<p><strong>SSD Multibox</strong> (short for Single Shot Multibox Detector) is a neural network that can detect and locate objects in an image in a single forward pass. The network is trained in a supervised manner on a dataset of images where a bounding box and a class label is given for each object of interest. The loss term</p>

<pre><code>multibox_loss = confidence_loss + alpha * location_loss
</code></pre>

<p>is made up of two parts: </p>

<p><strong>Confidence loss</strong> is a categorical cross-entropy loss for classifying the detected objects. The purpose of this term is to make sure that correct label is assigned to each detected object.</p>

<p><strong>Location loss</strong> is a regression loss (either the smooth L1 or the L2 loss) on the parameters (width, height and corner offset) of the detected bounding box. The purpose of this term is to make sure that the correct region of the image is identified for the detected objects. The <strong>alpha</strong> term is a hyper parameter used to scale the location loss.</p>

<p>The precise formulation of the loss is given in Equation 1 of the <a href="https://arxiv.org/abs/1512.02325" rel="nofollow noreferrer">SSD: Single Shot MultiBox Detector</a> paper.</p>
Can someone give me an explanation for Multibox loss function?
<p>I have found some expression for SSD Multibox-loss function as follows:</p>

<p><strong>multibox_loss = confidence_loss + alpha * location_loss</strong></p>

<p>Can someone explains what are the explanations for those terms?</p>