Set `library_name` to `tf-keras`. (#2)
Browse files- Set `library_name` to `tf-keras`. (8cd0f50931214a9ad4557be8e06ba79773659aad)
Co-authored-by: Lucain Pouget <[email protected]>
README.md
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library_name: keras
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---
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## Model description
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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/)
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Full credits go to [Vu Minh Chien](https://www.linkedin.com/in/vumichien/)
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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.
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## Dataset
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[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.
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## Training procedure
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### Training hyperparameters
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**Model architecture**:
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- UNet with a pretrained DenseNet 201 backbone.
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The following hyperparameters were used during training:
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- learning_rate: 1e-04
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- train_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: ReduceLROnPlateau
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- num_epochs: 10
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### Training results
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| Epoch | Training loss | Validation Loss | Learning rate |
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|:------:|:-------------:|:---------------:|:-------------:|
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| 1 | 0.1333 | 0.1315 | 1e-04 |
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| 2 | 0.0948 | 0.1232 | 1e-04 |
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| 3 | 0.0834 | 0.1220 | 1e-04 |
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| 4 | 0.0775 | 0.1213 | 1e-04 |
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| 5 | 0.0736 | 0.1196 | 1e-04 |
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| 6 | 0.0707 | 0.1205 | 1e-04 |
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| 7 | 0.0687 | 0.1190 | 1e-04 |
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| 8 | 0.0667 | 0.1177 | 1e-04 |
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| 9 | 0.0654 | 0.1177 | 1e-04 |
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| 10 | 0.0635 | 0.1182 | 9e-05 |
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### View Model Demo
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<details>
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<summary> View Model Plot </summary>
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</details>
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version https://git-lfs.github.com/spec/v1
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oid sha256:71ab91e079cd80b4bc2d7ad05667d9b9f634bb1fd260426553fd8deabbde390f
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size 2080
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