MNIST
MNIST (Modified National Institute of Standards and Technology) is one of the most iconic machine learning datasets. It groups together centered and standardized images of handwritten numbers (0 to 9) that are used to train and evaluate image classification models.
70,000 images (60,000 for the training, 10,000 for the test), PNG or IDX format
Free access under a Creative Commons Attribution license
Description
Each image in the MNIST dataset is:
- In gray levels
- 28x28 pixels in size
- Centered and pre-processed for optimal learning
- Annotated with the corresponding class (number between 0 and 9)
The dataset is divided into two sets:
- 60,000 images for training
- 10,000 images for testing
It is often used as a starting point for testing new algorithms in Computer Vision/Deep Learning.
What is this dataset for?
MNIST is used for:
- Training image classification models
- The neural network benchmark (CNN, MLP, auto-encoders,...)
- The educational demonstration of supervised learning pipelines
- Experimenting with dimensionality reduction or clustering techniques
- Validation of transfer learning or image generation techniques (GaNS)
Can it be enriched or improved?
Yes, several approaches exist:
- Apply distortions (rotation, noise, scale) to test robustness
- Extend the dataset with multilingual handwritten numbers
- Use MNIST as a basis for generating new synthetic datasets
- Integrate data into hybrid architectures (multimodality, self-supervision,...)
🔗 Source: MNIST Dataset
Frequently Asked Questions
Why is MNIST still in use today?
Because it is a simple standard, quick to handle, and ideal for testing or comparing new algorithms. It's a great starting point for learning Computer Vision techniques.
Are there more complex alternatives to MNIST?
Yes: Fashion-MNIST (clothing), EMNIST (letters + numbers), or QuickDraw (free designs) offer variants with different levels of difficulty.
Is the dataset adapted to modern models?
For advanced searches, MNIST is often too simple. However, it is still useful for prototyping, learning, or quickly demonstrating concepts.
