  This report outlines our approach in the IEEE SPS VIP Cup 2023: Ophthalmic
Biomarker Detection competition. Our primary objective in this competition was
to identify biomarkers from Optical Coherence Tomography (OCT) images obtained
from a diverse range of patients. Using robust augmentations and 5-fold
cross-validation, we trained two vision transformer-based models: MaxViT and
EVA-02, and ensembled them at inference time. We find MaxViT's use of
convolution layers followed by strided attention to be better suited for the
detection of local features while EVA-02's use of normal attention mechanism
and knowledge distillation is better for detecting global features. Ours was
the best-performing solution in the competition, achieving a patient-wise F1
score of 0.814 in the first phase and 0.8527 in the second and final phase of
VIP Cup 2023, scoring 3.8% higher than the next-best solution.
