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---
tags:
- image-segmentation
library_name: keras
---
## Model description
Full credits go to: [Vu Minh Chien](https://www.linkedin.com/in/vumichien/)
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 inferring depth information, given only a single RGB image as input.
## Dataset
[NYU Depth Dataset V2](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html) 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 |
### View Model Demo
### View Model Plot
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