The MNIST dataset provided in a easy-to-use CSV format

The original dataset is in a format that is difficult for beginners to use. This dataset uses the work of Joseph Redmon to provide the MNIST dataset in a CSV format.

The dataset consists of two files: - mnist_train.csv - mnist_test.csv

The mnist_train.csv file contains the 60,000 training examples and labels. The mnist_test.csv contains 10,000 test examples and labels. Each row consists of 785 values: the first value is the label (a number from 0 to 9) and the remaining 784 values are the pixel values (a number from 0 to 255).

Know more about MNIST at Wiki

More - Find More Exciting🙀 Datasets Here - An Upvote👍 A Dayᕙ(`▿´)ᕗ , Keeps Aman Hurray Hurray..... ٩(˘◡˘)۶Haha

Dataset Overview

Dataset Name: Digit MNIST CSV (Flattened)

Description: This dataset is a CSV-formatted version of the original MNIST handwritten digits dataset. Each image has been flattened from its original 28×28 grayscale format into 784 numerical pixel features, with an additional label column indicating the digit class (0–9).

The dataset is intended for use in machine learning experiments, data analysis, and educational purposes, especially in environments where image-based datasets are inconvenient.

Dataset Composition

• Total Samples:

  • Training set: 60,000 rows
  • Test set: 10,000 rows

• Features:

  • pixel_0 to pixel_783: Normalized grayscale pixel intensities (float values in range [0, 1])
  • label: Integer digit class (0–9)

• File Format: CSV

• Files Included:

  • mnist_train.csv
  • mnist_test.csv

Data Collection Process

The original MNIST dataset was collected by the National Institute of Standards and Technology (NIST) and consists of handwritten digits written by U.S. Census Bureau employees and high school students.

This CSV version was generated by:

1. Loading the MNIST dataset using torchvision.datasets.MNIST
2. Converting each image to a tensor
3. Flattening each 28×28 image into a 784-length vector
4. Appending the corresponding label
5. Saving the processed data as CSV files

No additional preprocessing (such as augmentation or filtering) was applied beyond normalization.

Intended Use

Primary Uses:

• Classical machine learning algorithms (Logistic Regression, SVM, Random Forest)
• Data analysis and visualization
• Educational and academic projects
• Benchmarking CSV-based ML pipelines

Out-of-Scope Uses:

• Real-world handwriting recognition systems
• Sensitive or high-stakes decision-making applications

Distribution and Licensing

• Original Dataset License: MNIST is released under a permissive license for research and educational use.
• CSV Conversion License: Same as the original MNIST dataset.

Users should cite the original MNIST dataset when using this data.

Ethical Considerations

• The dataset contains no personally identifiable information (PII).
• All data is anonymized handwritten digit imagery.
• There are no known ethical risks associated with using this dataset.

Bias and Limitations

• The dataset represents handwriting styles from a limited demographic group.
• Not representative of global handwriting variations.
• Flattening removes spatial relationships between pixels, which may reduce performance for some models.

Maintenance

• Maintainer: Dataset creator / Kaggle uploader
• Update Frequency: None (static dataset)

Citation

If you use this dataset, please cite:

Yann LeCun, Corinna Cortes, and Christopher J.C. Burges.
The MNIST database of handwritten digits.

MNIST Handwritten Digits (CSV Format)

This dataset is a CSV-formatted version of the classic MNIST handwritten digits dataset.
It contains grayscale images of handwritten digits (0–9) and is widely used for training and evaluating machine learning and deep learning models.

📁 Files Included

• mnist_train.csv — 60,000 training examples
• mnist_test.csv — 10,000 test examples

📊 Data Format

Each row represents one 28 × 28 grayscale image flattened into a single row:

• Column 1: label — digit class (0 to 9)
• Columns 2–785: pixel1 to pixel784 — pixel intensity values (0–255)

This tabular format makes the dataset easy to use with standard data science and machine learning libraries without requiring image preprocessing.

🎯 Use Cases

• Learning and practicing machine learning
• Digit classification tasks
• Quick experimentation with tabular models
• Kaggle notebooks and educational projects

📜 License

This dataset is distributed under the MIT License.

Dataset

This dataset was created by Aravint Annamalai

Contents

THE MNIST DATABASE of handwritten digits mnist 手寫辨識資料 http://yann.lecun.com/exdb/mnist/ mnist in csv 格式，出自於kaggle https://www.kaggle.com/oddrationale/mnist-in-csv/data Yann LeCun, Courant Institute, NYU Corinna Cortes, Google Labs, New York Christopher J.C. Burges, Microsoft Research, Redmond 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. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.

