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arXiv · 2309.15277

Boosting High Resolution Image Classification with Scaling-up Transformers

Yi Wang
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Abstract

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.

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Yi Wang. 2023-10-13. Boosting High Resolution Image Classification with Scaling-up Transformers. https://arxiv.org/abs/2309.15277

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