User Story: Model Training

[teman67]

As a Developer I use a TensorFlow model for image classification using a Convolutional Neural Network (CNN) and test the model so I can confirm the model works correctly with high accuracy and low loss values.

• Preprocess the image data, including resizing, normalization, and augmentation, to prepare it for training.
• Design the architecture of the CNN model using TensorFlow's Keras API, including convolutional layers, pooling layers, and fully connected layers.
• Compile the CNN model with appropriate loss function, optimizer, and evaluation metrics for image classification tasks.
• Split the image dataset into training, validation, and testing subsets to train and evaluate the model.
• Train the CNN model on the training dataset using TensorFlow, monitoring the training progress and performance on the validation set.
• Evaluate the trained model on the testing dataset to assess its performance in terms of accuracy and loss values.
• Visualize the training and validation metrics, such as accuracy and loss curves, to analyze the model's learning behavior and identify any overfitting or underfitting issues.
• Fine-tune the model architecture and hyperparameters based on the evaluation results and performance metrics to improve model performance.
• Test the trained CNN model with real-world or unseen data to validate its generalization capability and effectiveness in classifying new images.
• Conduct additional testing with different image datasets or scenarios to verify the robustness and reliability of the CNN model.
• Document the model architecture, training procedures, and evaluation results for future reference and reproducibility.
• Iterate on the CNN model based on user feedback and testing results to continuously improve its accuracy and performance.