Playing Card Recognition with EfficientNet B0

This repository contains a Python notebook that utilizes a PyTorch implementation of the EfficientNet B0 model to determine what playing card is in an image. The notebook provides a step-by-step guide on how to train the model using a dataset from Kaggle.

Dataset

The dataset used in this project can be fetched from the following link on Kaggle: Cards Image Dataset (Classification)

The dataset includes images of playing cards belonging to different classes, suitable for classification tasks.

Usage

To use the notebook, follow these steps:

1. Download the dataset from the provided Kaggle link.
2. Ensure you have all necessary dependencies installed (PyTorch, torchvision, etc.).
3. Open the Python notebook CardClasiffyer.ipynb.
4. Follow the instructions in the notebook to set up the data, train the model, and perform inference on new images.

Requirements

• Python 3.x
• PyTorch
• torchvision
• Other dependencies as specified in the notebook

Credits

• PyTorch implementation of EfficientNet B0: PyTorch Hub
• Dataset: Cards Image Dataset (Classification) by gpiosenka
• Video: Rob Mulla

License

This project is licensed under the MIT License.