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:
- Data Collection: Use a standard image dataset, such as CIFAR-10, which contains labeled images of different objects.
- Preprocessing: Preprocess the images by normalizing pixel values, resizing, and augmenting the dataset to improve model performance.
- Model Design: Design a CNN architecture using Python and a deep learning library such as TensorFlow or PyTorch.
- Training: Train the CNN on the preprocessed dataset, using techniques such as batch normalization and dropout to prevent overfitting.
- Evaluation: Evaluate the model’s performance on a test dataset, calculating metrics such as accuracy, precision, and recall.
- Optimization: Optimize the model by tuning hyperparameters and experimenting with different architectures.
- 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
Requirements: i need answer and video explaination for this assigment
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