This project aims to implement a deep learning-based model to classify images into two categories: roses and sunflowers. The project demonstrates the effectiveness of convolutional neural networks (CNNs) for binary image classification tasks using the TensorFlow and Keras frameworks.
Key Components:
Dataset: A curated dataset of rose and sunflower images is used for training and testing. The dataset is split into training and validation sets.
Model Architecture: A CNN architecture is designed to extract spatial features from the images. Layers include convolution, pooling, and fully connected layers.