  Our research focuses on the analysis and improvement of the Graph-based
Relation Inference Transformer (GRIT), which serves as an important benchmark
in the field. We conduct a comprehensive ablation study using the PISC-fine
dataset, to find and explore improvement in efficiency and performance of
GRITv2. Our research has provided a new state-of-the-art relation recognition
model on the PISC relation dataset. We introduce several features in the GRIT
model and analyse our new benchmarks in two versions: GRITv2-L (large) and
GRITv2-S (small). Our proposed GRITv2-L surpasses existing methods on relation
recognition and the GRITv2-S is within 2% performance gap of GRITv2-L, which
has only 0.0625x the model size and parameters of GRITv2-L. Furthermore, we
also address the need for model compression, an area crucial for deploying
efficient models on resource-constrained platforms. By applying quantization
techniques, we efficiently reduced the GRITv2-S size to 22MB and deployed it on
the flagship OnePlus 12 mobile which still surpasses the PISC-fine benchmarks
in performance, highlighting the practical viability and improved efficiency of
our model on mobile devices.
