Transformers Outperform ConvNets for Root Segmentation: A Systematic Comparison Across Nine Datasets
This study systematically compares Transformer and ConvNet architectures across nine root segmentation datasets, revealing that Transformer-based models, particularly when pre-trained, significantly outperform ConvNets in accuracy and domain transfer, although dataset choice ultimately explains far more performance variance than model architecture.