<p>You could try using Focal Loss.</p>

<p>See: <a href="https://arxiv.org/pdf/1708.02002.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1708.02002.pdf</a></p>

<p>In the Tensorflow Object Detection model file this would appear as follows:</p>

<pre><code>loss {
    localization_loss {
        weighted_smooth_l1 {
        }
    }
    classification_loss {
        weighted_sigmoid_focal {
            gamma: 2.0
            alpha: 0.25
        }
    }
    classification_weight: 1.0
    localization_weight: 1.0
}
</code></pre>
Imbalanced dataset Object Detection
<p>Is there a good way to fine tune a model for object detection (in particular, I am trying to use the Tensorflow Object Detection API) for a dataset with highly skewed data? I am trying to use take some categories of COCO and combine it with my own custom data, but there are only about 50 images of my data.</p>

<p>I have tried with just combining the coco data and my own data but it just predicts the coco categories everytime. </p>