CNN Parameter Tuner 3.2.0

Greetings and welcome to CNN Parameter Tuner 3.2.0!

This innovative application serves as your gateway to the exciting world of Convolutional Neural Network (CNN) parameter tuning. It empowers you to explore and optimize CNN algorithms with precision and ease, unlocking the full potential of image classification tasks.

The tool offers a wide array of preprocessing options, including normalization, standardization, and various augmentation techniques. You can also experiment with different train-test-validation ratios, custom CNN architectures, epochs, batch sizes, activation functions, optimizer choices, and loss functions. Additionally, the application provides the convenient option to generate a Python script (Jupyter Notebook) based on the selected parameter values for future use or further customization.

Please ensure that your images are organized into separate folders, with each folder name representing a class label. Upon loading the dataset, the application will automatically detect and assign these folder names as the class labels for the classification task.

Kindly note that the software may take approximately one minute to appear. We appreciate your patience!

New Feature: We are excited to introduce Visual Learner Mode, an interactive and beginner-friendly visualization tool for exploring CNN hyperparameters in real time. Users can now adjust the learning rate, kernel size, stride, dropout, number of filters, and activation function, and immediately observe how these changes impact model behavior through live plots and illustrations. This feature is perfect for students and visual learners who want to intuitively understand CNN concepts.