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AlexNet-Transfer-Learning-on-MNIST

Overview

This project implements a transfer learning approach using a pre-trained AlexNet model on the Fashion-MNIST dataset. By fine-tuning the final layer of the AlexNet model, we demonstrate how pre-trained models can be used effectively for new tasks with minimal modifications.

Dataset

  • Fashion-MNIST dataset consists of 60,000 training images and 10,000 test images, with 10 classes of clothing items such as T-shirts, coats, dresses, etc.
  • Each image is a 28x28 grayscale image, resized to 224x224 to match the input requirements of AlexNet.

Model

  • The AlexNet model, pre-trained on the ImageNet dataset, is used as the base model.
  • All layers except the final fully connected layer are frozen, and the final layer is replaced to predict 10 clothing categories from the Fashion-MNIST dataset.

Training

  • Optimizer: Adam optimizer with a learning rate of 0.001.
  • Loss Function: CrossEntropyLoss.
  • Epochs: The model is trained for 10 epochs.
  • Batch Size: 32

Results

  • The fine-tuned model achieves over 90% accuracy on the test set.
  • This demonstrates the power of transfer learning by leveraging pre-trained models for faster training and higher accuracy on small datasets.