Update README.md

README.md

@@ -5,4 +5,60 @@ language:
 pipeline_tag: image-segmentation
 tags:
 - climate
----
+---
+
+# 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 is built upon V-FloodNet.
+
+**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.
+
+**V-FloodNet paper:**  
+Liang, Y., Li, X., Tsai, B., Chen, Q., & Jafari, N. (2023). V-FloodNet: A video segmentation system for urban flood detection and quantification. Environmental Modelling & Software, 160, 105586.
+
+## 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.