Supercharge Your AI: ResNet50 Transfer Learning Unleashed

Read the original article here: https://lnkd.in/dhpZqbih

Overview

Have you ever wondered how to use a pre-trained model such as ResNet50 to enhance the performance of your machine learning projects? If that’s the case, this article is meant for you.

In this article, you will learn how to perform transfer learning using Keras and ResNet50, which is a popular pre-trained convolutional neural network (CNN) architecture.

This article starts with the basic introduction of?ResNet ?and?transfer learning . After that, you will learn how to apply the transfer learning model using resnet50 and Keras to classify the CIFAR-10 dataset. You will learn various essential steps of transfer learning such as how to freeze layers of a ResNet50 model, how to add new trainable layers, and how to fine-tune the model.?

It does not matter whether you are a beginner or advanced user of AI &ML; this article will help you immensely to accelerate your machine learning projects.?

What Is ResNet?

ResNet is an innovative CNN architecture proposed by Microsoft researchers in 2015. It won the ImageNet competition in?2015?by a significant margin while achieving the?top-5?error rate of just?3.57%.

With the use of ResNet, one can easily develop deep neural networks with many layers ranging from?18?to?152?layers without sacrificing performance. The secret is the skip connections in ResNet, which allow a seamless flow of information across various layers.

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Prior to ResNet, deep learning models often suffered from vanishing and exploding gradient problems. ?ResNet mitigated these issues by using residual connections (skip connections).

We can use ResNet for various tasks, such as image classification, object detection, segmentation, facial recognition, transfer learning, and medical imaging. Some of the popular ResNet architectures are: ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152. In this article, we will use ResNet-50 as a base model for transfer learning on the CIFAR-10 dataset.

The figure below shows a pictorial representation of ResNet architecture. Here, we won’t be explaining the network. Interested readers can learn more about the ResNet architecture from my previous blog post .

What Is Transfer Learning

Transfer learning is a popular method in deep learning that allows the use of features learned from one task and apply them to a related task. In transfer learning, a pre-trained model designed for a specific task is adapted to a new but similar task.

Let us illustrate transfer learning with the help of an analogy. Assume that you are a skillful tennis player. You have developed various skills, such as honing excellent hand-eye coordination, agility, and strategic thinking by playing tennis.

Now, if you want to play badminton, you do not need to start from scratch. Many of the fundamental skills that you learned while playing tennis can help you immensely to quickly learn badminton without any racquet sports experience.

A similar concept applies in transfer learning, where a model trained on one task, such as recognizing objects in photos, can be adapted to perform a related but different task, such as identifying specific types of objects.

Practical Implementation

Here, we will develop a transfer learning model using resnet50 and Keras to classify the CIFAR-10 dataset.

• Hashtags: #TransferLearning, #DeepLearning, #AI, #MachineLearning, #ArtificialIntelligence, #TechInnovation, #NLP, #DataScience, #ComputerVision
• Read the original article here: https://lnkd.in/dhpZqbih