--- license: mit task_categories: - image-classification language: - en tags: - mnist - ufo - uap - computer-vision - image-classification - synthetic-data size_categories: - 10KTable of Contents

* [Why UFO-MNIST](#why-ufo-mnist) * [Get the Data](#get-the-data) * [Labels](#labels) * [Usage](#usage) * [Benchmark](#benchmark) * [Visualization](#visualization) * [Contributing](#contributing) * [Citing UFO-MNIST](#citing-ufo-mnist) * [License](#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: ![](doc/img/ufo-mnist-sprite.png) ## 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](data/ufo_mnist_v1/ufo_mnist_28x28.npz) | `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](data/ufo/train-images-idx3-ubyte.gz) | | `train-labels-idx1-ubyte.gz` | training set labels | 8,000 | [Download](data/ufo/train-labels-idx1-ubyte.gz) | | `t10k-images-idx3-ubyte.gz` | test set images | 2,000 | [Download](data/ufo/t10k-images-idx3-ubyte.gz) | | `t10k-labels-idx1-ubyte.gz` | test set labels | 2,000 | [Download](data/ufo/t10k-labels-idx1-ubyte.gz) | Metadata: | Name | Content | | --- | --- | | [`labels.json`](data/ufo_mnist_v1/labels.json) | label mapping | | [`manifest.csv`](data/ufo_mnist_v1/manifest.csv) | source manifest | | [`samples.csv`](data/ufo_mnist_v1/samples.csv) | per-sample split, label, source family, and seed | | [`dataset_card.md`](data/ufo_mnist_v1/dataset_card.md) | dataset card | | [`checksums.json`](data/ufo_mnist_v1/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 ```python 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: ```python 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 ```bash 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`](src/ufo_mnist/inspect.py) | | Logistic regression | standardization | 0.459 | 0.456 | [`scripts/train_baseline.py`](scripts/train_baseline.py) | | Small CNN | rescale to `[0, 1]` | 0.996 | 0.996 | [`scripts/train_cnn.py`](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`](data/ufo_mnist_v1/cnn_metrics.json). ## Visualization Training samples by class: ![](data/ufo_mnist_v1/contact_sheet_train.png) Test samples by class: ![](data/ufo_mnist_v1/contact_sheet_test.png) ## 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: ```bibtex @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](LICENSE).