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Fine Tune a Vision Model for Image Classification with LoRA

IntermediateGuided Project

Learn to fine-tune a Vision Transformer (ViT) with LoRA (Low-Rank Adapters) for targeted image classification and adapt AI models to your own data for any use case. You might think fine-tuning a model with millions of parameters requires massive compute and memory, not with LoRA. In this guided project, you'll load a pretrained DeiT model using Hugging Face's API, extend its ability to detect new classes, and train it on a custom dataset. You'll learn how parameter-efficient fine-tuning (PEFT) makes adapting large transformer models fast, lightweight, and cost-effective for any use case.

Language

  • English

Topic

  • Computer Vision

Skills You Will Learn

  • Fine-tuning, Computer Vision, PyTorch

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • November 24, 2025
About this Guided Project
Transformers have revolutionized the way machines understand and process information, not only in text but also in images. Vision Transformers (ViTs) have become a cornerstone in modern computer vision, outperforming traditional convolutional networks on many tasks. However, fine-tuning such powerful models can be computationally expensive and memory-intensive, especially on personal hardware or CPUs.

That’s where LoRA (Low-Rank Adaptation) comes in. LoRA makes it possible to adapt large pretrained models efficiently. This project demonstrates how you can take a pretrained Vision Transformer and fine-tune it to recognize new image classes even with limited data and no GPU. By completing this project, you’ll not only understand how modern vision models work but also how to adapt them practically and efficiently for your own use cases.

A Look at the Project Ahead

In this project, you will:
  • Learn the architecture of Vision Transformers (ViTs): how images are processed and interpreted.
  • Explore the intuition behind LoRA (Low-Rank Adaptation): Why is it so effective for lightweight fine-tuning.
  • Load and modify a pretrained DeiT (Data-efficient Image Transformer) model from Hugging Face.
  • Extend a model’s classifier head to recognize new labels and correctly map label IDs for training.
  • Freeze base model weights and insert LoRA adapters to adapt only selected attention layers.
  • Preprocess image datasets using feature extractors compatible with vision transformers.
  • Fine-tune and evaluate the adapted model on a small image dataset.
Skills You’ll Gain:
  • Proficient understanding of transfer learning and parameter-efficient fine-tuning (PEFT).
  • Practical experience with Hugging Face Transformers, PEFT, and Datasets libraries.
  • Hands-on implementation of LoRA adapters in PyTorch.

What You'll Need

  • A compatible modern browser (Chrome, Firefox, Edge, or Safari).
  • Basic Python and PyTorch programming
  • High-level understanding of transformer architecture.
  • Introductory  knowledge of linear algebra (specifically matrix manipulation).
  • Curiosity about transformers and modern computer vision methods

Instructors

Joshua Zhou

Data Scientist

I like building fun and practical things.

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