Enhance README.md with Quick Start guide, updated dataset statistics, and citation information

README.md

@@ -62,7 +62,6 @@ This dataset is designed for **Few-Shot Learning (FSL)** research in product cla
 - **Class Numbers**: Non-continuous (some class numbers may be missing)
 - **Image Format**: PNG
 - **Typical Image Size**: 50-100 KB per image
-- **Average Images per Class**: 366.6 (279,747 ÷ 763)
 - **Compressed Archive Size**: ~9.9 GB (data.tzst)
 
 ## Dataset Structure
@@ -84,6 +83,38 @@ data.tzst
 
 **Note**: Class numbers are not continuous. For example, you might have class_0, class_2, class_5, etc., but not class_1, class_3, class_4. The total number of classes is 763.
 
+## Quick Start
+
+Get started with the FSL Product Classification dataset in just a few steps:
+
+```python
+from datasets import Dataset
+import os
+from tzst import extract_archive
+
+# 1. Extract the dataset
+extract_archive("data.tzst", "extracted_data/")
+
+# 2. Load a few samples
+data_dir = "extracted_data"
+samples = []
+for class_dir in sorted(os.listdir(data_dir))[:3]:  # First 3 classes
+    if class_dir.startswith("class_"):
+        class_path = os.path.join(data_dir, class_dir)
+        for img_file in os.listdir(class_path)[:5]:  # First 5 images
+            if img_file.endswith('.png'):
+                samples.append({
+                    'image': os.path.join(class_path, img_file),
+                    'label': int(class_dir.split("_")[1]),
+                    'class_name': class_dir,
+                    'image_id': img_file.replace('.png', '')
+                })
+
+print(f"Loaded {len(samples)} sample images from 3 classes")
+```
+
+For complete setup and advanced usage, see the sections below.
+
 ## Usage
 
 ## Installation and Setup
@@ -93,7 +124,12 @@ data.tzst
 ```bash
 # Create a new virtual environment (recommended)
 python -m venv fsl-env
-source fsl-env/bin/activate  # On Windows: fsl-env\Scripts\activate
+
+# Activate virtual environment
+# On Windows:
+fsl-env\Scripts\activate
+# On macOS/Linux:
+# source fsl-env/bin/activate
 
 # Install core dependencies
 pip install datasets tzst pillow
@@ -1052,6 +1088,12 @@ for i, (support_data, query_data) in enumerate(dataloader):
 3. **Use data augmentation**: Improve few-shot performance with transforms
 4. **Cache preprocessed data**: Save processed episodes to disk for faster iteration
 
+## Citation
+
+If you use this dataset in your research, please cite it as shown on the Hugging Face dataset page:
+
+<https://huggingface.co/datasets/xixu-me/fsl-product-classification?doi=true>
+
 ## License
 
 This dataset is released under the MIT License. See the [LICENSE file](LICENSE) for details.