🇬🇧 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.
Check out my Tutorial Video for a step-by-step guide!
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
-
💿 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.
- Place your images in the following default directories:
-
⚙️ Configure the Model:
- Open
defaults.py
and setMODEL_NAME
to the desired model architecture (resnet
,efficientnet
,vgg
,densenet
,mobilenet
).
- Open
-
🏋️ Train the Model:
- Start training and create a
.pkl
file by running:python3 main.py
- Start training and create a
-
📝 Testing:
- After training, evaluate your model's performance using:
python3 test.py
- After training, evaluate your model's performance using:
- Modify
defaults.py
ortrain.py
to adjust hyperparameters or experiment with different model architectures. - Extend functionality by adding preprocessing steps or data augmentation in
train.py
.
Contributions are welcome! If you have suggestions, bug reports, or want to add features, please submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
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.
-
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