What is the method for converting MXNet models?

There are two methods for converting MXNet models.

1. Export models using the export function in MXNet to the ONNX format, which stands for Open Neural Network Exchange. ONNX is an open method for representing deep learning models, allowing for cross-platform deployment and conversion. Once exported to ONNX format, the model can be loaded and executed using frameworks such as ONNX Runtime.
2. The export function in the gluoncv toolkit of MXNet can be used to export models into formats supported by other frameworks such as Caffe and TensorFlow. Gluoncv offers pre-trained models that can be converted into formats supported by other frameworks, making it easier to use the models across different frameworks. For example, the gluoncv.utils.export_block function can be used to export MXNet models into Caffe format.