Table 3.

Training hyperparameters used for the YOLO11x-cls model in the kidney ultrasound frame classification task. The model was trained using AdamW optimizer with a warm-up strategy and momentum to ensure stable convergence and generalization across varying patient data. This table outlines the critical hyperparameters that guided the training of YOLO11x-cls. These values were chosen based on empirical best practices and were kept consistent with the official implementation to ensure reproducibility. The combination of a conservative learning rate, momentum stabilization, and weight decay was essential for optimizing model performance in a medical imaging context where overfitting is a common risk.

Parameter	Value
model	YOLO11x-cls
Input image size	224x224
lr0	0.001
lrf	0.001
Optimizer	AdamW
Momentum value	0.937
Weight decay	0.0005
Warmup epochs	3.0
Warmup momentum	0.8