Step-by-Step Guide to Fine-tuning Pre-trained Models for Computer Vision Tasks

Photo by Pawel Czerwinski on Unsplash

Fine-tuning pre-trained models for specific computer vision tasks is a common and effective approach in deep learning. The idea is to start with a pre-trained model on a large dataset, typically trained for a general computer vision task like image classification on ImageNet, and then fine-tune it on a smaller dataset related to your specific task of interest. This process helps leverage the knowledge gained by the pre-trained model on a large dataset and adapt it to a more specific domain.

Here’s a step-by-step guide to fine-tuning pre-trained models for computer vision tasks:

1. Select a Pre-trained Model:

• Choose a pre-trained model that fits your task and architecture requirements. Popular choices include models like VGG, ResNet, Inception, and MobileNet.

2. Dataset Preparation:

• Collect and prepare your dataset. Ensure that your dataset is labeled and split into training, validation, and test sets.
• The size of your dataset influences the extent to which you can fine-tune the model. In general, the larger the dataset, the more you can fine-tune without overfitting.

3. Data Preprocessing: