridge"
+    },
+    {
+        "supercategory": "raw-material",
+        "color": [
+            168,
+            171,
+            172
+        ],
+        "isthing": 0,
+        "id": 100,
+        "name": "cardboard"
+    },
+    {
+        "supercategory": "furniture-stuff",
+        "color": [
+            146,
+            112,
+            198
+        ],
+        "isthing": 0,
+        "id": 107,
+        "name": "counter"
+    },
+    {
+        "supercategory": "textile",
+        "color": [
+            210,
+            170,
+            100
+        ],
+        "isthing": 0,
+        "id": 109,
+        "name": "curtain"
+    },
+    {
+        "supercategory": "furniture-stuff",
+        "color": [
+            92,
+            136,
+            89
+        ],
+        "isthing": 0,
+        "id": 112,
+        "name": "door-stuff"
+    },
+    {
+        "supercategory": "floor",
+        "color": [
+            218,
+            88,
+            184
+        ],
+        "isthing": 0,
+        "id": 118,
+        "name": "floor-wood"
+    },
+    {
+        "supercategory": "plant",
+        "color": [
+            241,
+            129,
+            0
+        ],
+        "isthing": 0,
+        "id": 119,
+        "name": "flower"
+    },
+    {
+        "supercategory": "food-stuff",
+        "color": [
+            217,
+            17,
+            255
+        ],
+        "isthing": 0,
+        "id": 122,
+        "name": "fruit"
+    },
+    {
+        "supercategory": "ground",
+        "color": [
+            124,
+            74,
+            181
+        ],
+        "isthing": 0,
+        "id": 125,
+        "name": "gravel"
+    },
+    {
+        "supercategory": "building",
+        "color": [
+            70,
+            70,
+            70
+        ],
+        "isthing": 0,
+        "id": 128,
+        "name": "house"
+    },
+    {
+        "supercategory": "furniture-stuff",
+        "color": [
+            255,
+            228,
+            255
+        ],
+        "isthing": 0,
+        "id": 130,
+        "name": "light"
+    },
+    {
+        "supercategory": "furniture-stuff",
+        "color": [
+            154,
+            208,
+            0
+        ],
+        "isthing": 0,
+        "id": 133,
+        "name": "mirror-stuff"
+    },
+    {
+        "supercategory": "structural",
+        "color": [
+            193,
+            0,
+            92
+        ],
+        "isthing": 0,
+        "id": 138,
+        "name": "net"
+    },
+    {
+        "supercategory": "textile",
+        "color": [
+            76,
+            91,
+            113
+        ],
+        "isthing": 0,
+        "id": 141,
+        "name": "pillow"
+    },
+    {
+        "supercategory": "ground",
+        "color": [
+            255,
+            180,
+            195
+        ],
+        "isthing": 0,
+        "id": 144,
+        "name": "platform"
+    },
+    {
+        "supercategory": "ground",
+        "color": [
+            106,
+            154,
+            176
+        ],
+        "isthing": 0,
+        "id": 145,
+        "name": "playingfield"
+    },
+    {
+        "supercategory": "ground",
+        "color": [
+            230,
+            150,
+            140
+        ],
+        "isthing": 0,
+        "id": 147,
+        "name": "railroad"
+    },
+    {
+        "supercategory": "water",
+        "color": [
+            60,
+            143,
+            255
+        ],
+        "isthing": 0,
+        "id": 148,
+        "name": "river"
+    },
+    {
+        "supercategory": "ground",
+        "color": [
+            128,
+            64,
+            128
+        ],
+        "isthing": 0,
+        "id": 149,
+        "name": "road"
+    },
+    {
+        "supercategory": "building",
+        "color": [
+            92,
+            82,
+            55
+        ],
+        "isthing": 0,
+        "id": 151,
+        "name": "roof"
+    },
+    {
+        "supercategory": "ground",
+        "color": [
+            254,
+            212,
+            124
+        ],
+        "isthing": 0,
+        "id": 154,
+        "name": "sand"
+    },
+    {
+        "supercategory": "water",
+        "color": [
+            73,
+            77,
+            174
+        ],
+        "isthing": 0,
+        "id": 155,
+        "name": "sea"
+    },
+    {
+        "supercategory": "furniture-stuff",
+        "color": [
+            255,
+            160,
+            98
+        ],
+        "isthing": 0,
+        "id": 156,
+        "name": "shelf"
+    },
+    {
+        "supercategory": "ground",
+        "color": [
+            255,
+            255,
+            255
+        ],
+        "isthing": 0,
+        "id": 159,
+        "name": "snow"
+    },
+    {
+        "supercategory": "furniture-stuff",
+        "color": [
+            104,
+            84,
+            109
+        ],
+        "isthing": 0,
+        "id": 161,
+        "name": "stairs"
+    },
+    {
+        "supercategory": "building",
+        "color": [
+            169,
+            164,
+            131
+        ],
+        "isthing": 0,
+        "id": 166,
+        "name": "tent"
+    },
+    {
+        "supercategory": "textile",
+        "color": [
+            225,
+            199,
+            255
+        ],
+        "isthing": 0,
+        "id": 168,
+        "name": "towel"
+    },
+    {
+        "supercategory": "wall",
+        "color": [
+            137,
+            54,
+            74
+        ],
+        "isthing": 0,
+        "id": 171,
+        "name": "wall-brick"
+    },
+    {
+        "supercategory": "wall",
+        "color": [
+            135,
+            158,
+            223
+        ],
+        "isthing": 0,
+        "id": 175,
+        "name": "wall-stone"
+    },
+    {
+        "supercategory": "wall",
+        "color": [
+            7,
+            246,
+            231
+        ],
+        "isthing": 0,
+        "id": 176,
+        "name": "wall-tile"
+    },
+    {
+        "supercategory": "wall",
+        "color": [
+            107,
+            255,
+            200
+        ],
+        "isthing": 0,
+        "id": 177,
+        "name": "wall-wood"
+    },
+    {
+        "supercategory": "water",
+        "color": [
+            58,
+            41,
+            149
+        ],
+        "isthing": 0,
+        "id": 178,
+        "name": "water-other"
+    },
+    {
+        "supercategory": "window",
+        "color": [
+            183,
+            121,
+            142
+        ],
+        "isthing": 0,
+        "id": 180,
+        "name": "window-blind"
+    },
+    {
+        "supercategory": "window",
+        "color": [
+            255,
+            73,
+            97
+        ],
+        "isthing": 0,
+        "id": 181,
+        "name": "window-other"
+    },
+    {
+        "supercategory": "plant",
+        "color": [
+            107,
+            142,
+            35
+        ],
+        "isthing": 0,
+        "id": 184,
+        "name": "tree-merged"
+    },
+    {
+        "supercategory": "structural",
+        "color": [
+            190,
+            153,
+            153
+        ],
+        "isthing": 0,
+        "id": 185,
+        "name": "fence-merged"
+    },
+    {
+        "supercategory": "ceiling",
+        "color": [
+            146,
+            139,
+            141
+        ],
+        "isthing": 0,
+        "id": 186,
+        "name": "ceiling-merged"
+    },
+    {
+        "supercategory": "sky",
+        "color": [
+            70,
+            130,
+            180
+        ],
+        "isthing": 0,
+        "id": 187,
+        "name": "sky-other-merged"
+    },
+    {
+        "supercategory": "furniture-stuff",
+        "color": [
+            134,
+            199,
+            156
+        ],
+        "isthing": 0,
+        "id": 188,
+        "name": "cabinet-merged"
+    },
+    {
+        "supercategory": "furniture-stuff",
+        "color": [
+            209,
+            226,
+            140
+        ],
+        "isthing": 0,
+        "id": 189,
+        "name": "table-merged"
+    },
+    {
+        "supercategory": "floor",
+        "color": [
+            96,
+            36,
+            108
+        ],
+        "isthing": 0,
+        "id": 190,
+        "name": "floor-other-merged"
+    },
+    {
+        "supercategory": "ground",
+        "color": [
+            96,
+            96,
+            96
+        ],
+        "isthing": 0,
+        "id": 191,
+        "name": "pavement-merged"
+    },
+    {
+        "supercategory": "solid",
+        "color": [
+            64,
+            170,
+            64
+        ],
+        "isthing": 0,
+        "id": 192,
+        "name": "mountain-merged"
+    },
+    {
+        "supercategory": "plant",
+        "color": [
+            152,
+            251,
+            152
+        ],
+        "isthing": 0,
+        "id": 193,
+        "name": "grass-merged"
+    },
+    {
+        "supercategory": "ground",
+        "color": [
+            208,
+            229,
+            228
+        ],
+        "isthing": 0,
+        "id": 194,
+        "name": "dirt-merged"
+    },
+    {
+        "supercategory": "raw-material",
+        "color": [
+            206,
+            186,
+            171
+        ],
+        "isthing": 0,
+        "id": 195,
+        "name": "paper-merged"
+    },
+    {
+        "supercategory": "food-stuff",
+        "color": [
+            152,
+            161,
+            64
+        ],
+        "isthing": 0,
+        "id": 196,
+        "name": "food-other-merged"
+    },
+    {
+        "supercategory": "building",
+        "color": [
+            116,
+            112,
+            0
+        ],
+        "isthing": 0,
+        "id": 197,
+        "name": "building-other-merged"
+    },
+    {
+        "supercategory": "solid",
+        "color": [
+            0,
+            114,
+            143
+        ],
+        "isthing": 0,
+        "id": 198,
+        "name": "rock-merged"
+    },
+    {
+        "supercategory": "wall",
+        "color": [
+            102,
+            102,
+            156
+        ],
+        "isthing": 0,
+        "id": 199,
+        "name": "wall-other-merged"
+    },
+    {
+        "supercategory": "textile",
+        "color": [
+            250,
+            141,
+            255
+        ],
+        "isthing": 0,
+        "id": 200,
+        "name": "rug-merged"
+    }
+]
\ No newline at end of file
diff --git a/panopticapi/combine_semantic_and_instance_predictions.py b/panopticapi/combine_semantic_and_instance_predictions.py
index ba58477..f7cd55a 100755
--- a/panopticapi/combine_semantic_and_instance_predictions.py
+++ b/panopticapi/combine_semantic_and_instance_predictions.py
@@ -12,7 +12,7 @@
 from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
-import os, sys
+import os
 import argparse
 import numpy as np
 from collections import defaultdict
@@ -23,12 +23,13 @@
 
