diff --git a/sbb_binarize/sbb_binarize.py b/sbb_binarize/sbb_binarize.py
index a7a5dd3..c49849c 100644
--- a/sbb_binarize/sbb_binarize.py
+++ b/sbb_binarize/sbb_binarize.py
@@ -44,7 +44,7 @@ class SbbBinarizer:
         padded_image = np.zeros((padded_image_height, padded_image_width, image_channels))
         padded_image[0:original_image_height, 0:original_image_width, :] = img[:, :, :]
 
-        image_batch = np.expand_dims(padded_image, 0)  # To create the batch information
+        image_batch = np.expand_dims(padded_image, 0)  # Create the batch dimension
         patches = tf.image.extract_patches(
             images=image_batch,
             sizes=[1, self.model_height, self.model_width, 1],
@@ -117,6 +117,7 @@ def split_list_into_worker_batches(files: List[Any], number_of_workers: int) ->
 def batch_predict(input_data):
     model_dir, input_images, output_images, worker_number = input_data
     print(f"Setting visible cuda devices to {str(worker_number)}")
+    # Each worker thread will be assigned only one of the available GPUs to allow multiprocessing across GPUs
     os.environ["CUDA_VISIBLE_DEVICES"] = str(worker_number)
 
     binarizer = SbbBinarizer()
@@ -146,13 +147,14 @@ if __name__ == '__main__':
         output_images = [output_path / (i.relative_to(input_path)) for i in input_images]
         input_images = [i for i in input_images]
 
-        print(f"Starting binarization of {len(input_images)} images")
+        print(f"Starting batch-binarization of {len(input_images)} images")
 
         number_of_gpus = len(tf.config.list_physical_devices('GPU'))
         number_of_workers = max(1, number_of_gpus)
         image_batches = split_list_into_worker_batches(input_images, number_of_workers)
         output_batches = split_list_into_worker_batches(output_images, number_of_workers)
 
+        # Must use spawn to create completely new process that has its own resources to properly multiprocess across GPUs
         with WorkerPool(n_jobs=number_of_workers, start_method='spawn') as pool:
             model_dirs = itertools.repeat(model_directory, len(image_batches))
             input_data = zip(model_dirs, image_batches, output_batches, range(number_of_workers))