Update README.md

README.md

@@ -35,6 +35,60 @@ This dataset card aims to describe the datasets used in the Cloud-Adapter, a col
 
 ## Uses
 
+```python
+# Step 1: Install the datasets library
+# Ensure you have the `datasets` library installed
+# You can install it using pip if it's not already installed:
+# pip install datasets
+
+from datasets import load_dataset
+from PIL import Image
+
+# Step 2: Load the Cloud-Adapter dataset
+# Replace "XavierJiezou/Cloud-Adapter" with the dataset repository name on Hugging Face
+dataset = load_dataset("XavierJiezou/Cloud-Adapter")
+
+# Step 3: Explore the dataset splits
+# The dataset contains three splits: "train", "val", and "test"
+print("Available splits:", dataset.keys())
+
+# Step 4: Access individual examples
+# Each example contains an image and a corresponding annotation (segmentation mask)
+train_data = dataset["train"]
+
+# View the number of samples in the training set
+print("Number of training samples:", len(train_data))
+
+# Step 5: Access a single data sample
+# Each data sample has two keys: "image" and "annotation"
+sample = train_data[0]
+
+# Step 6: Display the image and annotation
+# Use PIL to open and display the image and annotation
+image = sample["image"]
+annotation = sample["annotation"]
+
+# Display the image
+print("Displaying the image...")
+image.show()
+
+# Display the annotation
+print("Displaying the segmentation mask...")
+annotation.show()
+
+# Step 7: Use in a machine learning pipeline
+# You can integrate this dataset into your ML pipeline by iterating over the splits
+for sample in train_data:
+    image = sample["image"]
+    annotation = sample["annotation"]
+    # Process or feed `image` and `annotation` into your ML model here
+
+# Additional Info: Dataset splits
+# - dataset["train"]: Training split
+# - dataset["val"]: Validation split
+# - dataset["test"]: Testing split
+```
+
 ## Dataset Structure
 
 The dataset contains the following splits: