Key Point Prediction using 5D Point Cloud Data

This repository contains the code for training and testing a model to predict keypoints using 5D point cloud data. The original code referenced can be found here: (https://github.com/SizheAn/MARS/tree/main)

Prerequisites

Before running the code, make sure you have the following dependencies installed:

• Python 3.7
• NumPy
• Pandas
• Scikit-learn
• TensorFlow 2.2.0
• [Keras] (https://keras.io/) 2.3.0
• Matplotlib

I would advise you to use a virtual enviroment through conda

```bash
conda create -n 'enviroment_name' python=3.7
```

Training

To train the model, follow these steps:

1. Clone this repository:

   git clone https://github.com/Special256/RF-HAR.git

2. Navigate to the project directory:

   cd RF-HAR

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the training script:

   python MARS_model.py --dataset <dataset_path> --model_dir <path_to_save_the_model> --save_path <save_path>

   This will start the training process and save the trained model weights to a file.

Testing

To test the trained model, follow these steps:

1. Make sure you have completed the training steps mentioned above.

2. Run the test script:

   python test.py --dataset <dataset_path> --model_dir <model_dir_path> --save_path <save_path>

   This will load the trained model and evaluate its performance on the test dataset.

License

This project is licensed under the MIT License.