datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tactile-mnist-touch-syn-single-t32-320x240Documentation is available at https://github.com/TimSchneider42/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.
tactile-mnist-touch-starstruck-syn-single-t32-320x240Documentation is available at https://github.com/TimSchneider42/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.
mnist-lance
MNIST (Lance Format)
A Lance-formatted version of the classic MNIST handwritten-digit dataset covering 70,000 28×28 grayscale digits across ten balanced classes. Each row carries inline PNG bytes, the digit label, the human-readable class name, and a cosine-normalized CLIP image embedding, all backed by a bundled IVF_PQ vector index plus scalar indices on the label columns and available directly from the Hub at hf://datasets/lance-format/mnist-lance/data.
Key features… See the full description on the dataset page: https://huggingface.co/datasets/lance-format/mnist-lance.fashion-mnist-lance
Fashion-MNIST (Lance Format)
A Lance-formatted version of Fashion-MNIST covering 70,000 28×28 grayscale clothing images across ten balanced apparel classes. Each row carries inline PNG bytes, the integer label, the human-readable class name, and a cosine-normalized CLIP image embedding, all backed by a bundled IVF_PQ vector index plus scalar indices on the label columns and available directly from the Hub at hf://datasets/lance-format/fashion-mnist-lance/data.
Key… See the full description on the dataset page: https://huggingface.co/datasets/lance-format/fashion-mnist-lance.tactile-mnist-touch-real-single-t256-320x240Documentation is available at https://github.com/TimSchneider42/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.
fashion_mnist_ambiguousThe images were created such that they have an unclear ground truth,
i.e., such that they are similar to multiple - but not all - of the datasets classes.
Robust and uncertainty-aware models should be able to detect and flag these ambiguous images.
As such, the dataset should be merged / mixed with the original dataset and we
provide such 'mixed' splits for convenience. Please refer to the dataset card for details.tactile-mnist-touch-real-single-t256-320x240Documentation is available at https://github.com/[REDACTED]/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.
mnist_ambiguousThe images were created such that they have an unclear ground truth,
i.e., such that they are similar to multiple - but not all - of the datasets classes.
Robust and uncertainty-aware models should be able to detect and flag these ambiguous images.
As such, the dataset should be merged / mixed with the original dataset and we
provide such 'mixed' splits for convenience. Please refer to the dataset card for details.tactile-mnist-touch-real-single-t256-64x64Documentation is available at https://github.com/TimSchneider42/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.
tactile-mnist-touch-syn-single-t32-64x64Documentation is available at https://github.com/TimSchneider42/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.
tactile-mnist-touch-starstruck-syn-single-t32-64x64Documentation is available at https://github.com/TimSchneider42/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.
tactile-mnist-touch-syn-single-t32-64x64Documentation is available at https://github.com/[REDACTED]/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.
tactile-mnist-touch-syn-single-t32-320x240Documentation is available at https://github.com/[REDACTED]/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.
mnist
MNIST
Description
Raw MNIST dataset as PNG images.
Download
wget 'https://huggingface.co/datasets/teleprint-me/mnist/resolve/main/mnist.tar.gz?download=true' -O mnist.tar.gz
wget 'https://huggingface.co/datasets/teleprint-me/mnist/resolve/main/sha256sum.txt?download=true' -O sha256sum.txt
Validate
sha256sum -c sha256sum.txt
Expected output:
mnist.tar.gz: OK
Extract
tar xf mnist.tar.gz
This will create a top-level mnist/ directory… See the full description on the dataset page: https://huggingface.co/datasets/teleprint-me/mnist.mnist-webdataset-png
MNIST WebDataset PNG
The MNIST dataset with samples stored as PNG images and compiled into the WebDataset format.
DALI/JAX Example
The following code shows how this dataset can be loaded into JAX arrays by DALI.
from nvidia.dali import pipeline_def
import nvidia.dali.fn as fn
import nvidia.dali.types as types
from nvidia.dali.plugin.jax import DALIGenericIterator
from nvidia.dali.plugin.base_iterator import LastBatchPolicy
def get_data_iterator(batch_size, dataset_path):… See the full description on the dataset page: https://huggingface.co/datasets/hayden-donnelly/mnist-webdataset-png.AmbiGAN-MNIST
AmbiGAN-MNIST Companion Dataset
Dataset Summary
The AmbiGAN-MNIST Companion Dataset is a synthetic dataset of ambiguous handwritten digits generated using Ambiguous Generative Adversarial Networks (AmbiGAN).
It is designed to stress-test binary image classifiers under ambiguity, rather than to improve standard classification performance.
Source dataset: MNIST
Resolution: 28×28 grayscale
Generation scheme: 200 samples for each of the 45 digit class pairs… See the full description on the dataset page: https://huggingface.co/datasets/inesgomes/AmbiGAN-MNIST.mnist
MNIST Dataset
This dataset contains MNIST handwritten digit images.
Dataset Description
The classic MNIST dataset containing images of handwritten digits (0-9) for image classification tasks.
Usage
# Download the dataset
wget https://huggingface.co/datasets/Cleanlab/mnist/resolve/main/mnist.tar.gz
tar -xzf mnist.tar.gz
License
MIT License
tactile-mnist-touch-starstruck-syn-single-t32-64x64Documentation is available at https://github.com/[REDACTED]/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.
tactile-mnist-touch-real-single-t256-64x64Documentation is available at https://github.com/[REDACTED]/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.
tactile-mnist-touch-starstruck-syn-single-t32-320x240Documentation is available at https://github.com/[REDACTED]/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.