<p>Dataset disbalance always causes performance decrease. Though, there are a few tricks, which may be helpful in your situation:</p>
<ol>
<li>The simplest one - class weight. May be computed by sklearn's <code>compute_class_weight</code> method.</li>
<li>Quite modern approach - Focal loss (<a href="https://arxiv.org/abs/1708.02002" rel="nofollow noreferrer">https://arxiv.org/abs/1708.02002</a>). Roughly, this loss function drives more NN's attention for 'hard-detected' objects (simply by increasing the loss on them), which includes imbalanced classes.</li>
</ol>
<p>Your low confidence problem may be one of the underfitting consequences. Thats from personal experience with two-stage detectors (mostly Faster-RCNN)</p>
Using a unbalanced dataset in YOLO
<p>I was wondering if using unbalanced dataset with YOLO would cause it to train worse in terms of accuracy? Would the classes with less images have less accuracy?</p>
<p>I have 3 classes with 14.4 k images</p>
<p>1 class has 12,000 image examples
the other 2 have 1,000 image examples each</p>
<p>would this be an issue?</p>
<p>I am training on YOLOR right now and my MAP is at 0.36 on my custom dataset</p>
<p>I classified with the weights and the classification is good but I need to set the confidence very  low as the classes with less images have a very low confidence (0.05 - 0.12) while the class with 12,000 images has confidence (0.45 - 0.90</p>