CIFAR-10 Image Classification with Transfer Learning

This repository explores image classification on the CIFAR-10 dataset using transfer learning with pre-trained models (AlexNet & ResNet) in PyTorch. The notebooks demonstrate two approaches:

1. Fine-tuning ResNet-50 (Notebook 1)

• Leverages the pre-trained ResNet-50 model, freezing its weights and retraining only the final classification layer.

2. Fine-tuning AlexNet (Notebook 2)

• Utilizes the pre-trained AlexNet model, similar to ResNet-50, with fine-tuning of the final layer.

Code Structure:

Notebook 1 (ResNet-50):

• Imports necessary libraries.
• Loads the pre-trained ResNet-50 model with frozen weights.
• Modifies the final layer for 10-class classification.
• Configures device (CPU or GPU).
• Defines data transformations for image pre-processing (resizing, conversion to tensors, normalization).
• Loads the CIFAR-10 dataset for training and testing with data augmentation.
• Creates data loaders for training and testing.
• Defines the loss function (cross-entropy) and optimizer (Adam).
• Implements a training loop with epoch iterations, loss calculation, backpropagation, and parameter updates.
• Evaluates the model on the test set and prints accuracy.

Notebook 2 (AlexNet):

• Similar structure to Notebook 1, but using the pre-trained AlexNet model downloaded with alexnet_pytorch.

Key Points:

• Transfer learning leverages pre-trained models on large datasets for improved performance on new tasks with less training data.
• Fine-tuning adjusts only the final layers of the pre-trained model for the specific classification problem.
• Data augmentation is an optional technique to increase training data variety, potentially improving model robustness.
• Hyperparameter tuning (learning rate, epochs) can be explored to optimize performance.