--- 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)
View Model Plot ![Model Image](./model.png)