1. Ultimate Guide to Fine-Tuning in PyTorch : Part 1 — Pre-trained Model and Its Configuration
2. Ultimate Guide to Fine-Tuning in PyTorch : Part 3 —Deep Dive to PyTorch Data Transforms with Examples
3. Fine-tune a pretrained model
4. TORCHVISION OBJECT DETECTION FINETUNING TUTORIAL
5. [NLP Tutorial] Fine-Tuning in PyTorch

Parts

This article is divided into four parts, with each part focusing on different aspects of fine-tuning models.

Part 1 : We will delve into defining a pre-trained model and configuring it to suit your target task.
Part 2 : The second part will explore various techniques to enhance the accuracy of your fine-tuned model.
Part 3 : Moving on to Part Three, we will cover the process of Defining Data and Applying Transformations tailored specifically to your target task.
Part 4 : Finally, in the last of this series, we’ll address Model Training Observability, including which metrics to track during training and how to effectively manage model checkpoints, among other important aspects.

Outlines

• Introduction — The Model and Its Configuration
• Loading a pre-trained model
• Modifying model head
• Setting Learning Rate, Optimizer and Weight Decay
• Choosing Loss Function
• Freezing Full or Partial network
• Define Model Floating-point precision
• Training and Validation Mode
• Single GPU and Multi GPU
• Conclusion

Introduction

Defining a model includes a range of important decisions, including selecting the appropriate architecture, customizing the model head, configuring the loss function and learning rate, setting the desired floating-point precision, and determining which layers to freeze or fine-tune, and many more. In this article, we will explore each of these aspects in detail, providing valuable insights to help you effectively define and fine tune your model.

Loading a pre-trained model