  We present a holistic approach for high resolution image classification that
won second place in the ICCV/CVPPA2023 Deep Nutrient Deficiency Challenge. The
approach consists of a full pipeline of: 1) data distribution analysis to check
potential domain shift, 2) backbone selection for a strong baseline model that
scales up for high resolution input, 3) transfer learning that utilizes
published pretrained models and continuous fine-tuning on small sub-datasets,
4) data augmentation for the diversity of training data and to prevent
overfitting, 5) test-time augmentation to improve the prediction's robustness,
and 6) "data soups" that conducts cross-fold model prediction average for
smoothened final test results.
