Köleş, SelinKarakaş, SelamiNdigande, Alain PatrickÖzer, Sedat2024-01-232024-01-232023979-835030396-4http://hdl.handle.net/10679/9062https://doi.org/10.1109/TSP59544.2023.10197832In this paper, we study and analyze the performance of various loss functions on a recently proposed real-time instance segmentation algorithm, YOLACT++. In particular, we study the loss functions, including Huber Loss, Binary Cross Entropy (BCE), Mean Square Error (MSE), Log-Cosh-Dice Loss, and their various combinations within the YOLACT++ architecture. We demonstrate that we can use different loss functions from the default loss function (BCE) of YOLACT++ for improved real-time segmentation results. In our experiments, we show that a certain combination of two loss functions improves the segmentation performance of YOLACT++ in terms of the mean Average Precision (mAP) metric on Cigarettes dataset, when compared to its original loss function.engrestrictedAccessconferenceObject26426710.1109/TSP59544.2023.10197832Instance segmentationLoss functionReal time segmentationYOLACT++2-s2.0-85168650663