This figure presents the architecture of our ViT model. Input images are divided into patches, embedded, and subsequently augmented with positional encodings. The encoder processes these embeddings using self-attention mechanisms and a multilayer perceptron (MLP). The final output is a parameter likelihood vector generated via a softmax layer.