Contents of the dataset

The dataset contains 70000 images (train + test), each with 784 pixels and 70000 labels The dimensions of the csv file are: 70000 x 785 with the first column being the target variable

How to read the dataset

import numpy as np import pandas as pd

df = pd.read_csv(mnist.csv, header=None) y = np.array(df.iloc[:, 0]) # The 0th column is the target variable, y.shape yields (70000, ) X = np.array(df.iloc[:, 1:]) # The rest of the columns are the input data (pixel values) X = X.reshape((X.shape[0], int(np.sqrt(X.shape[1])), int(np.sqrt(X.shape[1])))) # X.shape yields (70000, 28, 28)

MNIST tabular (CSV) dataset: image_path, label, split

Here we present a dataset, MNIST4OD, of large size (number of dimensions and number of instances) suitable for Outliers Detection task.The dataset is based on the famous MNIST dataset (http://yann.lecun.com/exdb/mnist/).We build MNIST4OD in the following way:To distinguish between outliers and inliers, we choose the images belonging to a digit as inliers (e.g. digit 1) and we sample with uniform probability on the remaining images as outliers such as their number is equal to 10% of that of inliers. We repeat this dataset generation process for all digits. For implementation simplicity we then flatten the images (28 X 28) into vectors.Each file MNIST_x.csv.gz contains the corresponding dataset where the inlier class is equal to x.The data contains one instance (vector) in each line where the last column represents the outlier label (yes/no) of the data point. The data contains also a column which indicates the original image class (0-9).See the following numbers for a complete list of the statistics of each datasets ( Name | Instances | Dimensions | Number of Outliers in % ):MNIST_0 | 7594 | 784 | 10MNIST_1 | 8665 | 784 | 10MNIST_2 | 7689 | 784 | 10MNIST_3 | 7856 | 784 | 10MNIST_4 | 7507 | 784 | 10MNIST_5 | 6945 | 784 | 10MNIST_6 | 7564 | 784 | 10MNIST_7 | 8023 | 784 | 10MNIST_8 | 7508 | 784 | 10MNIST_9 | 7654 | 784 | 10

The dataset contains various MNIST like datasets in teh form of a csv files.

  MNIST

Based on the MNIST Dataset in OpenML: OpenML mnist_784. The way to reproduce: from sklearn.datasets import fetch_openml dfX, dsY = fetch_openml('mnist_784', version = 1, return_X_y = True, as_frame = True)

dfX.columns = [str(ii) for ii in range(dfX.shape[1])] dfX['Label'] = dsY dfX.to_csv('MNIST.csv')

  Fashion MNIST

Based on Zalando Research - FashionMNIST.
Packaged into a CSV in a Row… See the full description on the dataset page: https://huggingface.co/datasets/Royi/MNIST.

The legendary MNIST handwritten digits dataset in CSV format with test-train split.

The MNIST dataset provided in a easy-to-use CSV format

The original dataset is in a format that is difficult for beginners to use. This dataset uses the work of Joseph Redmon to provide the MNIST dataset in a CSV format.

The dataset consists of two files:

1. mnist_train.csv
2. mnist_test.csv

The mnist_train.csv file contains the 60,000 training examples and labels. The mnist_test.csv contains 10,000 test examples and labels. Each row consists of 785 values: the first value is the label (a number from 0 to 9) and the remaining 784 values are the pixel values (a number from 0 to 255).

MNIST dataset used during the Perceval Quest challenge

This repository hosts a partial MNIST dataset used during the Perceval Quest as part of the Hybrid AI Quantum Challenge. The dataset is stored under data/ and split into train.csv and val.csv. This dataset is a subset of the original MNIST dataset that can be found here and introduced in [LeCun et al., 1998a]. The Perceval Quest challenge lasted from November 2024 to March 2025. More than 64 teams participated in its first phase… See the full description on the dataset page: https://huggingface.co/datasets/Quandela/PercevalQuest-MNIST.

Please find below the descriptions of the three configurations for partitioning the MNIST Train dataset into 10 clients and the MNIST Train data:

1. Balanced Distribution: In the first configuration, the MNIST dataset is partitioned among 10 clients in a balanced manner. This means that the data samples from each class are evenly distributed among the clients. Each client receives a roughly equal number of images from each digit class, ensuring that the distribution of samples across clients is proportional and representative of the overall dataset. [ Config 1]
2. Heterogeneous Distribution (One Class per Client): In the second configuration, the MNIST dataset is partitioned in a heterogeneous manner, where each client is assigned a single digit class exclusively. This means that one client will only receive images of the digit '0', another client will receive images of the digit '1', and so on. In this setup, each client becomes an expert in classifying a specific digit, allowing for specialized training and evaluation. [ Config 2]
3. Mixed Distribution: In the third configuration, the MNIST dataset is partitioned using a mixed distribution approach. This means that the data samples from all digit classes are distributed among the 10 clients, but the distribution is not necessarily balanced. The number of samples assigned to each client may vary for different digit classes, resulting in an uneven distribution across the clients. This configuration aims to capture both the overall diversity of the dataset and the varying difficulty levels of classifying different digits. [ Config 3 ]

Mnist-dataset/
├── config1/
│ ├── client-1/
│ │ └── data.csv
│ ├── client-2/
│ │ └── data.csv
│ ├── client-3/
│ │ └── data.csv
│ └── ...
├── config2/
│ ├── client-1/
│ │ └── data.csv
│ ├── client-2/
│ │ └── data.csv
│ ├── client-3/
│ │ └── data.csv
│ └── ...
├── config3/
│ ├── client-1/
│ │ └── data.csv
│ ├── client-2/
│ │ └── data.csv
│ ├── client-3/
│ │ └── data.csv
│ └── ...
└── mnist_test.csv

***

License: Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset, which is a derivative work from original NIST datasets. MNIST dataset is made available under the terms of the Creative Commons Attribution-Share Alike 3.0 license.

