Update README.md

README.md

@@ -5,4 +5,129 @@ task_categories:
 - feature-extraction
 language:
 - en
----
+---
+
+# **Office-Home-LDS Dataset**
+
+The Office-Home-LDS dataset is constructed by introducing label skew on top of the domain skew present in the original Office-Home dataset. <br>
+The goal is to create a more challenging and realistic dataset that simultaneously exhibits both label skew and domain skew.
+
+## 📂 **Huggingface Dataset Structure**
+The dataset is organized as follows:
+
+```text
+Office-Home-LDS/
+├── data/ 
+│   └── Office-Home.zip        # Original raw dataset (compressed)
+├── new_dataset/               # Processed datasets based on different settings
+│   ├── Office-Home-0.1.zip    # Split with Dirichlet α = 0.1 (compressed)
+│   ├── Office-Home-0.5.zip    # Split with Dirichlet α = 0.5 (compressed)
+│   └── Office-Home-0.05.zip   # Split with Dirichlet α = 0.05 (compressed)
+├── Dataset-Office-Home-LDS.py # Python script for processing and splitting Original raw dataset
+└── README.md                  # Project documentation
+```
+
+## 📥 **Extract Files**
+After downloading the dataset, you can extract it using the following commands:
+
+### 🔹 Linux/macOS:
+```bash
+unzip data/Office-Home.zip -d ./data/Office-Home
+unzip new_dataset/Office-Home-0.1.zip -d ./new_dataset/Office-Home-0.1
+unzip new_dataset/Office-Home-0.5.zip -d ./new_dataset/Office-Home-0.5
+unzip new_dataset/Office-Home-0.05.zip -d ./new_dataset/Office-Home-0.05
+```
+
+### 🔹 Windows:
+#### 🟦 PowerShell Method:
+```bash
+  # Extract the original dataset
+  Expand-Archive -Path data/Office-Home.zip -DestinationPath ./data/Office-Home
+
+  # Extract processed datasets
+  Expand-Archive -Path new_dataset/Office-Home-0.1.zip -DestinationPath ./new_dataset/Office-Home-0.1
+  Expand-Archive -Path new_dataset/Office-Home-0.5.zip -DestinationPath ./new_dataset/Office-Home-0.5
+  Expand-Archive -Path new_dataset/Office-Home-0.05.zip -DestinationPath ./new_dataset/Office-Home-0.05
+```
+#### 🟦 Python Method:  
+```bash
+  import zipfile
+  import os
+  
+  # Create target directories if they don't exist
+  os.makedirs('./data/Office-Home', exist_ok=True)
+  os.makedirs('./new_dataset/Office-Home-0.1', exist_ok=True)
+  os.makedirs('./new_dataset/Office-Home-0.5', exist_ok=True)
+  os.makedirs('./new_dataset/Office-Home-0.05', exist_ok=True)
+  
+  # Extract zip files
+  with zipfile.ZipFile('data/Office-Home.zip', 'r') as zip_ref:
+      zip_ref.extractall('./data/Office-Home')
+  
+  with zipfile.ZipFile('new_dataset/Office-Home-0.1.zip', 'r') as zip_ref:
+      zip_ref.extractall('./new_dataset/Office-Home-0.1')
+  
+  with zipfile.ZipFile('new_dataset/Office-Home-0.5.zip', 'r') as zip_ref:
+      zip_ref.extractall('./new_dataset/Office-Home-0.5')
+  
+  with zipfile.ZipFile('new_dataset/Office-Home-0.05.zip', 'r') as zip_ref:
+      zip_ref.extractall('./new_dataset/Office-Home-0.05')
+```
+## 📂 **Extracted File Structure**
+The dataset is organized as follows:
+
+```text
+```text
+Office-Home-LDS/
+├── data/ 
+│   └── Office-Home/                  # Original dataset
+│       ├── Art/
+│       ├── Clipart/
+│       ├── Product/
+│       └── Real World/
+├── new_dataset/ 
+│   ├── Office-Home-0.1/              # Split with α = 0.1
+│   │   ├── Art/                      # Domain: Art
+│   │   │   ├── client/               # Client-level split images
+│   │   │   ├── train/                # Train set images
+│   │   │   └── test/                 # Test set images
+│   │   ├── Clipart/                  # Domain: Clipart
+│   │   │   ├── client/               # Client-level split
+│   │   │   ├── train/                # Train set images
+│   │   │   └── test/                 # Test set images
+│   │   ├── Product/                  # Domain: Product
+│   │   │   ├── client/               # Client-level split
+│   │   │   ├── train/                # Train set images
+│   │   │   └── test/                 # Test set images
+│   │   ├── Real World/               # Domain: Real_World
+│   │   │   ├── client/               # Client-level split
+│   │   │   ├── train/                # Train set images
+│   │   │   └── test/                 # Test set images
+│   │   ├── output_indices/           # Split information and indices
+│   │   │   ├── Art/                  # Indices for Art domain
+│   │   │   │   ├── class_indices.npy              # Class-level indices
+│   │   │   │   ├── client_client_indices.npy      # Client split indices
+│   │   │   │   ├── test_test_indices.npy          # Test set indices
+│   │   │   │   └── train_train_indices.npy        # Train set indices
+│   │   │   ├── Clipart/               # Indices for Clipart domain
+│   │   │   │   ├── class_indices.npy
+│   │   │   │   ├── client_client_indices.npy
+│   │   │   │   ├── test_test_indices.npy
+│   │   │   │   └── train_train_indices.npy
+│   │   │   ├── Product/               # Indices for Product domain
+│   │   │   │   ├── class_indices.npy
+│   │   │   │   ├── client_client_indices.npy
+│   │   │   │   ├── test_test_indices.npy
+│   │   │   │   └── train_train_indices.npy
+│   │   │   ├── Real World/            # Indices for Real_World domain
+│   │   │   │   ├── class_indices.npy
+│   │   │   │   ├── client_client_indices.npy
+│   │   │   │   ├── test_test_indices.npy
+│   │   │   │   └── train_train_indices.npy
+│   │   │   └── combined_class_allocation.txt     # Global class allocation info
+│   ├── Office-Home-0.5/              # Split with α = 0.5 (similar structure)
+│   └── Office-Home-0.05/             # Split with α = 0.05 (similar structure)
+├── Dataset-Office-Home-LDS.py         # Python script for processing and splitting Original raw dataset
+└── README.md                          # Project documentation
+
+```