Image Classifier Project

This project implements an image classifier using PyTorch. It allows users to train a model on a dataset of images and make predictions on new images with ease.

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Requirements

• Python 3.8 or later
• PyTorch
• torchvision
• Pillow
• numpy
• tqdm
• matplotlib

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Installation

1. Clone the Repository:

   git clone https://github.com/NailaRais/Image-Classifier-Udacity.git
   cd your-repo-name

2. Install Dependencies:

   pip install -r requirements.txt

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Usage

1. Training the Model

Train the model with your dataset by running:

python train.py

During execution, you will be prompted to specify:

• Model Architecture: Choose the base model (e.g., ResNet, VGG).
• Learning Rate: Set the learning rate for training.
• Hidden Units: Define the number of hidden units in the classifier.
• Number of Epochs: Specify how many epochs the model should train.

The trained model is saved as best_model.pth in the current directory.

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2. Making Predictions

To predict the class of a new image, use:

python predict.py

You will be prompted to provide:

• Model Architecture: Specify the trained model's architecture.
• Top Classes to Display: Define how many top predictions should be shown.

The output will display the predicted class and the associated probabilities in the console.

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GitHub Actions Workflow

A GitHub Actions workflow is included to automatically train the model whenever changes are pushed to the main branch.
You can find the workflow file here:

.github/workflows/train_model.yml

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License

This project is licensed under the MIT License.
For more details, refer to the LICENSE file.