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Diego Riccioly
Diego Riccioly

Download MNIST: A Simple and Effective Way to Learn Image Processing


How to Download MNIST Dataset and Use It for Machine Learning




The MNIST dataset is a large database of handwritten digits that is commonly used for training various image processing systems and machine learning models. It contains 60,000 training images and 10,000 testing images of digits from 0 to 9, each with a size of 28x28 pixels. The dataset is widely used as a benchmark for evaluating the performance of different algorithms and techniques in computer vision and deep learning.


In this article, we will show you how to download the MNIST dataset in different formats, how to load and plot the dataset in Python, and how to train a simple neural network on the dataset using Keras. By the end of this article, you will have a better understanding of the MNIST dataset and how to use it for your own machine learning projects.




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What is MNIST Dataset and Why is it Useful?




MNIST Dataset Overview




The MNIST dataset was created in 1994 by Yann LeCun, Corinna Cortes, and Christopher J.C. Burges as a combination of two of NIST's databases: Special Database 1 and Special Database 3. Special Database 1 and Special Database 3 consist of digits written by high school students and employees of the United States Census Bureau, respectively. The creators felt that since NIST's training dataset was taken from American Census Bureau employees, while the testing dataset was taken from American high school students, it was not well-suited for machine learning experiments. Furthermore, the black and white images from NIST were normalized to fit into a 28x28 pixel bounding box and anti-aliased, which introduced grayscale levels.


The MNIST dataset has become one of the most popular datasets in the field of machine learning, especially for beginners who want to learn the basics of image processing and neural networks. The dataset is easy to use, has a simple format, and has a relatively small size. The dataset also provides a good balance between complexity and simplicity, as the digits are easy to recognize but still have some variations and noise. The dataset has been used for testing various methods and techniques, such as support vector machines, convolutional neural networks, generative adversarial networks, and more.


MNIST Dataset Applications




The MNIST dataset has many applications in different domains and industries. Some examples are:


  • Handwriting recognition: The MNIST dataset can be used to train models that can recognize handwritten digits or characters on paper documents, forms, checks, etc.



  • Optical character recognition (OCR): The MNIST dataset can be used to train models that can convert scanned images or PDF files into editable text.



  • Computer vision: The MNIST dataset can be used to train models that can perform tasks such as image segmentation, object detection, face recognition, etc.



  • Deep learning: The MNIST dataset can be used to train models that can generate realistic images, enhance image quality, perform style transfer, etc.



How to Download MNIST Dataset in Different Formats?




Download MNIST Dataset in Binary Format




The original source of the MNIST dataset is [here]( The dataset is stored in a proprietary binary format that consists of four files:


  • train-labels-idx1-ubyte.gz: training set labels (9,991 bytes)



  • t10k-images-idx3-ubyte.gz: test set images (1,625,281 bytes)



  • t10k-labels-idx1-ubyte.gz: test set labels (4,454 bytes)



To download the MNIST dataset in binary format, you can use the following commands in a terminal:


mkdir mnist cd mnist wget wget wget wget gunzip *.gz


This will create a folder named mnist and download and unzip the four files in it. Each file has a header that describes the number of images, the number of rows, and the number of columns. The rest of the file contains the pixel values of each image, stored as unsigned bytes. Each pixel has a value between 0 and 255, where 0 is black and 255 is white.


Download MNIST Dataset in CSV Format




If you prefer to work with the MNIST dataset in a more human-readable format, you can download it in CSV format from [here]( The dataset is stored in two files:


  • mnist_train.csv: training set (53 MB)



  • mnist_test.csv: test set (9 MB)



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mkdir mnist_csv cd mnist_csv wget wget


This will create a folder named mnist_csv and download the two files in it. Each file has one row per image, and each row has 785 columns. The first column is the label of the image, and the rest are the pixel values of the image, stored as integers between 0 and 255.


Download MNIST Dataset in TensorFlow Format




If you are using TensorFlow as your machine learning framework, you can easily download and load the MNIST dataset using the TensorFlow Datasets (TFDS) library. TFDS is a collection of ready-to-use datasets that are compatible with TensorFlow's data pipeline. The MNIST dataset is one of the datasets available in TFDS.


To download the MNIST dataset in TensorFlow format, you can use the following commands in a Python script or notebook:


import tensorflow as tf import tensorflow_datasets as tfds # Download and load MNIST dataset as tf.data.Datasets train_ds, test_ds = tfds.load('mnist', split=['train', 'test'], shuffle_files=True) # Print some information about the dataset print(train_ds) print(test_ds)


This will download and load the MNIST dataset as two tf.data.Datasets objects: train_ds and test_ds. Each object is an iterable of (image, label) pairs, where image is a tf.Tensor of shape (28, 28, 1) and dtype tf.uint8, and label is a tf.Tensor of shape () and dtype tf.int64. You can use these objects to feed your TensorFlow model or perform other operations on them.


How to Load and Plot MNIST Dataset in Python?




Load MNIST Dataset Using Pandas




If you have downloaded the MNIST dataset in CSV format, you can use Pandas to load it into a DataFrame. Pandas is a popular library for data analysis and manipulation in Python. A DataFrame is a two-dimensional tabular data structure that can store various types of data.


To load the MNIST dataset using Pandas, you can use the following commands in a Python script or notebook:


import pandas as pd # Load training set as a DataFrame train_df = pd.read_csv('mnist_train.csv', header=None) # Load test set as a DataFrame test_df = pd.read_csv('mnist_test.csv', header=None) # Print some information about the DataFrames print(train_df.shape) print(test_df.shape) print(train_df.head()) print(test_df.head())


This will load the MNIST dataset as two DataFrames: train_df and test_df. Each DataFrame has 785 columns, where the first column is the label and the rest are the pixel values. Each DataFrame has 60,000 and 10,000 rows, respectively, corresponding to the number of images in the training and test sets. You can use the head() method to see the first five rows of each DataFrame.


Load MNIST Dataset Using TensorFlow Datasets




If you have downloaded the MNIST dataset in TensorFlow format, you can use TensorFlow Datasets to load it into tf.data.Datasets objects. TensorFlow Datasets is a library that provides a collection of ready-to-use datasets that are compatible with TensorFlow's data pipeline. A tf.data.Dataset is an iterable of elements, where each element can be a tuple, a dictionary, a tensor, or a nested structure of these types.


To load the MNIST dataset using TensorFlow Datasets, you can use the following commands in a Python script or notebook:


import tensorflow as tf import tensorflow_datasets as tfds # Download and load MNIST dataset as tf.data.Datasets train_ds, test_ds = tfds.load('mnist'


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