Master thesis (xlapsa00)

This is the accompanying python script for master thesis at Brno university of technology, Faculty of information technologies that includes a utility for running specific models with different settings.

Setup

This project requires Python 3.8. After installing Python, you can install the project's dependencies using pip.

Here are the step by step instructions to setup the project:

1. Clone the repository:

   git clone https://github.com/TomasLapsansky/Master-thesis.git
   cd Master-thesis

2. Create a virtual environment (optional but recommended):

   python3 -m venv env
   source env/bin/activate

3. Install the requirements:

   pip install -r requirements.txt

Usage

After setting up the project, you can run the script with different command-line arguments.

Here's the list of the arguments:

• -m, --training_model: Model name (case sensitive). This is a required argument.
• -d, --dataset: Dataset name (case sensitive).
• -e, --eval: Set if execute evaluation.
• -r, --dropout: Set dropout rate. Default is 0.5.
• -t, --trained: Use pre-trained model. Default is False.
• -f, --frozen: Freeze layers of base model until a specified layer.
• --type: Set type of efficient net.
• --lr, --learning_rate: Set learning rate for model. Default is 0.0001.
• -c, --checkpoint: Path to loaded checkpoint.
• -p, --print: Path to image.

Here is an example of how to run the script:

python main.py -m efficientdet --type L -d celeb-df -t

The repository also contains a preprocessing folder, where the scripts for dataset processing are located.

License

This project is licensed under the terms of the MIT license.