  Knowledge Distillation (KD) is one proposed solution to large model sizes and
slow inference speed in semantic segmentation. In our research we identify 25
proposed distillation loss terms from 14 publications in the last 4 years.
Unfortunately, a comparison of terms based on published results is often
impossible, because of differences in training configurations. A good
illustration of this problem is the comparison of two publications from 2022.
Using the same models and dataset, Structural and Statistical Texture
Distillation (SSTKD) reports an increase of student mIoU of 4.54 and a final
performance of 29.19, while Adaptive Perspective Distillation (APD) only
improves student performance by 2.06 percentage points, but achieves a final
performance of 39.25. The reason for such extreme differences is often a
suboptimal choice of hyperparameters and a resulting underperformance of the
student model used as reference point. In our work, we reveal problems of
insufficient hyperparameter tuning by showing that distillation improvements of
two widely accepted frameworks, SKD and IFVD, vanish when hyperparameters are
optimized sufficiently. To improve comparability of future research in the
field, we establish a solid baseline for three datasets and two student models
and provide extensive information on hyperparameter tuning. We find that only
two out of eight techniques can compete with our simple baseline on the ADE20K
dataset.
