anmol2059 / svdd2024 Goto Github PK

1. Prepare the Dataset:

   • Follow the instructions in the SVDD Challenge CtrSVDD_Utils Repository to manage the dataset.

2. Organize Dataset Structure:

   • Ensure your main directory has the following structure:

     Datasets/
     ├── dev/
     ├── train/
     ├── eval/
     ├── dev.txt
     └── eval.txt
     

3. Set Up the Environment:

   • Create a conda environment using the provided requirements.txt file:

     conda create --name your_env_name --file requirements.txt
     conda activate your_env_name

4. Run Training:

   • Execute the training script by specifying the base directory of the dataset:

     python train.py --base_dir {path_to_Datasets_folder}

   • Additional arguments can be added, such as --algo for the rawboost algorithm:

     python train.py --base_dir {path_to_Datasets_folder} --algo {algorithm_choice}

   • To change the model, modify the model selection directly in the train.py script header.

5. Run Evaluation:

   • Execute the evaluation script by specifying the base directory of the dataset:

     python eval.py --base_dir {path_to_Datasets_folder}

• Custom Arguments:

  • You can customize various parameters through command-line arguments as needed.
  • Example:

    python train.py --base_dir {path_to_Datasets_folder} --batch_size 64 --epochs 50

• Changing the Model:

  • To use a different model, edit the model import and instantiation in the train.py file.

For further details, refer to the code comments within the scripts.