metadata
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
- Use Cases & Limitations
- How to Use
- Model Details
- Training Procedure
- Evaluation
- Ethical Considerations & Limitations
- Caveats and Recommendations
- Citation
- License
- 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
# 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