This project involves training a neural network model to classify tire textures as either "Normal Tire" or "Cracked Tire." The model is built using the Keras library and is trained on a dataset of tire texture images. The trained model is then incorporated into a simple GUI application using Tkinter for image classification.
- Jupyter Notebook file containing the code for data analysis, visualization, neural network model creation, training, and evaluation.
- Utilizes the Keras library for building a Convolutional Neural Network (CNN).
- Implements data augmentation techniques for training dataset improvement.
- Saves the trained model as 'model3.hdf5'.
- Evaluates the model's performance on the testing dataset.
- Python script for a GUI application using Tkinter.
- Loads the trained model ('model3.hdf5') and defines classes for classification.
- Allows users to upload an image for classification using the trained model.
- Displays the selected image and the predicted class (Normal Tire or Cracked Tire).
- Placeholder background image for the Tkinter GUI.
- Replace with the actual path to your preferred background image.
- Placeholder upload icon for the Tkinter GUI button.
- Replace with the actual path to your preferred upload icon.
- Install Dependencies pip install tensorflow pillow matplotlib keras scikit-learn