Programming Question

Image Classification with Convolutional Neural Networks

Example Dataset: link :

Overview:

Students will implement a Convolutional Neural Network (CNN) for an image classification task. The goal is to build a model that can accurately classify images from a dataset such as CIFAR-10.

Instructions:

1. Data Collection: Use a standard image dataset, such as CIFAR-10, which contains labeled images of different objects.
2. Preprocessing: Preprocess the images by normalizing pixel values, resizing, and augmenting the dataset to improve model performance.
3. Model Design: Design a CNN architecture using Python and a deep learning library such as TensorFlow or PyTorch.
4. Training: Train the CNN on the preprocessed dataset, using techniques such as batch normalization and dropout to prevent overfitting.
5. Evaluation: Evaluate the model’s performance on a test dataset, calculating metrics such as accuracy, precision, and recall.
6. Optimization: Optimize the model by tuning hyperparameters and experimenting with different architectures.
7. Reporting: Document the entire process, including the design choices, training process, evaluation results, and insights gained, adhering to the Neural Networks Implementation Project Rubric.

Submission Instructions:

Code File(s):

• Submit your full implementation as either:
• A Jupyter Notebook (.ipynb)
• A Python script (.py)
• Your code must include:
• Data loading and preprocessing
• CNN architecture design
• Training loop and loss function
• Evaluation metrics
• Hyperparameter tuning/experiments
• Use TensorFlow or PyTorch for model implementation.

Report (.pdf or .docx):

Structure your report according to the Neural Networks Implementation Project Rubric and include:

• Introduction: Problem description and dataset overview
• Methodology:
• Preprocessing steps
• CNN architecture design (include diagrams if helpful)
• Training setup and hyperparameters
• Results:
• Performance metrics (accuracy, precision, recall, etc.)
• Confusion matrix and/or classification report
• Training/validation loss and accuracy curves
• Discussion:
• Observations, challenges, and insights
• Justification for design and optimization decisions
• Potential improvements and future work
• Conclusion: Summary of outcomes and takeaways

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