notebooks/depth-anything/README.md

# Depth estimation with DepthAnything and OpenVINO

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[Depth Anything](https://depth-anything.github.io/) 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. 
![image.png](https://depth-anything.github.io/static/images/pipeline.png)

More details about model can be found in [project web page](https://depth-anything.github.io/), [paper](https://arxiv.org/abs/2401.10891), and official [repository](https://github.com/LiheYoung/Depth-Anything)

In this tutorial we will explore how to convert and run DepthAnything using OpenVINO. An additional part demonstrates how to run quantization with [NNCF](https://github.com/openvinotoolkit/nncf/) to speed up the model.

## Notebook Contents

This notebook demonstrates Monocular Depth Estimation with the [DepthAnything](https://github.com/LiheYoung/Depth-Anything) in OpenVINO.

The tutorial 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

## Installation Instructions

This is a self-contained example that relies solely on its own code.</br>
We recommend  running the notebook in a virtual environment. You only need a Jupyter server to start.
For details, please refer to [Installation Guide](../../README.md).