readme: ---
  tags:
  - image-segmentation
  library_name: keras
  ---
  ## Model description
  The original idea from Keras examples [Monocular depth estimation](https://keras.io/examples/vision/depth_estimation/) of author [Victor Basu](https://www.linkedin.com/in/victor-basu-520958147/)
  
  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 infer 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 
  
  ![Model Demo](./demo.png)
    
  
  <details>
  
  <summary> View Model Plot </summary>
  
    ![Model Image](./model.png)
    
  </details>
  