Project: Grocery Store Image Classification

Description: This project is about building a Convolutional Neural Network (CNN) to classify images of grocery items into one of three categories: Fruits, Packages, and Vegetables. The model is trained on a dataset consisting of images of fruits, packages, and vegetables. The dataset is divided into training, validation, and test sets.

Technologies Used: Python 3.9 NumPy TensorFlow 2.6 Keras Pandas CSV SciPy Matplotlib

How to Use: Clone the repository to your local machine. Download the GroceryStoreDataset-master.zip from (https://github.com/marcusklasson/GroceryStoreDataset) and extract it. Set the paths for train, validation, and test directories in the script to the appropriate paths on your local machine. Run the script to train the model. Once the model is trained, you can use the classify() function to classify new images. Set the path of the image you want to classify as the argument for this function. Note: The classify() function is currently commented out in the script. You will need to uncomment it and provide an image path to use it.

Acknowledgements: The GroceryStoreDataset was used in this project. The CNN architecture used in this project is inspired by the TensorFlow tutorial on image classification.