ViT Model

Vision Transformers (ViTs) are a class of deep learning models that leverage the self-attention mechanism, initially popularized in natural language processing, to analyze image data. Current research focuses on improving ViT efficiency for deployment on resource-constrained devices through techniques like quantization-aware architecture search, pruning (both weight and token), and novel training strategies such as masked autoencoding. These efforts aim to address the computational demands of ViTs while maintaining high accuracy, leading to applications in diverse fields including medical image analysis, autonomous driving, and security. The resulting advancements are significant for both advancing fundamental understanding of deep learning and enabling practical applications in various domains.