fcrn-dp-nyu-depth-v2-tf

Use Case and High-Level Description

This is a model for monocular depth estimation trained on the NYU Depth V2 dataset, as described in the paper Deeper Depth Prediction with Fully Convolutional Residual Networks, where it is referred to as ResNet-UpProj. The model input is a single color image. The model output is an inverse depth map that is defined up to an unknown scale factor. More details can be found in the following repository.

Specification

Metric

Value

Type

Monodepth

GFLOPs

63.5421

MParams

34.5255

Source framework

TensorFlow*

Accuracy

Metric

Value

RMSE

0.573

log10

0.055

rel

0.127

Accuracy numbers obtained on NUY Depth V2 dataset. The log10 metric is logarithmic absolute error, defined as abs(log10(gt) - log10(pred)), where gt - ground truth depth map, pred - predicted depth map. The rel metric is relative absolute error defined as absolute error normalized on ground truth depth map values (abs(gt - pred) / gt, where gt - ground truth depth map, pred - predicted depth map).

Input

Original Model

Image, name - Placeholder, shape - 1, 228, 304, 3, format is B, H, W, C, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is RGB.

Converted Model

Image, name - Placeholder, shape - 1, 228, 304, 3, format is B, H, W, C, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is BGR.

Output

Original Model

Inverse depth map, name - ConvPred/ConvPred, shape - 1, 128, 160, 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 - ConvPred/ConvPred, shape - 1, 128, 160, 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: