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README.md

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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 
+
+![Model Demo](./demo.png)
+  
+
+<details>
+
+<summary> View Model Plot </summary>
+
+  ![Model Image](./model.png)
+  
+</details>