This project focuses on building a deep learning model for image classification using Convolutional Neural Networks (CNN).
The model was trained to classify images into multiple categories using labeled datasets. The workflow included data preprocessing, dataset splitting, model training, and performance evaluation.
Key steps in the project:
- Data preprocessing and image normalization
- Building CNN architecture
- Training and validating the model
- Evaluating model accuracy and performance
Technologies used:
Python, TensorFlow / Keras, NumPy, Matplotlib
This project demonstrates how deep learning can be used to automatically identify and classify visual data with high accuracy.
