midasnet

Use Case and High-Level Description

MidasNet is a model for monocular depth estimation trained by mixing several datasets; as described in the following paper: Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer

The model input is a blob that consists of a single image of 1, 3, 384, 384 in RGB order.

The model output is an inverse depth map that is defined up to an unknown scale factor.

Example

See here

Specification

Metric

Value

Type

Monodepth

GFLOPs

207.25144

MParams

104.081

Source framework

PyTorch*

Accuracy

Metric

Value

rmse

0.07071

Input

Original Model

Image, name - image, shape - 1, 3, 384, 384, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is RGB.

Mean values - [123.675, 116.28, 103.53]. Scale values - [51.525, 50.4, 50.625].

Converted Model

Image, name - image, shape - 1, 3, 384, 384, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is BGR.

Output

Original Model

Inverse depth map, name - inverse_depth, shape - 1, 384, 384, format is B, H, W, where:

  • B - batch size

  • H - height

  • W - width

Inverse depth map is defined up to an unknown scale factor.

Converted Model

Inverse depth map, name - inverse_depth, shape - 1, 384, 384, format is B, H, W, where:

  • B - batch size

  • H - height

  • W - width

Inverse depth map is defined up to an unknown scale factor.

Download a Model and Convert it into OpenVINO™ IR Format

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: