UFO-MNIST

Table of Contents

• Why UFO-MNIST
• Get the Data
• Labels
• Usage
• Benchmark
• Visualization
• Contributing
• Citing UFO-MNIST
• License

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:

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:

Name	Content	Examples	Size	Link	SHA-256
ufo_mnist_28x28.npz	train/test images and labels	10,000	5.7 MB	Download	65a87cf1121c38c247862637faea5cb3d0381dcb18dc4dac42d9545f0ef6c5e6

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

Name	Content	Examples	Link
train-images-idx3-ubyte.gz	training set images	8,000	Download
train-labels-idx1-ubyte.gz	training set labels	8,000	Download
t10k-images-idx3-ubyte.gz	test set images	2,000	Download
t10k-labels-idx1-ubyte.gz	test set labels	2,000	Download

Metadata:

Name	Content
labels.json	label mapping
manifest.csv	source manifest
samples.csv	per-sample split, label, source family, and seed
dataset_card.md	dataset card
checksums.json	release checksums

Labels

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

Label	Description
0	disk
1	orb
2	triangle
3	cigar_rod
4	light_formation
5	irregular_glow
6	aircraft
7	balloon
8	bird
9	celestial_or_artifact

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.

Classifier	Preprocessing	Test accuracy	Macro F1	Code
Nearest centroid	raw pixels	0.420	-	src/ufo_mnist/inspect.py
Logistic regression	standardization	0.459	0.456	scripts/train_baseline.py
Small CNN	rescale to [0, 1]	0.996	0.996	scripts/train_cnn.py

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:

Test samples by class:

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.