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Model description

The original idea from Keras examples Monocular depth estimation of author Victor Basu

Full credits go to Vu Minh Chien

Depth estimation is a crucial step towards inferring scene geometry from 2D images. The goal in monocular depth estimation is to predict the depth value of each pixel or infer depth information, given only a single RGB image as input.


NYU Depth Dataset V2 is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect.

Training procedure

Training hyperparameters

Model architecture:

  • UNet with a pretrained DenseNet 201 backbone.

The following hyperparameters were used during training:

  • learning_rate: 1e-04
  • train_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: ReduceLROnPlateau
  • num_epochs: 10

Training results

Epoch Training loss Validation Loss Learning rate
1 0.1333 0.1315 1e-04
2 0.0948 0.1232 1e-04
3 0.0834 0.1220 1e-04
4 0.0775 0.1213 1e-04
5 0.0736 0.1196 1e-04
6 0.0707 0.1205 1e-04
7 0.0687 0.1190 1e-04
8 0.0667 0.1177 1e-04
9 0.0654 0.1177 1e-04
10 0.0635 0.1182 9e-05

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