 from panopticapi.utils import IdGenerator, id2rgb, save_json
 
-import PIL.Image     as Image
+import PIL.Image as Image
 
 try:
     from pycocotools import mask as COCOmask
 except Exception:
-    raise Exception("Please install pycocotools module from https://github.com/cocodataset/cocoapi")
+    raise Exception(
+        "Please install pycocotools module from https://github.com/cocodataset/cocoapi")
 
 
 def combine_to_panoptic_single_core(proc_id, img_ids, img_id2img, inst_by_image,
@@ -108,7 +109,8 @@ def combine_to_panoptic_multi_core(img_id2img, inst_by_image,
                                    stuff_area_limit, categories):
     cpu_num = multiprocessing.cpu_count()
     img_ids_split = np.array_split(list(img_id2img), cpu_num)
-    print("Number of cores: {}, images per core: {}".format(cpu_num, len(img_ids_split[0])))
+    print("Number of cores: {}, images per core: {}".format(
+        cpu_num, len(img_ids_split[0])))
     workers = multiprocessing.Pool(processes=cpu_num)
     processes = []
     for proc_id, img_ids in enumerate(img_ids_split):
@@ -144,7 +146,8 @@ def combine_predictions(semseg_json_file, instseg_json_file, images_json_file,
     if segmentations_folder is None:
         segmentations_folder = panoptic_json_file.rsplit('.', 1)[0]
     if not os.path.isdir(segmentations_folder):
-        print("Creating folder {} for panoptic segmentation PNGs".format(segmentations_folder))
+        print("Creating folder {} for panoptic segmentation PNGs".format(
+            segmentations_folder))
         os.mkdir(segmentations_folder)
 
     print("Combining:")
@@ -166,7 +169,8 @@ def combine_predictions(semseg_json_file, instseg_json_file, images_json_file,
             continue
         inst_by_image[inst['image_id']].append(inst)
     for img_id in inst_by_image.keys():
-        inst_by_image[img_id] = sorted(inst_by_image[img_id], key=lambda el: -el['score'])
+        inst_by_image[img_id] = sorted(
+            inst_by_image[img_id], key=lambda el: -el['score'])
 
     sem_by_image = defaultdict(list)
     for sem in sem_results:
diff --git a/panopticapi/evaluation.py b/panopticapi/evaluation.py
index 26b4970..b39890b 100755
--- a/panopticapi/evaluation.py
+++ b/panopticapi/evaluation.py
@@ -3,11 +3,10 @@
 from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
-import os, sys
+import os
 import numpy as np
 import json
 import time
-from datetime import timedelta
 from collections import defaultdict
 import argparse
 import multiprocessing
@@ -19,19 +18,20 @@
 OFFSET = 256 * 256 * 256
 VOID = 0
 
-class PQStatCat():
-        def __init__(self):
-            self.iou = 0.0
-            self.tp = 0
-            self.fp = 0
-            self.fn = 0
 
-        def __iadd__(self, pq_stat_cat):
-            self.iou += pq_stat_cat.iou
-            self.tp += pq_stat_cat.tp
-            self.fp += pq_stat_cat.fp
-            self.fn += pq_stat_cat.fn
-            return self
+class PQStatCat():
+    def __init__(self):
+        self.iou = 0.0
+        self.tp = 0
+        self.fp = 0
+        self.fn = 0
+
+    def __iadd__(self, pq_stat_cat):
+        self.iou += pq_stat_cat.iou
+        self.tp += pq_stat_cat.tp
+        self.fp += pq_stat_cat.fp
+        self.fn += pq_stat_cat.fn
+        return self
 
 
 class PQStat():
@@ -65,7 +65,8 @@ def pq_average(self, categories, isthing):
             pq_class = iou / (tp + 0.5 * fp + 0.5 * fn)
             sq_class = iou / tp if tp != 0 else 0
             rq_class = tp / (tp + 0.5 * fp + 0.5 * fn)
-            per_class_results[label] = {'pq': pq_class, 'sq': sq_class, 'rq': rq_class}
+            per_class_results[label] = {
+                'pq': pq_class, 'sq': sq_class, 'rq': rq_class}
             pq += pq_class
             sq += sq_class
             rq += rq_class
@@ -80,12 +81,15 @@ def pq_compute_single_core(proc_id, annotation_set, gt_folder, pred_folder, cate
     idx = 0
     for gt_ann, pred_ann in annotation_set:
         if idx % 100 == 0:
-            print('Core: {}, {} from {} images processed'.format(proc_id, idx, len(annotation_set)))
+            print('Core: {}, {} from {} images processed'.format(
+                proc_id, idx, len(annotation_set)))
         idx += 1
 
