Update README.md

README.md

@@ -10,46 +10,46 @@ Liang, Y., Li, X., Tsai, B., Chen, Q., & Jafari, N. (2023). V-FloodNet: A video
 
 ## Prerequisites
 
-Install Conda on your Ubuntu 24.04 with default version of Python and Nvidia GPU.
+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
-```
+    ```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
-```
+    ```sh
+    curl -O https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Linux-x86_64.sh
+    ```
 
-3. Located the downloaded file and install it:
-```sh
-bash 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://github.com/rezasalatin/V-BeachNet.git
-cd V-BeachNet
-```
+    ```sh
+    git clone https://huggingface.com/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 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 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 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 output directory.
+    ```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.