What is MobileNet?

MobileNet is a type of convolutional neural network designed for mobile and embedded vision applications. They are based on a streamlined architecture that uses depthwise separable convolutions to build lightweight deep neural networks that can have low latency for mobile and embedded devices.

Why is MobileNet used?

Description. MobileNet-v2 is a convolutional neural network that is 53 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database [1]. You can use classify to classify new images using the MobileNet-v2 model.

Is MobileNet a deep learning model?

The MobileNet was proposed as a deep learning model by Andrew G. Howard et al of Google Research team in their research work entitled “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”.

Who created MobileNet?

Google
In 2017, Google introduced MobileNets, a family of computer vision models based on TensorFlow. The original paper was followed by the release of MobileNetV2 in April 2018 and MobileNetV3 in May 2019 . Today, we explore the evolution of this important architecture in the field of computer vision.

What is NASNet?

NASNet stands for Neural Search Architecture (NAS) Network and is a Machine Learning model. The key principles are different from standard models like GoogleNet and is likely to bring a major breakthrough in AI soon.

What is MobileNet ML?

MobileNets are a family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application.

What is the size of MobileNet?

MobileNets support any input size greater than 32 x 32, with larger image sizes offering better performance.

How does MobileNet model work?

The job of the MobileNet layers is to convert the pixels from the input image into features that describe the contents of the image, and pass these along to the other layers. Hence, MobileNet is used here as a feature extractor for a second neural network.

What can MobileNet classify?

MobileNets are a class of small, low-latency, low-power models that can be used for classification, detection, and other common tasks convolutional neural networks are good for. Because of their small size, these are considered great deep learning models to be used on mobile devices.

Is MobileNet fully convolutional?

The MobileNet structure is built on depthwise separable convolutions as mentioned in the previous section except for the first layer which is a full convolution. By defining the network in such simple terms we are able to easily explore network topologies to find a good network.

What is a bottleneck layer?

A bottleneck layer is a layer that contains few nodes compared to the previous layers. It can be used to obtain a representation of the input with reduced dimensionality. An example of this is the use of autoencoders with bottleneck layers for nonlinear dimensionality reduction.

Is NASNet a CNN?

Equipped with abundance of computing power and engineering genius, Google introduced NASNet, which framed the problem of finding the best CNN architecture as a Reinforcement Learning problem.