Comparing MobileNet Models in TensorFlow

In recent years, neural networks and deep learning have sparked tremendous progress in the field of natural language processing (NLP) and computer vision. While many of the face, object, landmark, logo, and text recognition and detection technologies are provided for Internet-connected devices, we believe that the ever-increasing computational power of mobile devices can enable the delivery of these technologies into the hands of users anytime, anywhere, regardless of Internet connection. However, computer vision for on-device and embedded applications faces many challenges -- models must run quickly with high accuracy in a resource-constrained environment, making use of limited computation, power, and space. TensorFlow offers various pre-trained models, such as drag-and-drop models, in order to identify approximately 1,000 default objects. When compared with other similar models, such as the Inception model datasets, MobileNet works better with latency, size, and accuracy.