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Homework Assignment: Image Classification with ResNet and Transfer Learning

Objective:

The objective of this homework is to gain hands-on experience with image classification using deep neural networks, specifically ResNet, through the application of transfer learning. Students will fine-tune pre-trained ResNet models on either the CIFAR-10 or CIFAR-100 datasets and report their results.

Instructions:

Dataset Selection:

Choose either the CIFAR-10 or CIFAR-100 dataset for this assignment. Provide a brief overview of the selected dataset, including the number of classes and the size of the images.

Understand ResNet:

Give a brief introduction to ResNet (Residual Networks). Explain the architecture, focusing on the concept of residual learning and the benefits it brings to training deep neural networks.

Pre-trained ResNet Models:

Select a version of ResNet (e.g., ResNet-18, ResNet-50) and find a pre-trained model using a popular deep learning library such as PyTorch or TensorFlow. Provide details on how to load the pre-trained model.

Fine-Tuning:

Implement the fine-tuning process by loading the pre-trained ResNet model and adjusting the final layers to match the number of classes in the chosen CIFAR dataset. Train the model on the training set while validating on the validation set.

Experiments:

Conduct experiments with different hyperparameters, such as learning rates, batch sizes, and training epochs. Report the results, including accuracy and loss metrics, for each experiment.

Performance Evaluation:

Evaluate the performance of the fine-tuned ResNet model on the test set of the chosen CIFAR dataset. Discuss the achieved accuracy and any observations regarding the model's performance.

Comparison:

Optionally, compare the performance of the fine-tuned ResNet model with a baseline model (e.g., a model trained from scratch on the same dataset) or other state-of-the-art approaches. Discuss the advantages of using transfer learning in this context.

Documentation:

Prepare a comprehensive document that includes the following:

Introduction to the chosen CIFAR dataset.

Explanation of the ResNet architecture and transfer learning.

Details of the fine-tuning process and hyperparameter settings.

Results of experiments and performance evaluation.

Comparison with baseline models or alternative approaches (if applicable).

Conclusion and potential improvements.

Submission:

Submit your documentation along with any relevant code and data files.

Due Date:

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