tentime/ufo-mnist

UFO-MNIST

  

<details><summary>Table of Contents</summary><p>

• —Why UFO-MNIST
• —Get the Data
• —Labels
• —Usage
• —Benchmark
• —Visualization
• —Contributing
• —Citing UFO-MNIST
• —License </p></details><p></p>

UFO-MNIST is a dataset of UFO-like spotting patterns and common aerial lookalikes, consisting of a training set of 8,000 examples and a test set of 2,000 examples. Each example is a 28x28 grayscale image associated with one of 10 labels.

UFO-MNIST is designed as a compact, MNIST-style benchmark for machine-learning examples and image classifiers. It shares the original MNIST image size and train/test structure, and it is provided both as a compressed NumPy archive and as IDX gzip files compatible with common MNIST loaders.

Here's an example of how the data looks:

[image]

Why UFO-MNIST

MNIST-style datasets are useful because they are small, fast, visual, and easy to load. UFO-MNIST keeps those properties while moving away from handwritten digits into low-resolution spotting categories: disks, orbs, triangles, formations, glows, aircraft, balloons, birds, and celestial or sensor artifacts.

The dataset is assembled from public UFO/UAP sighting references, official release material, and generated augmentations that make the classes balanced and easy to use in MNIST-style experiments.

Get the Data

You can use the NumPy archive directly:

The dataset is also stored in the same IDX gzip format used by the original MNIST dataset:

Metadata:

Labels

Each training and test example is assigned to one of the following labels:

Usage

Loading the NumPy archive

import numpy as np

data = np.load("data/ufo_mnist_v1/ufo_mnist_28x28.npz")
X_train = data["train_images"]
y_train = data["train_labels"]
X_test = data["test_images"]
y_test = data["test_labels"]

Loading the IDX files with Python

Use utils/mnist_reader.py in this repository:

from utils import mnist_reader

X_train, y_train = mnist_reader.load_mnist("data/ufo", kind="train")
X_test, y_test = mnist_reader.load_mnist("data/ufo", kind="t10k")

Build from source

python3 -m pip install -e .
ufo-mnist build --output data/ufo_mnist_v1 --seed 1337
ufo-mnist inspect --dataset data/ufo_mnist_v1
python3 scripts/export_idx.py

Benchmark

The table below lists local benchmarks on the provided train/test split.

The CNN benchmark uses three convolutional blocks with batch normalization, dropout, adaptive pooling, and AdamW. Full metrics are available in `cnn_metrics.json`.

Visualization

Training samples by class:

[image]

Test samples by class:

[image]

Contributing

Issues and pull requests are welcome. Useful contributions include better loaders, benchmark submissions, visualization notebooks, and reproducible model scripts. If you submit a benchmark, include the exact train/test split, code, seed, preprocessing, and test accuracy.

Citing UFO-MNIST

If you use UFO-MNIST in a project or publication, cite this repository:

@misc{ufo_mnist_2026,
  title        = {UFO-MNIST: A 28x28 Grayscale Dataset of UFO-like Spotting Patterns},
  author       = {tentime},
  year         = {2026},
  howpublished = {\url{https://github.com/tentime/ufo-mnist}},
}

License

MIT. See LICENSE.