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license: apache-2.0 |
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### Hierarchical Variational Autoencoder (HVAE) |
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Generative model for 2D B-mode Ultrasound Images. |
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This model can be used for unconditional sampling as well as inpainting tasks. |
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Current architecture in this folder is only trained on resolution `256x256x3` (3 video frames per input/output). |
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🌽 The `zea` implementation is a snippet, adapted from the [source](https://github.com/swpenninga/hvae). |
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The weights that are currently present can be read as follows: |
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- `lvh`: trained on `EchonetLVH` dataset |
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- `ur`: encoder retrained with UniformRandomLines subsampling [`zea.agent`](https://github.com/tue-bmd/zea/blob/main/zea/agent/selection.py) |
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- `ge`: encoder retrained with GreedyEntropy subsampling [`zea.agent`](https://github.com/tue-bmd/zea/blob/main/zea/agent/selection.py) |
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- `24`: 24 lines/columns used when subsampling |
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📚 A usage tutorial can be found on [Collab](https://colab.research.google.com/github/tue-bmd/zea/blob/main/docs/source/notebooks/models/hvae_model_example.ipynb) |
