MNIST Image Classification with Modlee: An End-to-End Tutorial

TLDR

This blog will take you on a journey through the process of image classification using the MNIST dataset with Modlee's machine learning package. You'll learn about the historical significance of the MNIST dataset, the steps to create a machine learning pipeline for image classification, and how Modlee can automate this process. By the end of this blog, you'll be able to train, evaluate, and compare your models' performance, and understand the benefits of using Modlee in your machine learning projects.

A Deep Dive Into MNIST

The MNIST (Modified National Institute of Standards and Technology) dataset is a pillar of machine learning. It consists of 70,000 small grayscale images of handwritten digits, each 28x28 pixels. Since its creation, MNIST has served as a benchmark for machine learning algorithms, primarily for image classification tasks. While there have been significant advancements in the field, mastering MNIST remains a crucial step for any aspiring machine learning practitioner. Despite its simplicity, MNIST poses unique challenges that provide a practical introduction to key machine learning concepts and techniques.

Building an Image Classification Pipeline with MNIST

Creating an image classification pipeline involves several steps. First, we load the MNIST dataset into our environment. Next, we preprocess the data to ensure it is in a suitable format for our model. After that, we define a machine learning model for classification and train it using our preprocessed data. Finally, we evaluate our model's performance. Throughout this process, experimenting with different model configurations is essential to achieving the best possible performance.

Automating ML Experimentation with Modlee

Modlee is a powerful tool that automates the machine learning experimentation process. With Modlee, you can quickly load data, define models, and log experiments. The following tutorial will guide you through using Modlee for MNIST image classification, demonstrating how it simplifies and enhances the machine learning workflow.

import torchvision.transforms as transforms
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader
import pytorch_lightning as pl
import torch
import modlee
import os

# Initialize Modlee with your API key
modlee.init(api_key='replace-with-your-API-key')

# Load and preprocess the MNIST dataset
train_dataset = MNIST(root='./data', train=True, download=True, transform=transform)
val_dataset = MNIST(root='./data', train=False, download=True, transform=transform)

# Define DataLoaders
train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)
val_dataloader = DataLoader(val_dataset, batch_size=4)

# Use Modlee to recommend a model
recommender = modlee.recommender.from_modality_task(
    modality='image',
    task='classification'
)

# Train and evaluate the model
recommender.fit(train_loader)
modlee_model = recommender.model

Ready to explore further? Follow this link to access the full tutorial.

Understanding Modlee

Modlee is a Python package designed to streamline and automate your machine learning workflows. It offers features like automated experiment collaboration, which standardizes and shares all your experiment knowledge across diverse datasets and models. Modlee also provides actionable machine learning model architecture recommendations based on shared experiment metadata, predicting optimal models for your datasets. All your experiments are automatically documented, preserving essential information. By using Modlee, you can save time, protect your research, and progress your machine learning projects faster and smarter.

FAQ

What is the MNIST dataset?

MNIST is a dataset of 70,000 small grayscale images of handwritten digits. It is commonly used to benchmark machine learning algorithms, primarily image classification tasks.

How do I load the MNIST dataset for use in a machine learning model?

The MNIST dataset can be loaded using various libraries in Python, such as torchvision in PyTorch. It can be downloaded and transformed into a suitable format for your model.

What is image classification?

Image classification is a type of machine learning task where a model is trained to predict the class or category of an image.

What is a machine learning pipeline?

A machine learning pipeline is a sequence of data processing elements, where the output of one element is the input of the next. These elements include steps like data loading, data preprocessing, model training, and model evaluation.

Why is model experimentation critical when working with MNIST?

Model experimentation is essential to find the best performing model configuration. It involves tweaking various parameters and testing different models to achieve the highest accuracy.

What is Modlee and how does it automate the machine learning process?

Modlee is a Python package that automates various aspects of the machine learning workflow, such as data loading, model creation, and experiment logging. It streamlines the process, making it easier to train, evaluate, and compare models.