Rice Image Classification using VGG16 and Transfer Learning

Overview

This repository contains the code for a Convolutional Neural Network (CNN) developed for the classification of rice images. The model is built using the VGG16 architecture and employs transfer learning to leverage pre-trained weights. The dataset used is the Rice Image Dataset, available on Kaggle.

Dataset

The Rice Image Dataset is available on Kaggle: Rice Image Dataset.

• Dataset Description: The dataset comprises images of different varieties of rice grains. The task is to classify these images into their respective rice varieties.

• Dataset Structure:

  • train/: Training images
  • test/: Test images
  • labels.csv: CSV file containing image filenames and corresponding labels (class labels for rice varieties)

Model Architecture

The model is based on the VGG16 architecture. Transfer learning is employed by using pre-trained weights from the ImageNet dataset. The top 4 layers have been frozen and the fully connected layers have been added.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or create a pull request.