diff --git a/Using-a-TensorFlow-Pretrained-Model-to-Detect-Everyday-Objects.md b/Using-a-TensorFlow-Pretrained-Model-to-Detect-Everyday-Objects.md
index 7c3a869..4fb1c76 100644
--- a/Using-a-TensorFlow-Pretrained-Model-to-Detect-Everyday-Objects.md
+++ b/Using-a-TensorFlow-Pretrained-Model-to-Detect-Everyday-Objects.md
@@ -10,7 +10,7 @@ The custom inference model must be in the form of a TensorFlow Lite (.tflite) fi
 
 The model and its corresponding label map can be downloaded from [this link](http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip).  
 
-The .zip archive contains a file called "detect.tflite".  This TensorFlow Lite file is the inference model that TensorFlow will use to recognize the  everyday objects.  It is based on the mobilenet architecture, which was designed to provide low latency recognitions, while still maintaining reasonable recognition accuracy.
+The .zip archive contains a file called "detect.tflite".  This TensorFlow Lite file is the inference model that TensorFlow will use to recognize the  everyday objects.  It is based on the MobileNet neural network architecture, which was designed to provide low latency recognitions, while still maintaining reasonable recognition accuracy.
 
 The .zip archive also contains a text file called "labelmap.txt".  This text file contains a list of labels that correspond to the known objects in the "detect.tflite" model file.