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This tutorial presents a deployment workflow for Depth Anything V2, a PyTorch model that predicts a dense depth map from a single RGB image. The demonstration places the model inside a Simulink system that takes frames from a video stream, preprocesses each frame, runs inference, and postprocesses the output for display.
The speaker runs the simulation and visually checks the input image against the resulting depth map. This is a visual check of the processing pipeline; the demonstration does not report numerical accuracy or performance measurements. The same Simulink model then provides the basis for standalone source code that includes the network and the surrounding image processing logic.
The source description describes MATLAB Coder for C/C++ deployment on CPUs, GPU Coder for CUDA deployment on NVIDIA GPUs, and Embedded Coder for generating code for the entire Simulink system. In the code walkthrough, the network loads weights from binary files on its first pass, prepares a normalized image tensor, and applies a patch embedding convolution. A transformer encoder and decoder produce the depth map.
The workflow targets on-device inference and integration into an existing application. The source description states that the generated code is independent of Python and third-party software by default, but the video provides no hardware sizing guidance or deployment benchmarks.
The demonstration uses MATLAB and Simulink with their code generation products. These are commercial tools; running the open-source Depth Anything model does not itself provide those licenses.