-        pan_gt = np.array(Image.open(os.path.join(gt_folder, gt_ann['file_name'])), dtype=np.uint32)
+        pan_gt = np.array(Image.open(os.path.join(
+            gt_folder, gt_ann['file_name'])), dtype=np.uint32)
         pan_gt = rgb2id(pan_gt)
-        pan_pred = np.array(Image.open(os.path.join(pred_folder, pred_ann['file_name'])), dtype=np.uint32)
+        pan_pred = np.array(Image.open(os.path.join(
+            pred_folder, pred_ann['file_name'])), dtype=np.uint32)
         pan_pred = rgb2id(pan_pred)
 
         gt_segms = {el['id']: el for el in gt_ann['segments_info']}
@@ -98,16 +102,20 @@ def pq_compute_single_core(proc_id, annotation_set, gt_folder, pred_folder, cate
             if label not in pred_segms:
                 if label == VOID:
                     continue
-                raise KeyError('In the image with ID {} segment with ID {} is presented in PNG and not presented in JSON.'.format(gt_ann['image_id'], label))
+                raise KeyError('In the image with ID {} segment with ID {} is presented in PNG and not presented in JSON.'.format(
+                    gt_ann['image_id'], label))
             pred_segms[label]['area'] = label_cnt
             pred_labels_set.remove(label)
             if pred_segms[label]['category_id'] not in categories:
-                raise KeyError('In the image with ID {} segment with ID {} has unknown category_id {}.'.format(gt_ann['image_id'], label, pred_segms[label]['category_id']))
+                raise KeyError('In the image with ID {} segment with ID {} has unknown category_id {}.'.format(
+                    gt_ann['image_id'], label, pred_segms[label]['category_id']))
         if len(pred_labels_set) != 0:
-            raise KeyError('In the image with ID {} the following segment IDs {} are presented in JSON and not presented in PNG.'.format(gt_ann['image_id'], list(pred_labels_set)))
+            raise KeyError('In the image with ID {} the following segment IDs {} are presented in JSON and not presented in PNG.'.format(
+                gt_ann['image_id'], list(pred_labels_set)))
 
         # confusion matrix calculation
-        pan_gt_pred = pan_gt.astype(np.uint64) * OFFSET + pan_pred.astype(np.uint64)
+        pan_gt_pred = pan_gt.astype(np.uint64) * \
+            OFFSET + pan_pred.astype(np.uint64)
         gt_pred_map = {}
         labels, labels_cnt = np.unique(pan_gt_pred, return_counts=True)
         for label, intersection in zip(labels, labels_cnt):
@@ -129,7 +137,8 @@ def pq_compute_single_core(proc_id, annotation_set, gt_folder, pred_folder, cate
             if gt_segms[gt_label]['category_id'] != pred_segms[pred_label]['category_id']:
                 continue
 
-            union = pred_segms[pred_label]['area'] + gt_segms[gt_label]['area'] - intersection - gt_pred_map.get((VOID, pred_label), 0)
+            union = pred_segms[pred_label]['area'] + gt_segms[gt_label]['area'] - \
+                intersection - gt_pred_map.get((VOID, pred_label), 0)
             iou = intersection / union
             if iou > 0.5:
                 pq_stat[gt_segms[gt_label]['category_id']].tp += 1
@@ -156,19 +165,22 @@ def pq_compute_single_core(proc_id, annotation_set, gt_folder, pred_folder, cate
             intersection = gt_pred_map.get((VOID, pred_label), 0)
             # plus intersection with corresponding CROWD region if it exists
             if pred_info['category_id'] in crowd_labels_dict:
-                intersection += gt_pred_map.get((crowd_labels_dict[pred_info['category_id']], pred_label), 0)
+                intersection += gt_pred_map.get(
+                    (crowd_labels_dict[pred_info['category_id']], pred_label), 0)
             # predicted segment is ignored if more than half of the segment correspond to VOID and CROWD regions
             if intersection / pred_info['area'] > 0.5:
                 continue
             pq_stat[pred_info['category_id']].fp += 1
-    print('Core: {}, all {} images processed'.format(proc_id, len(annotation_set)))
+    print('Core: {}, all {} images processed'.format(
+        proc_id, len(annotation_set)))
     return pq_stat
 
 
 def pq_compute_multi_core(matched_annotations_list, gt_folder, pred_folder, categories):
     cpu_num = multiprocessing.cpu_count()
     annotations_split = np.array_split(matched_annotations_list, cpu_num)
-    print("Number of cores: {}, images per core: {}".format(cpu_num, len(annotations_split[0])))
+    print("Number of cores: {}, images per core: {}".format(
+        cpu_num, len(annotations_split[0])))
     workers = multiprocessing.Pool(processes=cpu_num)
     processes = []
     for proc_id, annotation_set in enumerate(annotations_split):
@@ -204,27 +216,33 @@ def pq_compute(gt_json_file, pred_json_file, gt_folder=None, pred_folder=None):
     print("\tJSON file: {}".format(pred_json_file))
 
     if not os.path.isdir(gt_folder):
-        raise Exception("Folder {} with ground truth segmentations doesn't exist".format(gt_folder))
+        raise Exception(
+            "Folder {} with ground truth segmentations doesn't exist".format(gt_folder))
     if not os.path.isdir(pred_folder):
-        raise Exception("Folder {} with predicted segmentations doesn't exist".format(pred_folder))
+        raise Exception(
+            "Folder {} with predicted segmentations doesn't exist".format(pred_folder))
 
     pred_annotations = {el['image_id']: el for el in pred_json['annotations']}
     matched_annotations_list = []
     for gt_ann in gt_json['annotations']:
         image_id = gt_ann['image_id']
         if image_id not in pred_annotations:
-            raise Exception('no prediction for the image with id: {}'.format(image_id))
+            raise Exception(
+                'no prediction for the image with id: {}'.format(image_id))
         matched_annotations_list.append((gt_ann, pred_annotations[image_id]))
 
