diff --git a/label_maker/images.py b/label_maker/images.py
index 44b94cb..48dff49 100644
--- a/label_maker/images.py
+++ b/label_maker/images.py
@@ -1,6 +1,7 @@
 # pylint: disable=unused-argument
 """Generate an .npz file containing arrays for training machine learning algorithms"""
 
+import pickle
 from os import makedirs, path as op
 from random import shuffle
 
@@ -32,8 +33,10 @@ def download_images(dest_folder, classes, imagery, ml_type, background_ratio, **
         Other properties from CLI config passed as keywords to other utility functions
     """
     # open labels file
-    labels_file = op.join(dest_folder, 'labels.npz')
-    tiles = np.load(labels_file)
+    #labels_file = op.join(dest_folder, 'labels.npz')
+    #tiles = np.load(labels_file)
+    with open(op.join(dest_folder, 'labels.pkl'), 'rb') as f:
+        tiles = pickle.load(f)
 
     # create tiles directory
     tiles_dir = op.join(dest_folder, 'tiles')
@@ -50,14 +53,14 @@ def class_test(value):
         elif ml_type == 'classification':
             return value[0] == 0
         return None
-    class_tiles = [tile for tile in tiles.files if class_test(tiles[tile])]
 
+    class_tiles = [key for key, tile in tiles.items() if class_test(tile)]
     # for classification problems with a single class, we also get background
     # tiles up to len(class_tiles) * config.get('background_ratio')
     background_tiles = []
     limit = len(class_tiles) * background_ratio
     if ml_type == 'classification' and len(classes) == 1:
-        background_tiles_full = [tile for tile in tiles.files if tile not in class_tiles]
+        background_tiles_full = [tile for tile in tiles if tile not in class_tiles]
         shuffle(background_tiles_full)
         background_tiles = background_tiles_full[:limit]
 
diff --git a/label_maker/label.py b/label_maker/label.py
index d0edd4e..f9d5d89 100644
--- a/label_maker/label.py
+++ b/label_maker/label.py
@@ -5,6 +5,7 @@
 from subprocess import run, Popen, PIPE
 import json
 from functools import partial
+import pickle
 
 import numpy as np
 import mapbox_vector_tile
@@ -106,9 +107,11 @@ def make_labels(dest_folder, zoom, country, classes, ml_type, bounding_box, spar
         print('Using sparse mode; subselected {} background tiles'.format(n_neg_ex))
 
     # write out labels as numpy arrays
-    labels_file = op.join(dest_folder, 'labels.npz')
+    labels_file = op.join(dest_folder, 'labels.pkl')
     print('Writing out labels to {}'.format(labels_file))
-    np.savez(labels_file, **tile_results)
+    #np.savez(labels_file, **tile_results)
+    with open(labels_file, 'wb') as f:
+        pickle.dump(tile_results, f)
 
     # write out labels as GeoJSON or PNG
     if ml_type == 'classification':
diff --git a/label_maker/package.py b/label_maker/package.py
index 5be29d2..06c6f48 100644
--- a/label_maker/package.py
+++ b/label_maker/package.py
@@ -1,6 +1,7 @@
 # pylint: disable=unused-argument
 """Generate an .npz file containing arrays for training machine learning algorithms"""
 
+import pickle
 from os import path as op
 from urllib.parse import urlparse
 import numpy as np
@@ -35,9 +36,11 @@ def package_directory(dest_folder, classes, imagery, ml_type, seed=False, train_
         np.random.seed(seed)
 
     # open labels file, create tile array
-    labels_file = op.join(dest_folder, 'labels.npz')
-    labels = np.load(labels_file)
-    tile_names = [tile for tile in labels.files]
+    labels_file = op.join(dest_folder, 'labels.pkl')
+    #labels = np.load(labels_file)
+    with open(labels_file, 'rb') as f:
+        labels = pickle.load(f)
+    tile_names = [tile for tile in labels]
     tile_names.sort()
     tiles = np.array(tile_names)
     np.random.shuffle(tiles)
@@ -45,7 +48,7 @@ def package_directory(dest_folder, classes, imagery, ml_type, seed=False, train_
     # find maximum number of features in advance so numpy shapes match
     if ml_type == 'object-detection':
         max_features = 0
-        for tile in labels.files:
+        for tile in labels:
             features = len(labels[tile])
             if features > max_features:
                 max_features = features
diff --git a/label_maker/preview.py b/label_maker/preview.py
index c73a5b0..9a3b9b7 100644
--- a/label_maker/preview.py
+++ b/label_maker/preview.py
@@ -1,6 +1,7 @@
 # pylint: disable=unused-argument
 """Produce imagery examples for specified classes"""
 
+import pickle
 from os import path as op
 from os import makedirs
 from urllib.parse import urlparse
@@ -35,8 +36,10 @@ def preview(dest_folder, number, classes, imagery, ml_type, **kwargs):
         Other properties from CLI config passed as keywords to other utility functions
     """
     # open labels file
-    labels_file = op.join(dest_folder, 'labels.npz')
-    tiles = np.load(labels_file)
+    labels_file = op.join(dest_folder, 'labels.pkl')
+    #tiles = np.load(labels_file)
+    with open(labels_file, 'rb') as f:
+        tiles = pickle.load(f)
 
     # create example tiles directory
     examples_dir = op.join(dest_folder, 'examples')
@@ -53,8 +56,7 @@ def preview(dest_folder, number, classes, imagery, ml_type, **kwargs):
         if not op.isdir(class_dir):
             makedirs(class_dir)
 
-        class_tiles = (t for t in tiles.files
-                       if class_match(ml_type, tiles[t], i + 1))
+        class_tiles = (t for t in tiles if class_match(ml_type, tiles[t], i + 1))
         print('Downloading at most {} tiles for class {}'.format(number, cl.get('name')))
         for n, tile in enumerate(class_tiles):
             if n > number: