<p><strong>SSD</strong> - single shot detector - is a NN architecture designed for detection purposes - which means localization(bounding boxes) and classification at once.</p>

<p><strong>Mobilenet</strong>- (<a href="https://arxiv.org/abs/1704.04861" rel="noreferrer">https://arxiv.org/abs/1704.04861</a>) - efficient architecture introduced by Google (using depthwise and pointwise convolutions). It can be used for classification purposes, or as a feature extractor for other (i.e. detection).</p>

<p>In the SSD paper they present the use of VGG NN as the <strong>feature extractor</strong> for the detection, the features maps are being taken from several different layers (resolutions) and being fed to their corresponding classification and localization  layers (Classification head and Regression head).</p>

<p>So actually, one can decide to use a different kind of feature extractor - like MobileNet-SSD - which means you use SSD arch. while your feature extractor is mobilenet arch.</p>

<p>By reading the SSD paper, and the mobilenet paper you would be able to understand the model exist in the model zoo.</p>
Difference between SSD and Mobilenet
<p>I am confusing between SSD and mobilenet. As far as I know, both of them are neural network. SSD provides localization while mobilenet provides classification. Thus the combination of SSD and mobilenet can produce the object detection. The image is taken from <a href="https://arxiv.org/abs/1512.02325" rel="noreferrer">SSD paper</a>. The default classification network of SSD is VGG-16. So, for SSD Mobilenet, VGG-16 is replaced with mobilenet. Are my statements correct?</p>

<p>Where can I get more information about SSD Mobilenet especially that one available on Tensorflow model zoo?</p>

<p><a href="https://i.stack.imgur.com/g30dc.png" rel="noreferrer"><img src="https://i.stack.imgur.com/g30dc.png" alt="enter image description here"></a></p>