***

Easy MNIST

MNIST processed into three easy to use formats. Each .zip file contains a labels_and_paths.csv file and a data directory.

  mnist_png.zip

MNIST in the png format. label path 0 5 data/0.png 1 0 data/1.png 2 4 data/2.png 3 1 data/3.png 4 9 data/4.png ... ... ... 69995 2 data/69995.png 69996 3 data/69996.png 69997 4 data/69997.png 69998 5… See the full description on the dataset page: https://huggingface.co/datasets/hayden-donnelly/easy-mnist.

Content

Content refers to what is inside the data or document. It includes the actual information such as values, records, features, text, images, or labels.

Context

Context explains why the data exists and how it should be understood. It provides background so the content makes sense.

Please find below the descriptions of the three configurations for partitioning the MNIST Train dataset into 10 clients and the MNIST Train data:

1. Balanced Distribution: In the first configuration, the MNIST dataset is partitioned among 10 clients in a balanced manner. This means that the data samples from each class are evenly distributed among the clients. Each client receives a roughly equal number of images from each digit class, ensuring that the distribution of samples across clients is proportional and representative of the overall dataset. [ Config 1]
2. Heterogeneous Distribution (One Class per Client): In the second configuration, the MNIST dataset is partitioned in a heterogeneous manner, where each client is assigned a single digit class exclusively. This means that one client will only receive images of the digit '0', another client will receive images of the digit '1', and so on. In this setup, each client becomes an expert in classifying a specific digit, allowing for specialized training and evaluation. [ Config 2]
3. Mixed Distribution: In the third configuration, the MNIST dataset is partitioned using a mixed distribution approach. This means that the data samples from all digit classes are distributed among the 10 clients, but the distribution is not necessarily balanced. The number of samples assigned to each client may vary for different digit classes, resulting in an uneven distribution across the clients. This configuration aims to capture both the overall diversity of the dataset and the varying difficulty levels of classifying different digits. [ Config 3 ]

The structure of "Mnist-dataset" folder is :
Mnist-dataset/
├── config1/
│ ├── client-1/
│ │ └── client_1_config1.csv
│ ├── client-2/
│ │ └── client_2_config1.csv
│ ├── client-3/
│ │ └── client_3_config1.csv
│ └── ...
├── config2/
│ ├── client-1/
│ │ └── client_1_config2.csv
│ ├── client-2/
│ │ └── client_2_config2.csv
│ ├── client-3/
│ │ └── client_3_config2.csv
│ └── ...
├── config3/
│ ├── client-1/
│ │ └── client_1_config3.csv
│ ├── client-2/
│ │ └── client_2_config3.csv
│ ├── client-3/
│ │ └── client_3_config3.csv
│ └── ...
└── mnist_test.csv

Based on Kaggle - Sign Language MNIST.Repackaged both CSV's into a single CSV with a field datasetType to assign each to its type. The class mapping: 0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I', 10: 'K', 11: 'L', 12: 'M', 13: 'N', 14: 'O', 15: 'P', 16: 'Q', 17: 'R', 18: 'S', 19: 'T', 20: 'U', 21: 'V', 22: 'W', 23: 'X', 24: 'Y'

Labels 9 (J) and 25 (Z) are excluded as these letters require motion in ASL hence no such images are available.

This directory includes a few sample datasets to get you started.

california_housing_data*.csv is California housing data from the 1990 US Census; more information is available at: https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pub

mnist_*.csv is a small sample of the MNIST database, which is described at: http://yann.lecun.com/exdb/mnist/

anscombe.json contains a copy of Anscombe's quartet; it was originally… See the full description on the dataset page: https://huggingface.co/datasets/ns-1/my-datayset.

Dataset

This dataset was created by Vishwas 27

Contents

Author: Han Xiao, Kashif Rasul, Roland Vollgraf
Source: Zalando Research
Please cite: Han Xiao and Kashif Rasul and Roland Vollgraf, Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms, arXiv, cs.LG/1708.07747

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. Fashion-MNIST is intended 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.

Raw data available at: https://github.com/zalandoresearch/fashion-mnist

Target classes

Each training and test example is assigned to one of the following labels: Label Description
0 T-shirt/top
1 Trouser
2 Pullover
3 Dress
4 Coat
5 Sandal
6 Shirt
7 Sneaker
8 Bag
9 Ankle boot