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--- |
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license: apache-2.0 |
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library_name: pytorch |
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pipeline_tag: image-to-image |
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datasets: |
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- ylecun/mnist |
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language: |
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- en |
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--- |
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# DenseUNet |
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An image-to-image model for upscaling blurry MNIST-like images. |
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## Table of contents |
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- [Model Summary](#model-summary) |
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- [Use Cases & Limitations](#use-cases--limitations) |
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- [How to Use](#how-to-use) |
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- [Model Details](#model-details) |
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- [Training Procedure](#training-procedure) |
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- [Evaluation](#evaluation) |
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- [Ethical Considerations & Limitations](#ethical-considerations--limitations) |
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- [Caveats and Recommendations](#caveats-and-recommendations) |
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- [Citation](#citation) |
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- [License](#license) |
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- [Contact](#contact) |
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--- |
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## Model Summary |
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- **Task:** Image to image (upscaling) |
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- **Architecture:** UNet with Dense layers |
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- **Inputs:** MNIST image |
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- **Outputs:** Upscaled images of numbers |
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- **Intended Users:** Students learning image-to-image tasks |
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## Use Cases & Limitations |
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### Intended uses |
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- learning image-to-image model behavior |
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### Out-of-scope uses / limitations |
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- Not for production or deployment |
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## How to Use |
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```python |
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# ToDo |
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``` |
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## Model Details |
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- **Developers:** Blaise Gauvin St-Denis (bstdenis) |
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- **Organization:** Ouranos |
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- **Version:** v1.0 (2025-08-13) |
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- **Dataset:** `mnist` (trainsplit) |
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- **Evaluation dataset:** `mnist` (test split) |
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- **Compute:** 1x L40 (ToDo training time) |
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- **Framework:** Dense UNet / PyTorch 2.8 |
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- **Hardware/Software dependencies:** CUDA 12.8, Python 3.12 |
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## Training Procedure |
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### Data preprocessing |
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- Resize to 32 x 32 with all possible locations for the 28 x 28 original data |
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- Downcale 4x (to 8 x 8) |
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### Hyperparameters |
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- Epochs: |
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- Batch size: |
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- Learnings rate: |
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- Weight decay: |
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- Seed: 0 |
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## Evaluation |
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## Ethical Considerations & Limitations |
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- For educational purpose only |
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## Caveats and Recommendations |
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## Citation |
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If you use this model, please cite |
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ToDo |
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## License |
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This model is released under the Apache 2.0 License. See `LICENSE` file. |
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## Contact |
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- **Maintainer:** @bstdenis |
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