-    pq_stat = pq_compute_multi_core(matched_annotations_list, gt_folder, pred_folder, categories)
+    pq_stat = pq_compute_multi_core(
+        matched_annotations_list, gt_folder, pred_folder, categories)
 
     metrics = [("All", None), ("Things", True), ("Stuff", False)]
     results = {}
     for name, isthing in metrics:
-        results[name], per_class_results = pq_stat.pq_average(categories, isthing=isthing)
+        results[name], per_class_results = pq_stat.pq_average(
+            categories, isthing=isthing)
         if name == 'All':
             results['per_class'] = per_class_results
-    print("{:10s}| {:>5s}  {:>5s}  {:>5s} {:>5s}".format("", "PQ", "SQ", "RQ", "N"))
+    print("{:10s}| {:>5s}  {:>5s}  {:>5s} {:>5s}".format(
+        "", "PQ", "SQ", "RQ", "N"))
     print("-" * (10 + 7 * 4))
 
     for name, _isthing in metrics:
@@ -255,4 +273,5 @@ def pq_compute(gt_json_file, pred_json_file, gt_folder=None, pred_folder=None):
                         help="Folder with prediction COCO format segmentations. \
                               Default: X if the corresponding json file is X.json")
     args = parser.parse_args()
-    pq_compute(args.gt_json_file, args.pred_json_file, args.gt_folder, args.pred_folder)
+    pq_compute(args.gt_json_file, args.pred_json_file,
+               args.gt_folder, args.pred_folder)
diff --git a/panopticapi/utils.py b/panopticapi/utils.py
index 0dea536..ef76ec0 100644
--- a/panopticapi/utils.py
+++ b/panopticapi/utils.py
@@ -32,6 +32,7 @@ class IdGenerator():
     class ids are presented and category_info record is a dict with fields
     'isthing' and 'color'
     '''
+
     def __init__(self, categories):
         self.taken_colors = set([0, 0, 0])
         self.categories = categories
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..5844e70
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,7 @@
+scikit_image==0.18.1
+numpy==1.20.3
+matplotlib==3.4.2
+cityscapesscripts==2.2.0
+Pillow==8.3.2
+pycocotools==2.0.2
+skimage==0.0
diff --git a/sample_data/images_info_examples.json b/sample_data/images_info_examples.json
index e572628..e59c6f1 100644
--- a/sample_data/images_info_examples.json
+++ b/sample_data/images_info_examples.json
@@ -1 +1,1539 @@
-{"info": {"description": "COCO 2018 Panoptic Dataset", "url": "http://cocodataset.org", "version": "1.0", "year": 2018, "contributor": "https://arxiv.org/abs/1801.00868", "date_created": "2018-06-01 00:00:00.0"}, "licenses": [{"url": "http://creativecommons.org/licenses/by-nc-sa/2.0/", "id": 1, "name": "Attribution-NonCommercial-ShareAlike License"}, {"url": "http://creativecommons.org/licenses/by-nc/2.0/", "id": 2, "name": "Attribution-NonCommercial License"}, {"url": "http://creativecommons.org/licenses/by-nc-nd/2.0/", "id": 3, "name": "Attribution-NonCommercial-NoDerivs License"}, {"url": "http://creativecommons.org/licenses/by/2.0/", "id": 4, "name": "Attribution License"}, {"url": "http://creativecommons.org/licenses/by-sa/2.0/", "id": 5, "name": "Attribution-ShareAlike License"}, {"url": "http://creativecommons.org/licenses/by-nd/2.0/", "id": 6, "name": "Attribution-NoDerivs License"}, {"url": "http://flickr.com/commons/usage/", "id": 7, "name": "No known copyright restrictions"}, {"url": "http://www.usa.gov/copyright.shtml", "id": 8, "name": "United States Government Work"}], "images": [{"license": 2, "file_name": "000000142238.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000142238.jpg", "height": 427, "width": 640, "date_captured": "2013-11-20 16:47:35", "flickr_url": "http://farm5.staticflickr.com/4028/5079131149_dde584ed79_z.jpg", "id": 142238}, {"license": 1, "file_name": "000000439180.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000439180.jpg", "height": 360, "width": 640, "date_captured": "2013-11-19 01:25:39", "flickr_url": "http://farm3.staticflickr.com/2831/9275116980_1d9b986e3b_z.jpg", "id": 439180}], "categories": [{"supercategory": "person", "color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, {"supercategory": "vehicle", "color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"}, {"supercategory": "vehicle", "color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"}, {"supercategory": "vehicle", "color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"}, {"supercategory": "vehicle", "color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"}, {"supercategory": "vehicle", "color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"}, {"supercategory": "vehicle", "color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"}, {"supercategory": "vehicle", "color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"}, {"supercategory": "vehicle", "color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"}, {"supercategory": "outdoor", "color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"}, {"supercategory": "outdoor", "color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"}, {"supercategory": "outdoor", "color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"}, {"supercategory": "outdoor", "color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"}, {"supercategory": "outdoor", "color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"}, {"supercategory": "animal", "color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"}, {"supercategory": "animal", "color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"}, {"supercategory": "animal", "color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"}, {"supercategory": "animal", "color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, {"supercategory": "animal", "color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"}, {"supercategory": "animal", "color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"}, {"supercategory": "animal", "color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"}, {"supercategory": "animal", "color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"}, {"supercategory": "animal", "color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"}, {"supercategory": "animal", "color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"}, {"supercategory": "accessory", "color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"}, {"supercategory": "accessory", "color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"}, {"supercategory": "accessory", "color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"}, {"supercategory": "accessory", "color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"}, {"supercategory": "accessory", "color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"}, {"supercategory": "sports", "color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"}, {"supercategory": "sports", "color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"}, {"supercategory": "sports", "color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"}, {"supercategory": "sports", "color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"}, {"supercategory": "sports", "color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"}, {"supercategory": "sports", "color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"}, {"supercategory": "sports", "color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"}, {"supercategory": "sports", "color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"}, {"supercategory": "sports", "color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"}, {"supercategory": "sports", "color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"}, {"supercategory": "kitchen", "color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"}, {"supercategory": "kitchen", "color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"}, {"supercategory": "kitchen", "color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"}, {"supercategory": "kitchen", "color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"}, {"supercategory": "kitchen", "color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"}, {"supercategory": "kitchen", "color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"}, {"supercategory": "kitchen", "color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"}, {"supercategory": "food", "color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"}, {"supercategory": "food", "color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"}, {"supercategory": "food", "color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"}, {"supercategory": "food", "color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"}, {"supercategory": "food", "color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"}, {"supercategory": "food", "color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"}, {"supercategory": "food", "color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"}, {"supercategory": "food", "color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"}, {"supercategory": "food", "color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"}, {"supercategory": "food", "color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"}, {"supercategory": "furniture", "color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"}, {"supercategory": "furniture", "color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"}, {"supercategory": "furniture", "color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"}, {"supercategory": "furniture", "color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"}, {"supercategory": "furniture", "color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"}, {"supercategory": "furniture", "color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"}, {"supercategory": "electronic", "color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"}, {"supercategory": "electronic", "color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"}, {"supercategory": "electronic", "color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"}, {"supercategory": "electronic", "color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"}, {"supercategory": "electronic", "color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"}, {"supercategory": "electronic", "color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"}, {"supercategory": "appliance", "color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"}, {"supercategory": "appliance", "color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"}, {"supercategory": "appliance", "color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"}, {"supercategory": "appliance", "color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"}, {"supercategory": "appliance", "color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"}, {"supercategory": "indoor", "color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"}, {"supercategory": "indoor", "color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"}, {"supercategory": "indoor", "color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"}, {"supercategory": "indoor", "color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"}, {"supercategory": "indoor", "color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"}, {"supercategory": "indoor", "color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"}, {"supercategory": "indoor", "color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"}, {"supercategory": "textile", "color": [255, 