fcrn-dp-nyu-depth-v2-tf

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.

Metric | Value |
---|---|

Type | Monodepth |

GFLOPs | 63.5421 |

MParams | 34.5255 |

Source framework | TensorFlow* |

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).

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`

.

Image, name - `Placeholder`

, shape - `1, 3, 228, 304`

, format is `B, C, H, W`

, where:

`B`

- batch size`C`

- channel`H`

- height`W`

- width

Channel order is `BGR`

.

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.

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.

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

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>

The original model is released under the following license:

Copyright (c) 2016, Iro Laina

All rights reserved.

Redistribution and use in source and binary forms, with or without

modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this

list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,

this list of conditions and the following disclaimer in the documentation

and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"

AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE

IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE

DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE

FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL

DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR

SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER

CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,

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