Depth Anything V2 PyTorch Code Generation with MATLAB Coder

Learn how to use MATLAB Coder™ and GPU Coder™ to generate optimized C/C++ and CUDA^® code for the Depth Anything V2 PyTorch^® model. Depth Anything V2 is a state-of-the-art monocular depth estimation model that predicts dense depth maps from a single RGB image.

Begin the workflow by loading the pretrained PyTorch model into MATLAB^®. You then run and test the model in MATLAB and Simulink^® to verify that system-level simulations produce results consistent with the original PyTorch model. Once you have validated the results, use MATLAB Coder to generate standalone C/C++ code for CPU deployment, GPU Coder to generate CUDA code for NVIDIA^® GPUs, or Embedded Coder^® to generate code for the entire Simulink model—including the preprocessing, postprocessing, and the PyTorch model—for deployment to embedded hardware.

The generated standalone source code is readable, portable, and independent of Python^® and third-party software by default. You can integrate the generated code into existing applications as source code, static libraries, or dynamic libraries. You can further optimize the generated code by enabling multicore execution, vectorization, or hardware-optimized libraries for your target platform.

Whether you are deploying applications to desktop systems, embedded hardware, or NVIDIA GPU–accelerated platforms, this workflow provides an efficient path from pretrained PyTorch models to production-ready code.