255, 128], "isthing": 0, "id": 92, "name": "banner"}, {"supercategory": "textile", "color": [147, 211, 203], "isthing": 0, "id": 93, "name": "blanket"}, {"supercategory": "building", "color": [150, 100, 100], "isthing": 0, "id": 95, "name": "bridge"}, {"supercategory": "raw-material", "color": [168, 171, 172], "isthing": 0, "id": 100, "name": "cardboard"}, {"supercategory": "furniture-stuff", "color": [146, 112, 198], "isthing": 0, "id": 107, "name": "counter"}, {"supercategory": "textile", "color": [210, 170, 100], "isthing": 0, "id": 109, "name": "curtain"}, {"supercategory": "furniture-stuff", "color": [92, 136, 89], "isthing": 0, "id": 112, "name": "door-stuff"}, {"supercategory": "floor", "color": [218, 88, 184], "isthing": 0, "id": 118, "name": "floor-wood"}, {"supercategory": "plant", "color": [241, 129, 0], "isthing": 0, "id": 119, "name": "flower"}, {"supercategory": "food-stuff", "color": [217, 17, 255], "isthing": 0, "id": 122, "name": "fruit"}, {"supercategory": "ground", "color": [124, 74, 181], "isthing": 0, "id": 125, "name": "gravel"}, {"supercategory": "building", "color": [70, 70, 70], "isthing": 0, "id": 128, "name": "house"}, {"supercategory": "furniture-stuff", "color": [255, 228, 255], "isthing": 0, "id": 130, "name": "light"}, {"supercategory": "furniture-stuff", "color": [154, 208, 0], "isthing": 0, "id": 133, "name": "mirror-stuff"}, {"supercategory": "structural", "color": [193, 0, 92], "isthing": 0, "id": 138, "name": "net"}, {"supercategory": "textile", "color": [76, 91, 113], "isthing": 0, "id": 141, "name": "pillow"}, {"supercategory": "ground", "color": [255, 180, 195], "isthing": 0, "id": 144, "name": "platform"}, {"supercategory": "ground", "color": [106, 154, 176], "isthing": 0, "id": 145, "name": "playingfield"}, {"supercategory": "ground", "color": [230, 150, 140], "isthing": 0, "id": 147, "name": "railroad"}, {"supercategory": "water", "color": [60, 143, 255], "isthing": 0, "id": 148, "name": "river"}, {"supercategory": "ground", "color": [128, 64, 128], "isthing": 0, "id": 149, "name": "road"}, {"supercategory": "building", "color": [92, 82, 55], "isthing": 0, "id": 151, "name": "roof"}, {"supercategory": "ground", "color": [254, 212, 124], "isthing": 0, "id": 154, "name": "sand"}, {"supercategory": "water", "color": [73, 77, 174], "isthing": 0, "id": 155, "name": "sea"}, {"supercategory": "furniture-stuff", "color": [255, 160, 98], "isthing": 0, "id": 156, "name": "shelf"}, {"supercategory": "ground", "color": [255, 255, 255], "isthing": 0, "id": 159, "name": "snow"}, {"supercategory": "furniture-stuff", "color": [104, 84, 109], "isthing": 0, "id": 161, "name": "stairs"}, {"supercategory": "building", "color": [169, 164, 131], "isthing": 0, "id": 166, "name": "tent"}, {"supercategory": "textile", "color": [225, 199, 255], "isthing": 0, "id": 168, "name": "towel"}, {"supercategory": "wall", "color": [137, 54, 74], "isthing": 0, "id": 171, "name": "wall-brick"}, {"supercategory": "wall", "color": [135, 158, 223], "isthing": 0, "id": 175, "name": "wall-stone"}, {"supercategory": "wall", "color": [7, 246, 231], "isthing": 0, "id": 176, "name": "wall-tile"}, {"supercategory": "wall", "color": [107, 255, 200], "isthing": 0, "id": 177, "name": "wall-wood"}, {"supercategory": "water", "color": [58, 41, 149], "isthing": 0, "id": 178, "name": "water-other"}, {"supercategory": "window", "color": [183, 121, 142], "isthing": 0, "id": 180, "name": "window-blind"}, 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+            "name": "food-other-merged"
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+                0
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+            "name": "building-other-merged"
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+            "color": [
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+                143
+            ],
+            "isthing": 0,
+            "id": 198,
+            "name": "rock-merged"
+        },
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+                156
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+            "isthing": 0,
+            "id": 199,
+            "name": "wall-other-merged"
+        },
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+                141,
+                255
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+            "isthing": 0,
+            "id": 200,
+            "name": "rug-merged"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/sample_data/panoptic_coco_detection_format.json b/sample_data/panoptic_coco_detection_format.json
index eb3b667..565aa46 100644
--- a/sample_data/panoptic_coco_detection_format.json
+++ b/sample_data/panoptic_coco_detection_format.json
@@ -1 +1,2408 @@
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+            "name": "laptop"
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+                135
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+            "name": "mouse"
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+            "name": "keyboard"
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+            "color": [
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+                142,
+                185
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+            "name": "cell phone"
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+        {
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+                114
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+        {
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+            "color": [
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+                90,
+                62
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+            "name": "oven"
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+        {
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+            "name": "toaster"
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+        {
+            "supercategory": "appliance",
+            "color": [
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+                167,
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+            "name": "sink"
+        },
+        {
+            "supercategory": "appliance",
+            "color": [
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+                105,
+                106
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+            "name": "refrigerator"
+        },
+        {
+            "supercategory": "indoor",
+            "color": [
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+                108,
+                45
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+            "id": 84,
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+            "color": [
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+                0
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+            "id": 85,
+            "name": "clock"
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+        {
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+            "name": "vase"
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+                76,
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+        {
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+                1
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+            "color": [
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+                0,
+                122
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+            "name": "hair drier"
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+            "color": [
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+                162,
+                208
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+            "id": 90,
+            "name": "toothbrush"
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+        {
+            "supercategory": "textile",
+            "color": [
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+                255,
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+        {
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+            "name": "blanket"
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+        {
+            "supercategory": "building",
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+                100
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+            "color": [
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+                17,
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+            "name": "fruit"
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+        {
+            "supercategory": "ground",
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+                74,
+                181
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+            "id": 125,
