<p>There are two option to approach the problem:</p>

<p>1.) You consider the problem as a two-class classification task. Then you train a classifier with samples from the class (images showing a mouse), and samples where the images do not depict a mouse. This approach works in general. To this end, you can still train an SVM, or you train a neural network. If you have only few samples, using SVM or tranfer learning with neural networks works good in most cases. Note that for applying a SVM, you have to define discriminative features (like HOG).</p>

<p>A question that arises with this approach is which images to select for the not-mouse class. </p>

<p>2.) You consider the problem as a one-class classification task. In this case, you only need samples of your desired class and the machine learning model is trained to recognize deviations from the class.</p>

<p>There a classical approaches like one-class SVM, where you train only on positive samples by fitting a hypersphere around the feature vectors of your class.</p>

<p>If you want to apply neural networks, you could use for example DROCC (<a href="https://arxiv.org/abs/2002.12718" rel="nofollow noreferrer">https://arxiv.org/abs/2002.12718</a>)</p>
How to determine if image is something or not?
<p>I have a trained model that determines if an image contains either cat or dog. I'm using SVM to classify. I want a new model that determines if an image is a mouse or not. It's different from the first model that classifies into 2 classes. This new model will return TRUE or FALSE only.</p>
<p>I don't think I can use a classifier model since I have only 1 class; the mouse. I don't intend to use the first model or anything related to it in the 2nd model.</p>
<p>What is the best way to approach this?</p>