ccexam 0.0.1

Digit classifiers (MNIST / USPS / SVHN) with a unified training, evaluation and inference CLI.

ACME Digit Classification

ACME Digit Classification is a machine learning framework for handwritten digit recognition developed as part of the FYS-8805 Collaborative Coding Exam at UiT.

The repository provides a unified interface for training, evaluating, and deploying digit classification models for three customer datasets:

• Customer A: MNIST
• Customer B: SVHN
• Customer C: USPS

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Installation

Install directly from GitHub [NEED TO BE FIXED]:

pip install git+https://github.com/FYS-8805-Collaborative-Coding/Collaborative-Coding-Exam.git

Alternatively, clone the repository via SSH:

git clone [e-mail]:FYS-8805-Collaborative-Coding/Collaborative-Coding-Exam.git

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Quick start: run interference

Suggested way to get inference from a trained model (example):

from src import run_inference

results = run_inference(model="model-a", input_path="path/to/your/data")
print(results)

Or from the command line:

python -m src.inference --model model-a --input datasets/inference/mnist_test_0_label_7.png

Trained model weights are stored in the weights/ directory and are loaded automatically during inference.

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Model Cards

Model A — model-a

Brief description of what this model does and what problem it solves.

Architecture	...
Training data	...
Intended use	...
Limitations	...

Performance:

Metric	Value
Precision	0.00
Recall	0.00
Speed (inference)	0.00 ms / sample

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Model B — SVHN

CNN classifier for Street View House Numbers. It predicts a single cropped house-number digit (0–9) from a 32×32 RGB image.

Architecture	3-block CNN (per block: Conv-BN-ReLU ×2 + MaxPool, channels 3→32→64→128) followed by a 2-layer fully-connected head with dropout 0.3
Training data	SVHN train_32x32.mat (~73k 32×32 RGB cropped-digit images), trained 5 epochs, Adam, lr 1e-3, batch size 64
Intended use	Classifying house-number digits
Limitations	Single digits only (not multi-digit house numbers). Fixed 32×32 RGB input. Trained only 5 epochs with no data augmentation

Performance: (measured on the SVHN test set, weights/svhn.pth, LUMI-G / MI250x)

Metric	Value
Precision	0.9465
Recall	0.9465
Speed (inference)	0.470 ms / sample

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Model C — model-c

Brief description of what this model does and what problem it solves.

Architecture	...
Training data	...
Intended use	...
Limitations	...

Performance:

Metric	Value
Precision	0.00
Recall	0.00
Speed (inference)	0.00 ms / sample

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Documentation

More detailed documentation is available at https://fys-8805-collaborative-coding.github.io/Collaborative-Coding-Exam/.

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Model development

Training

You can run training from the repository root with the CLI. All arguments are entirely optional; running the command without any flags will automatically train on the default mnist dataset:

# Run with absolute defaults (MNIST, 1 epoch, batch size 64, automatic device selection)
python -m src.training

# Run with custom configuration overrides
python -m src.training --dataset mnist --epochs 5 --batch-size 32 --device cuda

The checkpoint is written to weights/mnist.pth by default. The current training entry point supports mnist and can be extended with more datasets through the registry in src/training.py.

Testing

Run the basic tests with:

pytest -q

The tests are lightweight and only validate the training CLI, argument parsing, and factory wiring.

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Contributing to the code

We use a branch → pull request → review workflow. All changes to main require at least one approved review — direct pushes are not allowed.

1. Open an issue describing your change
2. Create a branch and commit your work (reference the issue, e.g. fixes #5)
3. Open a pull request towards main
4. Get a review, address feedback, then merge

See CONTRIBUTION.md for the full guide.

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Further use of the software

If you use this software in your research, teaching, or projects, please cite this repository. This project is released under the MIT License. You are free to use, modify, and distribute the software in accordance with the terms of the license.

Citation

APA:

Chen, S., Løkke, A., Gelato, R., Baburajan, R., Oei, K., & Catteau, M. (2026). Collaborative Coding Exam (Version 1.1.0) [Computer software]. https://github.com/FYS-8805-Collaborative-Coding/Collaborative-Coding-Exam

BibTex:

@software{Chen_Collaborative_Coding_Exam_2026,
author = {Chen, Siyan and Løkke, Andrea and Gelato, Riccardo and Baburajan, Rahul and Oei, Keyne and Catteau, Myrthe},
month = jun,
title = {{Collaborative Coding Exam}},
url = {https://github.com/FYS-8805-Collaborative-Coding/Collaborative-Coding-Exam},
version = {1.1.0},
year = {2026}
}

License

MIT