+            "name": "gravel"
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+            "name": "light"
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+                0
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+            "name": "mirror-stuff"
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+        {
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+            "color": [
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+                0,
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+            "name": "platform"
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+                176
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+            "name": "road"
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+                55
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+            "name": "snow"
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+                109
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+                255
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+        {
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+                223
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+            "name": "wall-stone"
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+                231
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+            "id": 176,
+            "name": "wall-tile"
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+            "color": [
+                107,
+                255,
+                200
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+            "id": 177,
+            "name": "wall-wood"
+        },
+        {
+            "supercategory": "water",
+            "color": [
+                58,
+                41,
+                149
+            ],
+            "id": 178,
+            "name": "water-other"
+        },
+        {
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+                121,
+                142
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+            "name": "window-blind"
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+            "color": [
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+                73,
+                97
+            ],
+            "id": 181,
+            "name": "window-other"
+        },
+        {
+            "supercategory": "plant",
+            "color": [
+                107,
+                142,
+                35
+            ],
+            "id": 184,
+            "name": "tree-merged"
+        },
+        {
+            "supercategory": "structural",
+            "color": [
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+                153,
+                153
+            ],
+            "id": 185,
+            "name": "fence-merged"
+        },
+        {
+            "supercategory": "ceiling",
+            "color": [
+                146,
+                139,
+                141
+            ],
+            "id": 186,
+            "name": "ceiling-merged"
+        },
+        {
+            "supercategory": "sky",
+            "color": [
+                70,
+                130,
+                180
+            ],
+            "id": 187,
+            "name": "sky-other-merged"
+        },
+        {
+            "supercategory": "furniture-stuff",
+            "color": [
+                134,
+                199,
+                156
+            ],
+            "id": 188,
+            "name": "cabinet-merged"
+        },
+        {
+            "supercategory": "furniture-stuff",
+            "color": [
+                209,
+                226,
+                140
+            ],
+            "id": 189,
+            "name": "table-merged"
+        },
+        {
+            "supercategory": "floor",
+            "color": [
+                96,
+                36,
+                108
+            ],
+            "id": 190,
+            "name": "floor-other-merged"
+        },
+        {
+            "supercategory": "ground",
+            "color": [
+                96,
+                96,
+                96
+            ],
+            "id": 191,
+            "name": "pavement-merged"
+        },
+        {
+            "supercategory": "solid",
+            "color": [
+                64,
+                170,
+                64
+            ],
+            "id": 192,
+            "name": "mountain-merged"
+        },
+        {
+            "supercategory": "plant",
+            "color": [
+                152,
+                251,
+                152
+            ],
+            "id": 193,
+            "name": "grass-merged"
+        },
+        {
+            "supercategory": "ground",
+            "color": [
+                208,
+                229,
+                228
+            ],
+            "id": 194,
+            "name": "dirt-merged"
+        },
+        {
+            "supercategory": "raw-material",
+            "color": [
+                206,
+                186,
+                171
+            ],
+            "id": 195,
+            "name": "paper-merged"
+        },
+        {
+            "supercategory": "food-stuff",
+            "color": [
+                152,
+                161,
+                64
+            ],
+            "id": 196,
+            "name": "food-other-merged"
+        },
+        {
+            "supercategory": "building",
+            "color": [
+                116,
+                112,
+                0
+            ],
+            "id": 197,
+            "name": "building-other-merged"
+        },
+        {
+            "supercategory": "solid",
+            "color": [
+                0,
+                114,
+                143
+            ],
+            "id": 198,
+            "name": "rock-merged"
+        },
+        {
+            "supercategory": "wall",
+            "color": [
+                102,
+                102,
+                156
+            ],
+            "id": 199,
+            "name": "wall-other-merged"
+        },
+        {
+            "supercategory": "textile",
+            "color": [
+                250,
+                141,
+                255
+            ],
+            "id": 200,
+            "name": "rug-merged"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/sample_data/panoptic_examples.json b/sample_data/panoptic_examples.json
index 7ddc893..b2e099d 100644
--- a/sample_data/panoptic_examples.json
+++ b/sample_data/panoptic_examples.json
@@ -1 +1,2153 @@
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+            ],
+            "isthing": 1,
+            "id": 49,
+            "name": "knife"
+        },
+        {
+            "supercategory": "kitchen",
+            "color": [
+                9,
+                80,
+                61
+            ],
+            "isthing": 1,
+            "id": 50,
+            "name": "spoon"
+        },
+        {
+            "supercategory": "kitchen",
+            "color": [
+                84,
+                105,
+                51
+            ],
+            "isthing": 1,
+            "id": 51,
+            "name": "bowl"
+        },
+        {
+            "supercategory": "food",
+            "color": [
+                74,
+                65,
+                105
+            ],
+            "isthing": 1,
+            "id": 52,
+            "name": "banana"
+        },
+        {
+            "supercategory": "food",
+            "color": [
+                166,
+                196,
+                102
+            ],
+            "isthing": 1,
+            "id": 53,
+            "name": "apple"
+        },
+        {
+            "supercategory": "food",
+            "color": [
+                208,
+                195,
+                210
+            ],
+            "isthing": 1,
+            "id": 54,
+            "name": "sandwich"
+        },
+        {
+            "supercategory": "food",
+            "color": [
+                255,
+                109,
+                65
+            ],
+            "isthing": 1,
+            "id": 55,
+            "name": "orange"
+        },
+        {
+            "supercategory": "food",
+            "color": [
+                0,
+                143,
+                149
+            ],
+            "isthing": 1,
+            "id": 56,
+            "name": "broccoli"
+        },
+        {
+            "supercategory": "food",
+            "color": [
+                179,
+                0,
+                194
+            ],
+            "isthing": 1,
+            "id": 57,
+            "name": "carrot"
+        },
+        {
+            "supercategory": "food",
+            "color": [
+                209,
+                99,
+                106
+            ],
+            "isthing": 1,
+            "id": 58,
+            "name": "hot dog"
+        },
+        {
+            "supercategory": "food",
+            "color": [
+                5,
+                121,
+                0
+            ],
+            "isthing": 1,
+            "id": 59,
+            "name": "pizza"
+        },
+        {
+            "supercategory": "food",
+            "color": [
+                227,
+                255,
+                205
+            ],
+            "isthing": 1,
+            "id": 60,
+            "name": "donut"
+        },
+        {
+            "supercategory": "food",
+            "color": [
+                147,
+                186,
+                208
+            ],
+            "isthing": 1,
+            "id": 61,
+            "name": "cake"
+        },
+        {
+            "supercategory": "furniture",
+            "color": [
+                153,
+                69,
+                1
+            ],
+            "isthing": 1,
+            "id": 62,
+            "name": "chair"
+        },
+        {
+            "supercategory": "furniture",
+            "color": [
+                3,
+                95,
+                161
+            ],
+            "isthing": 1,
+            "id": 63,
