README.md

eye_segmentation

To perform eye segmentation using a Colab notebook with datasets stored in Google Drive, follow these steps:

Step 1: Prepare the Dataset and Colab File

Organize the Data:

Create a folder named DRIVE on your local machine. Inside the DRIVE folder, create two subfolders: train_dataset and test_dataset. Place your training images in the train_dataset folder and your testing images in the test_dataset folder.

Prepare the Colab File:

Create a Colab notebook (.ipynb file) or download an existing one designed for eye segmentation. Ensure the notebook includes code to load data, build and train a segmentation model, and evaluate the results.

Step 2: Upload Files to Google Drive

Upload the DRIVE Folder:

Open Google Drive. Drag and drop the DRIVE folder (which contains the train_dataset and test_dataset subfolders) into your Google Drive.

Upload the Colab File:

Upload the Colab notebook file to your Google Drive.

Step 3: Run the Colab File

Open the Colab Notebook:

Navigate to the Colab notebook in your Google Drive. Right-click the notebook file and select "Open with" > "Google Colaboratory."

Mount Google Drive:

The first step in your Colab notebook should be to mount Google Drive so that the notebook can access your datasets. Use the following code snippet in your notebook to mount Google Drive: python Copy code from google.colab import drive drive.mount('/content/drive') Navigate to the DRIVE Folder:

After mounting, set the path to your DRIVE folder in the notebook. Here’s an example: python Copy code import os data_dir = '/content/drive/My Drive/DRIVE/' train_dir = os.path.join(data_dir, 'train_dataset') test_dir = os.path.join(data_dir, 'test_dataset') Run the Colab Notebook:

Execute the cells in your notebook sequentially to train and evaluate your eye segmentation model using the datasets stored in Google Drive. By following these steps, you’ll be able to organize your data, upload it to Google Drive, and run your Colab notebook for eye segmentation.