🚀 Shakespeare: Unleashing the Power of PyTorch for Object Identification

🇬🇧 English | 🇹🇷 Türkçe

Shakespeare is a lightweight PyTorch project designed to help you train a model to visually identify objects using popular architectures like ResNet, EfficientNet, VGG, DenseNet, or MobileNet.

📹 Tutorial Video

Check out my Tutorial Video for a step-by-step guide!

📂 Installation

Clone the repository:

git clone https://github.com/DogukanUrker/Shakespeare.git
cd Shakespeare

Run setup.py to create necessary folders and install required modules:

python3 setup.py

⚡️ Getting Started

1. 💿 Prepare Your Data:

   • Place your images in the following default directories:
     • data/object/: Contains images of objects you want to classify.
     • data/notObject/: Contains images that do not belong to the object class.
     • data/test/: Additional images for testing the trained model.

2. ⚙️ Configure the Model:

   • Open defaults.py and set MODEL_NAME to the desired model architecture (resnet, efficientnet, vgg, densenet, mobilenet).

3. 🏋️ Train the Model:

   • Start training and create a .pkl file by running:

     python3 main.py

4. 📝 Testing:

   • After training, evaluate your model's performance using:

     python3 test.py

🎨 Customization

• Modify defaults.py or train.py to adjust hyperparameters or experiment with different model architectures.
• Extend functionality by adding preprocessing steps or data augmentation in train.py.

💞 Contributing

Contributions are welcome! If you have suggestions, bug reports, or want to add features, please submit a pull request.

⚖️ License

This project is licensed under the MIT License - see the LICENSE file for details.

🌟 Support Our Project!

If you find Shakespeare helpful in your projects and would like to support its development and maintenance, you can contribute to my project's sustainability.

How You Can Help:

• Give Us a Star on GitHub: Show your appreciation by starring my GitHub repository. It helps me reach more developers like you!

• Visit my donation page to choose from multiple platforms and support my work directly.

Your support means a lot and helps us continue improving Shakespeare for the community. Thank you for considering!

Created by Doğukan Ürker