https://github.com/dogukanurker/shakespeare
Lightweight PyTorch project designed to help you train a model to visually identify objects using popular architectures.
https://github.com/dogukanurker/shakespeare
machine-learning pytorch
Last synced: 2 months ago
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Lightweight PyTorch project designed to help you train a model to visually identify objects using popular architectures.
- Host: GitHub
- URL: https://github.com/dogukanurker/shakespeare
- Owner: DogukanUrker
- License: mit
- Created: 2024-07-01T13:23:54.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-07-11T22:35:47.000Z (11 months ago)
- Last Synced: 2025-03-27T02:09:05.787Z (3 months ago)
- Topics: machine-learning, pytorch
- Language: Python
- Homepage: https://dogukanurker.com/shakespeare
- Size: 20.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ๐ Shakespeare: Unleashing the Power of PyTorch for Object Identification
**๐ฌ๐ง English** | [๐น๐ท Tรผrkรงe](README_TR.md)
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.
## ๐น Tutorial Video
Check out my [Tutorial Video](https://youtu.be/8xC1-un_sjA) for a step-by-step guide!
## ๐ Installation
Clone the repository:
```bash
git clone https://github.com/DogukanUrker/Shakespeare.git
cd Shakespeare
```
Run setup.py to create necessary folders and install required modules:
```bash
python3 setup.py
```
## โก๏ธ Getting Started
1. ๐ฟ **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. โ๏ธ **Configure the Model**:
- Open `defaults.py` and set `MODEL_NAME` to the desired model architecture (`resnet`, `efficientnet`, `vgg`, `densenet`, `mobilenet`).
3. ๐๏ธ **Train the Model**:
- Start training and create a `.pkl` file by running:
```bash
python3 main.py
```
4. ๐ **Testing**:
- After training, evaluate your model's performance using:
```bash
python3 test.py
```
## ๐จ 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`.
## ๐ Contributing
Contributions are welcome! If you have suggestions, bug reports, or want to add features, please submit a pull request.
## โ๏ธ License
This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.
## ๐ 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](https://github.com/DogukanUrker/Shakespeare). It helps me reach more developers like you!
- **Visit my [donation page](https://dogukanurker.com/donate)** 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 [Doฤukan รrker](https://dogukanurker.com)