Update README.md

README.md

@@ -11,7 +11,7 @@ tags:
 - Cloud
 ---
 
-# Cloud-Adapter-Datasets
+# KTDA-Datasets
 
 This dataset card aims to describe the datasets used in the [KTDA](https://xavierjiezou.github.io/KTDA/), Knowledge Transfer and Domain Adaptation for
 Fine-Grained Remote Sensing Image Segmentation
@@ -34,67 +34,6 @@ unzip grass.zip -d grass
 unzip cloud.zip -d l8_biome
 ```
 
-## Example
-
-```python
-import os
-import zipfile
-from huggingface_hub import hf_hub_download
-
-# Define the dataset repository
-repo_id = "XavierJiezou/ktda-datasets"
-# Select the zip file of the dataset to download
-zip_files = [
-    "grass.zip",
-    # "cloud.zip",
-]
-
-# Define a directory to extract the datasets
-output_dir = "cloud_adapter_paper_data"
-
-# Ensure the output directory exists
-os.makedirs(output_dir, exist_ok=True)
-
-# Step 1: Download and extract each ZIP file
-for zip_file in zip_files:
-    print(f"Downloading {zip_file}...")
-    # Download the ZIP file from Hugging Face Hub
-    zip_path = hf_hub_download(repo_id=repo_id, filename=zip_file, repo_type="dataset")
-    
-    # Extract the ZIP file
-    extract_path = os.path.join(output_dir, zip_file.replace(".zip", ""))
-    with zipfile.ZipFile(zip_path, "r") as zip_ref:
-        print(f"Extracting {zip_file} to {extract_path}...")
-        zip_ref.extractall(extract_path)
-
-# Step 2: Explore the extracted datasets
-# Example: Load and display the contents of the "hrc_whu" dataset
-dataset_path = os.path.join(output_dir, "hrc_whu")
-train_images_path = os.path.join(dataset_path, "img_dir", "train")
-train_annotations_path = os.path.join(dataset_path, "ann_dir", "train")
-
-# Display some files in the training set
-print("Training Images:", os.listdir(train_images_path)[:5])
-print("Training Annotations:", os.listdir(train_annotations_path)[:5])
-
-# Example: Load and display an image and its annotation
-from PIL import Image
-
-# Load an example image and annotation
-image_path = os.path.join(train_images_path, os.listdir(train_images_path)[0])
-annotation_path = os.path.join(train_annotations_path, os.listdir(train_annotations_path)[0])
-
-# Open and display the image
-image = Image.open(image_path)
-annotation = Image.open(annotation_path)
-
-print("Displaying the image...")
-image.show()
-
-print("Displaying the annotation...")
-annotation.show()
-```
-
 ## Source Data
 
 - l8_biome: https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data
@@ -102,7 +41,6 @@ annotation.show()
 ## Citation
 
 ```bib
-
 @article{l8_biome,
     title = {Cloud detection algorithm comparison and validation for operational Landsat data products},
     journal = {Remote Sensing of Environment},