+            "name": "couch"
+        },
+        {
+            "supercategory": "furniture",
+            "color": [
+                163,
+                255,
+                0
+            ],
+            "isthing": 1,
+            "id": 64,
+            "name": "potted plant"
+        },
+        {
+            "supercategory": "furniture",
+            "color": [
+                119,
+                0,
+                170
+            ],
+            "isthing": 1,
+            "id": 65,
+            "name": "bed"
+        },
+        {
+            "supercategory": "furniture",
+            "color": [
+                0,
+                182,
+                199
+            ],
+            "isthing": 1,
+            "id": 67,
+            "name": "dining table"
+        },
+        {
+            "supercategory": "furniture",
+            "color": [
+                0,
+                165,
+                120
+            ],
+            "isthing": 1,
+            "id": 70,
+            "name": "toilet"
+        },
+        {
+            "supercategory": "electronic",
+            "color": [
+                183,
+                130,
+                88
+            ],
+            "isthing": 1,
+            "id": 72,
+            "name": "tv"
+        },
+        {
+            "supercategory": "electronic",
+            "color": [
+                95,
+                32,
+                0
+            ],
+            "isthing": 1,
+            "id": 73,
+            "name": "laptop"
+        },
+        {
+            "supercategory": "electronic",
+            "color": [
+                130,
+                114,
+                135
+            ],
+            "isthing": 1,
+            "id": 74,
+            "name": "mouse"
+        },
+        {
+            "supercategory": "electronic",
+            "color": [
+                110,
+                129,
+                133
+            ],
+            "isthing": 1,
+            "id": 75,
+            "name": "remote"
+        },
+        {
+            "supercategory": "electronic",
+            "color": [
+                166,
+                74,
+                118
+            ],
+            "isthing": 1,
+            "id": 76,
+            "name": "keyboard"
+        },
+        {
+            "supercategory": "electronic",
+            "color": [
+                219,
+                142,
+                185
+            ],
+            "isthing": 1,
+            "id": 77,
+            "name": "cell phone"
+        },
+        {
+            "supercategory": "appliance",
+            "color": [
+                79,
+                210,
+                114
+            ],
+            "isthing": 1,
+            "id": 78,
+            "name": "microwave"
+        },
+        {
+            "supercategory": "appliance",
+            "color": [
+                178,
+                90,
+                62
+            ],
+            "isthing": 1,
+            "id": 79,
+            "name": "oven"
+        },
+        {
+            "supercategory": "appliance",
+            "color": [
+                65,
+                70,
+                15
+            ],
+            "isthing": 1,
+            "id": 80,
+            "name": "toaster"
+        },
+        {
+            "supercategory": "appliance",
+            "color": [
+                127,
+                167,
+                115
+            ],
+            "isthing": 1,
+            "id": 81,
+            "name": "sink"
+        },
+        {
+            "supercategory": "appliance",
+            "color": [
+                59,
+                105,
+                106
+            ],
+            "isthing": 1,
+            "id": 82,
+            "name": "refrigerator"
+        },
+        {
+            "supercategory": "indoor",
+            "color": [
+                142,
+                108,
+                45
+            ],
+            "isthing": 1,
+            "id": 84,
+            "name": "book"
+        },
+        {
+            "supercategory": "indoor",
+            "color": [
+                196,
+                172,
+                0
+            ],
+            "isthing": 1,
+            "id": 85,
+            "name": "clock"
+        },
+        {
+            "supercategory": "indoor",
+            "color": [
+                95,
+                54,
+                80
+            ],
+            "isthing": 1,
+            "id": 86,
+            "name": "vase"
+        },
+        {
+            "supercategory": "indoor",
+            "color": [
+                128,
+                76,
+                255
+            ],
+            "isthing": 1,
+            "id": 87,
+            "name": "scissors"
+        },
+        {
+            "supercategory": "indoor",
+            "color": [
+                201,
+                57,
+                1
+            ],
+            "isthing": 1,
+            "id": 88,
+            "name": "teddy bear"
+        },
+        {
+            "supercategory": "indoor",
+            "color": [
+                246,
+                0,
+                122
+            ],
+            "isthing": 1,
+            "id": 89,
+            "name": "hair drier"
+        },
+        {
+            "supercategory": "indoor",
+            "color": [
+                191,
+                162,
+                208
+            ],
+            "isthing": 1,
+            "id": 90,
+            "name": "toothbrush"
+        },
+        {
+            "supercategory": "textile",
+            "color": [
+                255,
+                255,
+                128
+            ],
+            "isthing": 0,
+            "id": 92,
+            "name": "banner"
+        },
+        {
+            "supercategory": "textile",
+            "color": [
+                147,
+                211,
+                203
+            ],
+            "isthing": 0,
+            "id": 93,
+            "name": "blanket"
+        },
+        {
+            "supercategory": "building",
+            "color": [
+                150,
+                100,
+                100
+            ],
+            "isthing": 0,
+            "id": 95,
+            "name": "bridge"
+        },
+        {
+            "supercategory": "raw-material",
+            "color": [
+                168,
+                171,
+                172
+            ],
+            "isthing": 0,
+            "id": 100,
+            "name": "cardboard"
+        },
+        {
+            "supercategory": "furniture-stuff",
+            "color": [
+                146,
+                112,
+                198
+            ],
+            "isthing": 0,
+            "id": 107,
+            "name": "counter"
+        },
+        {
+            "supercategory": "textile",
+            "color": [
+                210,
+                170,
+                100
+            ],
+            "isthing": 0,
+            "id": 109,
+            "name": "curtain"
+        },
+        {
+            "supercategory": "furniture-stuff",
+            "color": [
+                92,
+                136,
+                89
+            ],
+            "isthing": 0,
+            "id": 112,
+            "name": "door-stuff"
+        },
+        {
+            "supercategory": "floor",
+            "color": [
+                218,
+                88,
+                184
+            ],
+            "isthing": 0,
+            "id": 118,
+            "name": "floor-wood"
+        },
+        {
+            "supercategory": "plant",
+            "color": [
+                241,
+                129,
+                0
+            ],
+            "isthing": 0,
+            "id": 119,
+            "name": "flower"
+        },
+        {
+            "supercategory": "food-stuff",
+            "color": [
+                217,
+                17,
+                255
+            ],
+            "isthing": 0,
+            "id": 122,
+            "name": "fruit"
+        },
+        {
+            "supercategory": "ground",
+            "color": [
+                124,
+                74,
+                181
+            ],
+            "isthing": 0,
+            "id": 125,
+            "name": "gravel"
+        },
+        {
+            "supercategory": "building",
+            "color": [
+                70,
+                70,
+                70
+            ],
+            "isthing": 0,
+            "id": 128,
+            "name": "house"
+        },
+        {
+            "supercategory": "furniture-stuff",
+            "color": [
+                255,
+                228,
+                255
+            ],
+            "isthing": 0,
+            "id": 130,
+            "name": "light"
+        },
+        {
+            "supercategory": "furniture-stuff",
+            "color": [
+                154,
+                208,
+                0
+            ],
+            "isthing": 0,
+            "id": 133,
+            "name": "mirror-stuff"
+        },
+        {
+            "supercategory": "structural",
+            "color": [
+                193,
+                0,
+                92
+            ],
+            "isthing": 0,
+            "id": 138,
+            "name": "net"
+        },
+        {
+            "supercategory": "textile",
+            "color": [
+                76,
+                91,
+                113
+            ],
+            "isthing": 0,
+            "id": 141,
+            "name": "pillow"
+        },
+        {
+            "supercategory": "ground",
+            "color": [
+                255,
+                180,
+                195
+            ],
+            "isthing": 0,
+            "id": 144,
+            "name": "platform"
+        },
+        {
+            "supercategory": "ground",
+            "color": [
+                106,
+                154,
+                176
+            ],
+            "isthing": 0,
+            "id": 145,
+            "name": "playingfield"
+        },
+        {
+            "supercategory": "ground",
