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Flower Classification Using Transfer Learning (DesNet121)

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5 types of flower classification | DesNet121 | Training set 0.98 | Test set 0.95

Tutorial on image classification using transfer learning

Overview

Welcome to this notebook on image classification using transfer learning! In this notebook, we will explore how to use transfer learning, a powerful technique in deep learning, to solve image classification problems.

About Transfer Learning

Transfer learning is a machine learning technique that allows a model trained on one task to be reused for training a second, related task. In the context of deep learning, transfer learning uses a pre-trained neural network model as a starting point for a new task, rather than training the model from scratch. This approach is particularly useful when data or computing resources are limited.

Target

The goal of this notebook is to demonstrate how to use transfer learning to perform image classification on a dataset of flower images. We will leverage a pre-trained convolutional neural network (CNN) as a feature extractor and build a custom classifier on top of it to predict the species of the flower.

Dataset

We will use the “5 flower types classification dataset” available on Kaggle. This dataset contains images of five different types of flowers: lily, lotus, orchid, sunflower, and tulip. Each image is labeled with the corresponding flower type.

Methodology

  1. Data preparation: We will start by preparing the dataset, including loading images, preprocessing, and splitting into training, validation, and test sets.
  2. Model building: Next, we will load a pre-trained CNN as a base model, remove the top (classification) layers, and add custom layers on top of it to build our classifier.
  3. train: We will train the model using transfer learning, fine-tuning the weights of the custom layers while keeping the weights of the pre-trained layers frozen.
  4. Evaluate: Finally, we will evaluate the performance of the trained model on the test set and visualize the results.

Tools and Libraries

The Python programming language and several libraries are used here, including TensorFlow, Keras, and Matplotlib. These libraries provide powerful tools for deep learning, model building, and visualization.