How to handle dataset that has missing objects?

[mazatov]

I am seeking advice on the optimal approach for training an object detector using a dataset that may have missing objects.

Let's say my dataset, consists of images of cats and dogs. Approximately 33% have both cats and dogs labeled, 33% have only dogs labeled, and 33% have only cats labeled. Is there a strategy to utilize the 66% of the data where only one animal is labeled in order to enhance the overall accuracy of the detector? I am uncertain about how YOLO penalizes missing objects and would appreciate guidance on the best way to address this situation.

[KMoszczyc]

You probably don't want images with missing labels. You can try both and keep for test set only fully labeled images.