  Orientation recognition and standardization play a crucial role in the
effectiveness of medical image processing tasks. Deep learning-based methods
have proven highly advantageous in orientation recognition and prediction
tasks. In this paper, we address the challenge of imaging orientation in
cardiac MRI and present a method that employs deep neural networks to
categorize and standardize the orientation. To cater to multiple sequences and
modalities of MRI, we propose a transfer learning strategy, enabling adaptation
of our model from a single modality to diverse modalities. We conducted
comprehensive experiments on CMR images from various modalities, including
bSSFP, T2, and LGE. The validation accuracies achieved were 100.0\%, 100.0\%,
and 99.4\%, confirming the robustness and effectiveness of our model. Our
source code and network models are available at
https://github.com/rxzhen/MSCMR-orient