+            "color": [
+                230,
+                150,
+                140
+            ],
+            "isthing": 0,
+            "id": 147,
+            "name": "railroad"
+        },
+        {
+            "supercategory": "water",
+            "color": [
+                60,
+                143,
+                255
+            ],
+            "isthing": 0,
+            "id": 148,
+            "name": "river"
+        },
+        {
+            "supercategory": "ground",
+            "color": [
+                128,
+                64,
+                128
+            ],
+            "isthing": 0,
+            "id": 149,
+            "name": "road"
+        },
+        {
+            "supercategory": "building",
+            "color": [
+                92,
+                82,
+                55
+            ],
+            "isthing": 0,
+            "id": 151,
+            "name": "roof"
+        },
+        {
+            "supercategory": "ground",
+            "color": [
+                254,
+                212,
+                124
+            ],
+            "isthing": 0,
+            "id": 154,
+            "name": "sand"
+        },
+        {
+            "supercategory": "water",
+            "color": [
+                73,
+                77,
+                174
+            ],
+            "isthing": 0,
+            "id": 155,
+            "name": "sea"
+        },
+        {
+            "supercategory": "furniture-stuff",
+            "color": [
+                255,
+                160,
+                98
+            ],
+            "isthing": 0,
+            "id": 156,
+            "name": "shelf"
+        },
+        {
+            "supercategory": "ground",
+            "color": [
+                255,
+                255,
+                255
+            ],
+            "isthing": 0,
+            "id": 159,
+            "name": "snow"
+        },
+        {
+            "supercategory": "furniture-stuff",
+            "color": [
+                104,
+                84,
+                109
+            ],
+            "isthing": 0,
+            "id": 161,
+            "name": "stairs"
+        },
+        {
+            "supercategory": "building",
+            "color": [
+                169,
+                164,
+                131
+            ],
+            "isthing": 0,
+            "id": 166,
+            "name": "tent"
+        },
+        {
+            "supercategory": "textile",
+            "color": [
+                225,
+                199,
+                255
+            ],
+            "isthing": 0,
+            "id": 168,
+            "name": "towel"
+        },
+        {
+            "supercategory": "wall",
+            "color": [
+                137,
+                54,
+                74
+            ],
+            "isthing": 0,
+            "id": 171,
+            "name": "wall-brick"
+        },
+        {
+            "supercategory": "wall",
+            "color": [
+                135,
+                158,
+                223
+            ],
+            "isthing": 0,
+            "id": 175,
+            "name": "wall-stone"
+        },
+        {
+            "supercategory": "wall",
+            "color": [
+                7,
+                246,
+                231
+            ],
+            "isthing": 0,
+            "id": 176,
+            "name": "wall-tile"
+        },
+        {
+            "supercategory": "wall",
+            "color": [
+                107,
+                255,
+                200
+            ],
+            "isthing": 0,
+            "id": 177,
+            "name": "wall-wood"
+        },
+        {
+            "supercategory": "water",
+            "color": [
+                58,
+                41,
+                149
+            ],
+            "isthing": 0,
+            "id": 178,
+            "name": "water-other"
+        },
+        {
+            "supercategory": "window",
+            "color": [
+                183,
+                121,
+                142
+            ],
+            "isthing": 0,
+            "id": 180,
+            "name": "window-blind"
+        },
+        {
+            "supercategory": "window",
+            "color": [
+                255,
+                73,
+                97
+            ],
+            "isthing": 0,
+            "id": 181,
+            "name": "window-other"
+        },
+        {
+            "supercategory": "plant",
+            "color": [
+                107,
+                142,
+                35
+            ],
+            "isthing": 0,
+            "id": 184,
+            "name": "tree-merged"
+        },
+        {
+            "supercategory": "structural",
+            "color": [
+                190,
+                153,
+                153
+            ],
+            "isthing": 0,
+            "id": 185,
+            "name": "fence-merged"
+        },
+        {
+            "supercategory": "ceiling",
+            "color": [
+                146,
+                139,
+                141
+            ],
+            "isthing": 0,
+            "id": 186,
+            "name": "ceiling-merged"
+        },
+        {
+            "supercategory": "sky",
+            "color": [
+                70,
+                130,
+                180
+            ],
+            "isthing": 0,
+            "id": 187,
+            "name": "sky-other-merged"
+        },
+        {
+            "supercategory": "furniture-stuff",
+            "color": [
+                134,
+                199,
+                156
+            ],
+            "isthing": 0,
+            "id": 188,
+            "name": "cabinet-merged"
+        },
+        {
+            "supercategory": "furniture-stuff",
+            "color": [
+                209,
+                226,
+                140
+            ],
+            "isthing": 0,
+            "id": 189,
+            "name": "table-merged"
+        },
+        {
+            "supercategory": "floor",
+            "color": [
+                96,
+                36,
+                108
+            ],
+            "isthing": 0,
+            "id": 190,
+            "name": "floor-other-merged"
+        },
+        {
+            "supercategory": "ground",
+            "color": [
+                96,
+                96,
+                96
+            ],
+            "isthing": 0,
+            "id": 191,
+            "name": "pavement-merged"
+        },
+        {
+            "supercategory": "solid",
+            "color": [
+                64,
+                170,
+                64
+            ],
+            "isthing": 0,
+            "id": 192,
+            "name": "mountain-merged"
+        },
+        {
+            "supercategory": "plant",
+            "color": [
+                152,
+                251,
+                152
+            ],
+            "isthing": 0,
+            "id": 193,
+            "name": "grass-merged"
+        },
+        {
+            "supercategory": "ground",
+            "color": [
+                208,
+                229,
+                228
+            ],
+            "isthing": 0,
+            "id": 194,
+            "name": "dirt-merged"
+        },
+        {
+            "supercategory": "raw-material",
+            "color": [
+                206,
+                186,
+                171
+            ],
+            "isthing": 0,
+            "id": 195,
+            "name": "paper-merged"
+        },
+        {
+            "supercategory": "food-stuff",
+            "color": [
+                152,
+                161,
+                64
+            ],
+            "isthing": 0,
+            "id": 196,
+            "name": "food-other-merged"
+        },
+        {
+            "supercategory": "building",
+            "color": [
+                116,
+                112,
+                0
+            ],
+            "isthing": 0,
+            "id": 197,
+            "name": "building-other-merged"
+        },
+        {
+            "supercategory": "solid",
+            "color": [
+                0,
+                114,
+                143
+            ],
+            "isthing": 0,
+            "id": 198,
+            "name": "rock-merged"
+        },
+        {
+            "supercategory": "wall",
+            "color": [
+                102,
+                102,
+                156
+            ],
+            "isthing": 0,
+            "id": 199,
+            "name": "wall-other-merged"
+        },
+        {
+            "supercategory": "textile",
+            "color": [
+                250,
+                141,
+                255
+            ],
+            "isthing": 0,
+            "id": 200,
+            "name": "rug-merged"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 9881f00..f539602 100644
--- a/setup.py
+++ b/setup.py
@@ -1,15 +1,33 @@
-#-*- coding: utf-8 -*-
+# -*- coding: utf-8 -*-
+import os
+from pathlib import Path
+from setuptools import setup
 
 
-from setuptools import setup, Extension
+def read(file_name):
+    with open(os.path.join(Path(os.path.dirname(__file__)), file_name))\
+            as _file:
+        return _file.read()
+
+
+long_description = read('README.md')
 
 setup(
     name='panopticapi',
     packages=['panopticapi'],
-    package_dir = {'panopticapi': 'panopticapi'},
+    package_dir={'panopticapi': 'panopticapi'},
     install_requires=[
+        'scikit_image',
         'numpy',
+        'matplotlib',
+        'cityscapesscripts',
         'Pillow',
+        'pycocotools',
+        'skimage'
     ],
+    long_description=long_description,
+    long_description_content_type='text/markdown',
     version='0.1',
+    url='https://github.com/cocodataset/panopticapi',
+    download_url='https://github.com/cocodataset/panopticapi',
 )
diff --git a/visualization.py b/visualization.py
index 863f8d8..d79d45c 100644
--- a/visualization.py
+++ b/visualization.py
@@ -10,7 +10,7 @@
 from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
-import os, sys
+import os
 import numpy as np
 import json