This project implements a multi-class image classification model using deep learning techniques: https://en.wikipedia.org/wiki/Deep_learning.

This project aims to train a model to classify images into one of several predefined categories. Here's a breakdown of the functionalities:

• Data Preparation:
  • Load and pre-process image datasets.
  • Implement data augmentation techniques.
• Model Architecture:
  • Define a Convolutional Neural Network (CNN) architecture for image classification.
• Model Training:
  • Train the model on the prepared dataset.
  • Monitor training progress using metrics like accuracy and loss.
  • Implement techniques like early stopping and regularization to prevent overfitting.
• Evaluation:
  • Evaluate the model performance on the held-out testing set.
  • Calculate metrics like accuracy, precision, recall, and F1 score.
  • Visualize the model's predictions.

This project requires the following libraries:

• TensorFlow/PyTorch (for deep learning)
• NumPy (for numerical computations)
• OpenCV (for image processing)
• scikit-learn (for data manipulation)
• Matplotlib (for visualization) (optional)

Note: The specific libraries may vary depending on your implementation choices.

The project includes the following folders and files:

• dataset/: Contains the image datasets for training, validation, and testing.
• src/: Contains the source code for data preparation, model definition, training, and evaluation.
• requirements.txt: Lists the required Python libraries.
• README.md: This file (you are currently reading it!).