# SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition
## 3 Stacking of Distilled Vision Transformers (SDViT)
### Stacking
Figure 13: Misclassified image samples from the benchmark datasets. Figure 16: Confusion matrix of the proposed SDViT method on the NUS hand gesture dataset. Figure 15: Confusion matrix of the proposed SDViT method on the ASL with digits dataset. ering the average accuracy across all three datasets, the proposed method demonstrated an impressive accuracy of 99.87%, outperforming the SOTA approaches under comparison. ## 5 Conclusions

In conclusion, this paper proposes stacking of distilled vision transformers, referred to as SDViT, for hand gesture recognition. The proposed SDViT encompasses several key components that contribute to its effectiveness. Firstly, transfer learning and fine-tuning of pretrained ViTs are employed, leveraging their superior performance and computational efficiency compared with SOTA CNNs. By incorporating early stopping and reduce learning rate on plateau techniques, the pretrained ViTs are fine-tuned to achieve optimal results. Additionally, knowledge distillation techniques are applied using the pretrained ViTs as both teacher and student models. This process not only enhances the performance of the models but also reduces their overall size, resulting in reduced computational resource requirements. Furthermore, the knowledge distilled student models are ensembled using a stacking approach. This approach combines the predictions of multiple stacked models, effectively reducing bias and enhancing model stability and accuracy. While the proposed SDViT model demonstrates remarkable performance in hand gesture recognition, it is essential to acknowledge certain limitations and identify potential areas for improvement. One notable aspect is the reliance on distilled student models which, despite their computational efficiency, might not capture all of the nuanced features present in the original ViT. Additionally, the generalization of the model across diverse datasets and real-world scenarios warrants further investigation. The sensitivity of the model to variations in lighting conditions, occlusions, or dynamic backgrounds could be a potential challenge that needs attention. Furthermore, the ensemble learning approach, while effective, introduces additional computational overhead, and optimizing the ensemble size and configuration could be explored for efficiency. These considerations pave the way for future research to refine and extend the capabilities of the SDViT model in addressing real-world challenges and diverse application scenarios. The techniques and insights from SDViT, which include transfer learning, knowledge distillation, and ensemble learning, present a versatile toolkit with applications extending beyond hand gesture recognition. One notable application is in the realm of object recognition, where SDViT's transfer learning facilitates swift adaptation to new datasets, leveraging pretrained ViTs for robust feature extraction and hierarchical representations. The knowledge distillation process ensures the creation of compact yet accurate models that are suitable for resource-constrained environments. The ensemble learning aspect contributes to the model's robustness, enhancing accuracy and generalization by aggregating diverse perspectives from multiple distilled models. Additionally, in medical image analysis, SDViT's ability to capture intricate patterns and relationships proves valuable. Transfer learning aids in scenarios with limited labeled medical datasets, while knowledge distillation creates more efficient models without sacrificing diagnostic capabilities. The ensemble learning approach ensures reliability across diverse patient profiles and imaging conditions. These examples illustrate how SDViT's methodologies adapt to specific com