AWS_Nanodegree_Image_Classifier_VGG

This project is part of Udacity's AWS AI & ML Scholarship Nanodegree program. It involves training an image classifier to recognize different species of flowers using a deep learning model (VGG16). The model achieved a test accuracy of 92.70%.

Project Structure

The project consists of three main files:

1. Image_Classifier_Project_final.ipynb

• A Jupyter Notebook that integrates training and inference steps.
• Loads and preprocesses the image dataset
• Trains a deep learning-based image classifier
• Use the trained classifier to predict image content
• The trained model is saved as a checkpoint along with associated hyperparameters
• Visualizes the model’s performance.

2. train.py

• Handles the training of the neural network on a dataset of flower images.
• Supports VGG16 and EfficientNet-B0 architectures.
• Uses PyTorch for model training and optimization.
• Saves the trained model checkpoint.

3. predict.py

• Loads a trained model and performs inference on new images.
• Supports top-K class predictions.