README.md

---
tags:
- image-segmentation
- pytorch
- deep-learning
- computer-vision
- climate
license: mit
language:
- en
pipeline_tag: image-classification
---

# V-BeachNet

This repository contains the official PyTorch implementation for the paper "A New Framework for Quantifying Alongshore Variability of Swash Motion Using Fully Convolutional Networks."

**V-BeachNet paper:**  
Salatin, R., Chen, Q., Raubenheimer, B., Elgar, S., Gorrell, L., & Li, X. (2024). A New Framework for Quantifying Alongshore Variability of Swash Motion Using Fully Convolutional Networks. Coastal Engineering, 104542. doi: [10.1016/j.coastaleng.2024.104542](https://doi.org/10.1016/j.coastaleng.2024.104542).

## Prerequisites

This code is tested on a newly installed Ubuntu 24.04 with default version of Python and Nvidia GPU.

1. Install Anaconda prerequisite (Can also be accessed from [here](https://docs.anaconda.com/anaconda/install/linux/)):
    ```sh
    sudo apt update && \
    sudo apt install libgl1-mesa-dri libegl1 libglu1-mesa libxrandr2 libxss1 libxcursor1 libxcomposite1 libasound2-data libasound2-plugins libxi6 libxtst6
    ```

2. Download Anaconda3:
    ```sh
    curl -O https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Linux-x86_64.sh
    ```

3. Locate the downloaded file and install it:
    ```sh
    bash Anaconda3-2024.06-1-Linux-x86_64.sh
    ```

## Steps

1. Clone this repository and change directory:
    ```sh
    git clone https://huggingface.co/rezasalatin/V-BeachNet.git
    cd V-BeachNet
    ```

2. Create the virtual environment with the requirements:
    ```sh
    conda env create -f environment.yml
    conda activate vbeach
    ```

3. Visit the "Training_Station" folder and copy your manually segmented (using [labelme](https://github.com/labelmeai/labelme)) dataset to this directory. Open the following file to change any of the variables and save it. Then execute it to train the model:
    ```sh
    ./train_video_seg.sh
    ```
    Access your trained model from the `log/` directory.

4. Visit the "Testing_Station" folder and copy your data to this directory. Open the following file to change any of the variables (especially the model path from the `log/` folder) and save it. Then execute it to test the model:
    ```sh
    ./test_video_seg.sh
    ```
    Access your segmented data from the `output` directory.