Image Classification with TinyVGG

This project uses a TinyVGG neural network to classify images into two classes: "ticker" and "no_ticker". Read more about the TinyVGG here: https://arxiv.org/pdf/2004.15004

Getting Started

Data Setup

To use this project, you'll need to set up a data directory in the root of the project. This directory should contain all the images you want to train the model on.

Name the image files correctly: Image filenames should start with either "ticker" or "no_ticker" to indicate their class.

Training the Model

Once your data is set up, you can train the model by running:

python3 train.py

This will train the TinyVGG model on your dataset.

Running the Application After training the model, you can run the application by executing:

bash
python3 main.py

This will start a simple web application that allows you to upload images and classify them using the trained model.

Requirements

• Python 3.x
• PyTorch
• torchvision