This datasets are mnist, cifar10 and cifar100 and fashion mnist.

The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples,
and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been
size-normalized and centered in a fixed-size image

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class.
There are 50000 training images and 10000 test images.

The dataset is divided into five training batches and one test batch, each with 10000 images. The test
batch contains exactly 1000 randomly-selected images from each class. The training batches contain the
remaining images in random order, but some training batches may contain more images from one class than
another. Between them, the training batches contain exactly 5000 images from each class.

This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each.
There are 500 training images and 100 testing images per class. The 100 classes in the
CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label
(the class to which it belongs) and a "coarse" label (the superclass to which it belongs).
Here is the list of classes in the CIFAR-100:

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.
Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for
benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

streamlit run stream.py

The result is as follows:

You can select the dataset you want to use by:

In mnist and fashion mnist datasets, we give you comfort in 2 different looks as follows:

• we have deep neural network with keras in tensorflow

Install python and:

pip install -r requirements.txt