Jiawei

We developed a state-of-the-art one-stage detection model as the baseline framework for the competition, with the goal of accurately detecting small objects. As a result, we achieved a top 5 ranking in the competition with a public score of 0.7394 and a private score of 0.7550.

We trained EfficientNet and Swin Transformer model, incorporating auto-augmentation techniques to enhance dataset diversity. Applied TTA and ensemble strategies, achieving a public ranking of 9th and a private ranking of 8th, with scores of 0.9329 and 0.9344, respectively.

We developed a UNet-based model with a diverse backbone, addressing the imbalance of foreground and background through a combination of DiceLoss and Focal Loss. Our post-processing strategies improved accuracy, resulting in a 3rd place public ranking and 16th place private ranking, with scores of 0.9194 and 0.9109 respectively.

We developed a few-shot learning approach to accurately distinguish visually similar orchid species. Using various models, data augmentations, and optimization techniques, we ensembled top predictions, achieving a Macro-F1 score of 0.9115 (public) and 0.8096 (private), ranking 15th out of 743 teams.

We proposed a lightweight semantic segmentation model optimized for embedded systems and traffic scene analysis in Asian regions. The model balances accuracy, low power consumption, and real-time performance, designed for MediaTek's Dimensity Series platform. Achieved a public test score of 0.5624.

We propose a deep learning method for ultrasound nerve segmentation using UNet with an EfficientNet backbone. Two novel techniques, Erosion Mask Smoothing (ELS) and adaptive Single Model Ensemble (ASME), improved performance. Achieved a private test score of 0.7234, surpassing the baseline.