Siam network is a deep learning model, which is used for similarity comparison tasks, and is often used for face recognition, signature verification and other tasks.

Accuracy is one of the indexes to evaluate the performance of the model, which indicates the correct proportion of the model in the prediction. For tasks using the dual-network model, accuracy can be understood as that the model predicts the correct ratio when comparing the similarity between two inputs.

The calculation method of accuracy is to divide the number of samples correctly predicted by the model by the total number of samples. For example, if there are 100 samples and the model correctly predicts the similarity of 90 samples, the accuracy rate is 90%.

It should be noted that the accuracy of the dual-network model will be affected by many factors, including the structure of the model, the quality and quantity of training data, the setting of super parameters in the training process and so on. Therefore, the accuracy of the specific model needs to be evaluated and verified according to the specific application scenarios and data sets.