The MNIST database of handwritten digits.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/mnist-3.0.1.png" alt="Visualization" width="500px">

Fashion-MNIST is a dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('fashion_mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/fashion_mnist-3.0.1.png" alt="Visualization" width="500px">

MNIST for Diffusion

Training a diffusion model from scratch is pretty cool, why not do so with the canonical "hello world" dataset of computer vision? This dataset matches the sample dataset from this text_to_image.py diffusion tutorial. Specifying ckg/mnist-for-diffusion ought get you off to the races. This dataset contains two copies of the original MNIST train & test sets. The first half of the dataset contains MNIST images with the string-ified class id (i.e: "1") and the second… See the full description on the dataset page: https://huggingface.co/datasets/ckg/mnist-for-diffusion.

Credit: Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998

This is a subset of MNIST handwritten digits dataset (http://yann.lecun.com/exdb/mnist/). Training data of composed of 12,000 images of digits 0 to 9. Test data is composed of 6,000 images of digits 0 to 9 (Original dataset has 60,000 training and 10,000 testing images. We are using a subset for a Galaxy tutorial, so the training is not too computationally intensive). Images are grayscale and 28 by 28 pixels. Each pixel has a value between 0 and 255 (0 for color black, 255 for color white, and all other values for different shades of gray).

This dataset is curated from MNIST digits. I have placed the digits in a 70x70 image where the digits are of dim 28x28. The digits are placed in a random manner and the bounding box information is stored in the csv file within. Every image will have one digit each and an associated bounding box.

This dataset will be perfect for those who wanna learn how bounding boxes and object detection work. This can be used to write a basic custom model to learn object localization.

Moving variant of MNIST database of handwritten digits. This is the data used by the authors for reporting model performance. See tfds.video.moving_mnist.image_as_moving_sequence for generating training/validation data from the MNIST dataset.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('moving_mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

MNIST (Teeny-Tiny Castle)

This dataset is part of a tutorial tied to the Teeny-Tiny Castle, an open-source repository containing educational tools for AI Ethics and Safety research.

  How to Use

from datasets import load_dataset

dataset = load_dataset("AiresPucrs/MNIST-digit", split = 'train')