shotegni/Cifar10Mnist

Cifar10Mnist Dataset Card

Dataset Summary

Cifar10Mnist is a synthetic image dataset created by overlaying MNIST digit images on top of CIFAR-10 images. Each example contains a 32x32 RGB image and a paired label tuple: the original CIFAR-10 class name plus the MNIST digit label.

Supported Tasks

• —Image classification
• —Multi-label classification
• —Transfer learning
• —Synthetic data research

Languages

• —Not language-specific

Dataset Structure

Features

• —image: RGB image of shape (32, 32, 3)
• —label: tuple containing:
• —cifar_label: CIFAR-10 class name
• —mnist_label: MNIST digit label (0–9)

Split

• —train: 50,000 synthetic training examples
• —validation: Not explicitly split by the generator script
• —test: 10,000 synthetic test examples

Dataset Construction

Curation Rationale

This dataset was created to experiment with combined visual features drawn from CIFAR-10 and MNIST. The MNIST digit is resized to 32x32 and composited over the CIFAR image using a transparency mask.

Source Data

• —CIFAR-10: 32x32 RGB images in 10 object classes
• —MNIST: 28x28 grayscale digit images in 10 digit classes

Annotation

Labels are derived from the original datasets:

• —CIFAR-10 class label is preserved and converted to a human-readable class name (e.g. Airplane, Dog).
• —MNIST digit label is preserved as an integer 0–9.

Personal or Sensitive Information

• —No personal or sensitive information is present.

Dataset Uses

Recommended Uses

• —Training models on multi-source synthetic image classification
• —Evaluating models on low-resolution composite imagery
• —Research on label conditioning and mixed-domain learning

Limitations

• —Images remain low resolution (32x32) because CIFAR-10 is low-resolution.
• —The dataset is synthetic and may not reflect real-world visual complexity.

Creator

• —Created by the MDMTN project using the Create_Cifar10Mnist_dataset.py script.
• —Repository: https://github.com/salomonhotegni/MDMTN/

Citation

If you use this dataset, please cite the dataset creator or the project repository that generated it.

@INPROCEEDINGS{10650994,
  author={Hotegni, Sedjro S. and Berkemeier, Manuel and Peitz, Sebastian},
  booktitle={2024 International Joint Conference on Neural Networks (IJCNN)}, 
  title={Multi-Objective Optimization for Sparse Deep Multi-Task Learning}, 
  year={2024},
  doi={10.1109/IJCNN60899.2024.10650994}
}

License

• —Use the original licenses of CIFAR-10 and MNIST for dataset redistribution.
• —If this dataset is only for internal use, follow the terms of the CIFAR-10 and MNIST datasets.