tf-image-classification-eurosat

README for EuroSAT Image Classification Project

Project Overview

This project implements a convolutional neural network (CNN) using TensorFlow for image classification into multiple categories. The goal is to develop a deep learning model capable of identifying and classifying images into various defined categories using the Eurostat dataset.

Dataset

The dataset used is the EuroSAT dataset, which is available through TensorFlow Datasets. The dataset consists of RGB satellite images covering 10 different classes, such as residential areas, forests, and farmlands.

Project Structure

• Data Loading and Preparation:

  • The project begins by loading the EuroSAT dataset using TensorFlow Datasets.
  • The data is then preprocessed and augmented to improve the model's performance and generalization capabilities.

• Model Development:

  • A Convolutional Neural Network (CNN) is developed using TensorFlow and Keras.
  • Various architectures and hyperparameters are explored to optimize the model.

• Training and Evaluation:

  • The model is trained on the preprocessed dataset, and its performance is evaluated using standard metrics.
  • Metrics such as accuracy, precision, recall, and confusion matrix are used to assess the model's performance.

Usage

To run the project, follow these steps:

1. Clone the Repository:

   git clone <repository_url>

2. Install Dependencies: Ensure you have TensorFlow and TensorFlow Datasets installed. You can install them using pip:

   pip install tensorflow tensorflow-datasets

3. Run the Notebook: Open and run the provided Jupyter notebook tf-image-classification_eurosat.ipynb to execute the entire workflow, from data loading to model evaluation.

Key Code Snippets

• Data Loading:

  import tensorflow as tf
  import tensorflow_datasets as tfds
  
  dataset, info = tfds.load("eurosat/rgb", with_info=True, as_supervised=True)

• Model Training:

  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  history = model.fit(train_dataset, epochs=10, validation_data=validation_dataset)

• Evaluation:

  test_loss, test_accuracy = model.evaluate(test_dataset)
  print(f"Test Accuracy: {test_accuracy}")

Resources

• TensorFlow Datasets: EuroSAT
• EuroSAT Dataset GitHub

Contributing

Contributions to improve the project are welcome. Feel free to submit pull requests or report issues.