Build CNN model using data augmentation

In This project, I designed and implemented a Convolutional Neural Network (CNN) model for image classification, utilizing data augmentation techniques to enhance model performance and prevent overfitting. The aim was to create a robust and accurate model that could classify images even with a limited dataset.

Key Achievements:

CNN Architecture: Developed a deep learning model using a CNN architecture, optimized for image recognition tasks. The model included multiple convolutional, pooling, and fully connected layers, tailored to capture essential features from the input images.

Data Augmentation: To improve the model's generalization and performance, I applied various data augmentation techniques such as rotation, flipping, zooming, shifting, and scaling. This helped artificially expand the dataset by creating new variations of the existing images, reducing the risk of overfitting.

Training & Validation: Trained the CNN model on the augmented dataset and evaluated its performance using accuracy, loss metrics, and validation data. Adjusted hyperparameters like learning rate, batch size, and epochs to further fine-tune the model’s performance.

Performance Improvement: The use of data augmentation resulted in a significant improvement in the model’s accuracy and robustness, making it better at generalizing to unseen data while reducing overfitting issues.

Visualization: Visualized the model’s learning process through training curves, confusion matrices, and classification reports, providing clear insights into the performance of the model on both the training and validation sets.

This project showcased my ability to build and optimize deep learning models, leveraging data augmentation to maximize the effectiveness of limited datasets for image classification tasks.