DepthAnything is a highly practical solution for robust monocular depth estimation. Without pursuing novel technical modules, this project aims to build a simple yet powerful foundation model dealing with any images under any circumstances.
The framework of Depth Anything is shown below. it adopts a standard pipeline to unleashing the power of large-scale unlabeled images.
There are two version of DepthAnything models. The notebooks series include the demonstration of work Depth Anything V1 and Depth Anything V2. Depth Anything V2 significantly outperforms V1 in fine-grained details and robustness.
In these tutorials we will explore how to convert and run DepthAnything using OpenVINO. An additional part demonstrates how to run quantization with NNCF to speed up the model.
Both tutorials consists of following steps:
- Install prerequisites
- Load and run PyTorch model inference
- Convert Model to Openvino Intermediate Representation format
- Run OpenVINO model inference on single image
- Run OpenVINO model inference on video
- Optimize Model
- Compare results of original and optimized models
- Launch interactive demo
This is a self-contained example that relies solely on its own code.
We recommend running the notebook in a virtual environment. You only need a Jupyter server to start.
For details, please refer to Installation Guide.
