---
license: apache-2.0
library_name: pytorch
pipeline_tag: image-to-image
datasets:
- ylecun/mnist
language:
- en
---

# DenseUNet

An image-to-image model for upscaling blurry MNIST-like images.

## Table of contents
- [Model Summary](#model-summary)
- [Use Cases & Limitations](#use-cases--limitations)
- [How to Use](#how-to-use)
- [Model Details](#model-details)
- [Training Procedure](#training-procedure)
- [Evaluation](#evaluation)
- [Ethical Considerations & Limitations](#ethical-considerations--limitations)
- [Caveats and Recommendations](#caveats-and-recommendations)
- [Citation](#citation)
- [License](#license)
- [Contact](#contact)

---

## Model Summary
- **Task:** Image to image (upscaling)
- **Architecture:** UNet with Dense layers
- **Inputs:** MNIST image
- **Outputs:** Upscaled images of numbers
- **Intended Users:** Students learning image-to-image tasks

## Use Cases & Limitations
### Intended uses
- learning image-to-image model behavior

### Out-of-scope uses / limitations
- Not for production or deployment

## How to Use
```python
# ToDo
```

## Model Details
- **Developers:** Blaise Gauvin St-Denis (bstdenis)
- **Organization:** Ouranos
- **Version:** v1.0 (2025-08-13)
- **Dataset:** `mnist` (trainsplit)
- **Evaluation dataset:** `mnist` (test split)
- **Compute:** 1x L40 (ToDo training time)
- **Framework:** Dense UNet / PyTorch 2.8
- **Hardware/Software dependencies:** CUDA 12.8, Python 3.12

## Training Procedure

### Data preprocessing
- Resize to 32 x 32 with all possible locations for the 28 x 28 original data
- Downcale 4x (to 8 x 8)

### Hyperparameters
- Epochs:
- Batch size:
- Learnings rate:
- Weight decay:
- Seed: 0

## Evaluation

## Ethical Considerations & Limitations
- For educational purpose only

## Caveats and Recommendations

## Citation
If you use this model, please cite

ToDo

## License

This model is released under the Apache 2.0 License. See `LICENSE` file.

## Contact
- **Maintainer:** @bstdenis