Add files using upload-large-folder tool

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual environments
+.env
+.venv
+env/
+venv/
+ENV/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+*~
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# Generated files
+samples_visualization.png
+shape_types.png
+color_distribution.png
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+
+# Agent instructions (internal use only)
+AGENT_INSTRUCTIONS.md

AGENT_INSTRUCTIONS.md

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+# Shape Polygons Dataset - コード書き換え指示書
+
+このドキュメントは、既存のコードをShape Polygons Datasetを使用するように書き換えるための指示書です。
+
+---
+
+## データセット概要
+
+| 項目 | 内容 |
+|------|------|
+| 名前 | Shape Polygons Dataset |
+| 総画像数 | 70,000枚（train: 60,000 / test: 10,000） |
+| 画像形式 | PNG |
+| 画像サイズ | 小さい正方形画像（黒背景に色付きポリゴン） |
+| クラス数 | 6（3〜8角形） |
+| メタデータ | CSV形式 |
+
+---
+
+## ディレクトリ構造
+
+```
+shape-polygons-dataset/
+├── train/
+│   ├── images/
+│   │   ├── 00001.png
+│   │   ├── 00002.png
+│   │   └── ... (60,000 images)
+│   └── metadata.csv
+├── test/
+│   ├── images/
+│   │   ├── 00001.png
+│   │   └── ... (10,000 images)
+│   └── metadata.csv
+└── example.py
+```
+
+---
+
+## メタデータ (metadata.csv) のカラム
+
+| カラム名 | 型 | 説明 | 範囲 |
+|----------|------|------|------|
+| `filename` | string | 画像ファイル名 | "00001.png" 形式 |
+| `size` | float | ポリゴンの相対サイズ | 0.0 〜 1.0 |
+| `angle` | float | 回転角度（度） | 0.0 〜 360.0 |
+| `vertices` | int | **頂点数（クラスラベル）** | 3, 4, 5, 6, 7, 8 |
+| `center_x` | float | 中心X座標（正規化） | 0.0 〜 1.0 |
+| `center_y` | float | 中心Y座標（正規化） | 0.0 〜 1.0 |
+| `color_r` | float | 赤色成分 | 0.0 〜 1.0 |
+| `color_g` | float | 緑色成分 | 0.0 〜 1.0 |
+| `color_b` | float | 青色成分 | 0.0 〜 1.0 |
+
+---
+
+## タスク別の使い方
+
+### 1. 画像分類（Classification）
+
+**目的**: 画像から頂点数（3〜8）を予測する
+
+```python
+# クラスラベルの変換
+# vertices: 3, 4, 5, 6, 7, 8 → label: 0, 1, 2, 3, 4, 5
+label = row["vertices"] - 3
+
+# クラス数
+num_classes = 6
+
+# クラス名
+class_names = ["Triangle", "Quadrilateral", "Pentagon", "Hexagon", "Heptagon", "Octagon"]
+```
+
+### 2. 回帰（Regression）
+
+**目的**: サイズ、角度、位置などの連続値を予測
+
+```python
+# 単一値回帰
+target = row["size"]  # または row["angle"]
+
+# 複数値回帰
+target = [row["center_x"], row["center_y"]]
+
+# 色の回帰
+target = [row["color_r"], row["color_g"], row["color_b"]]
+```
+
+### 3. マルチタスク学習
+
+```python
+targets = {
+    "vertices": row["vertices"] - 3,  # 分類
+    "size": row["size"],               # 回帰
+    "angle": row["angle"],             # 回帰
+    "center": [row["center_x"], row["center_y"]],  # 回帰
+    "color": [row["color_r"], row["color_g"], row["color_b"]]  # 回帰
+}
+```
+
+---
+
+## コード書き換えパターン
+
+### パターン A: 既存のImageFolderベースのコードを変換
+
+**変換前（ImageFolder）:**
+```python
+from torchvision.datasets import ImageFolder
+dataset = ImageFolder("data/train", transform=transform)
+```
+
+**変換後:**
+```python
+import pandas as pd
+from torch.utils.data import Dataset
+from PIL import Image
+import os
+
+class ShapePolygonsDataset(Dataset):
+    def __init__(self, root_dir, split="train", transform=None):
+        self.root_dir = root_dir
+        self.split = split
+        self.transform = transform
+        self.metadata = pd.read_csv(os.path.join(root_dir, split, "metadata.csv"))
+    
+    def __len__(self):
+        return len(self.metadata)
+    
+    def __getitem__(self, idx):
+        row = self.metadata.iloc[idx]
+        img_path = os.path.join(self.root_dir, self.split, "images", row["filename"])
+        image = Image.open(img_path).convert("RGB")
+        
+        if self.transform:
+            image = self.transform(image)
+        
+        label = row["vertices"] - 3  # 0-5 for 3-8 vertices
+        return image, label
+
+dataset = ShapePolygonsDataset("path/to/dataset", split="train", transform=transform)
+```
+
+### パターン B: Hugging Face Datasetsを使用
+
+```python
+from datasets import load_dataset
+
+# Hugging Face Hubからロード
+dataset = load_dataset("kimura-koya/shape-polygons-dataset")
+
+# ローカルからロード（ImageFolder形式として）
+dataset = load_dataset("imagefolder", data_dir="path/to/dataset")
+
+# アクセス
+train_data = dataset["train"]
+test_data = dataset["test"]
+```
+
+### パターン C: シンプルなPandas + PILでのロード
+
+```python
+import pandas as pd
+from PIL import Image
+import os
+
+# メタデータ読み込み
+train_df = pd.read_csv("train/metadata.csv")
+test_df = pd.read_csv("test/metadata.csv")
+
+# 画像読み込み関数
+def load_image(split, filename):
+    return Image.open(os.path.join(split, "images", filename))
+
+# 使用例
+for idx, row in train_df.iterrows():
+    img = load_image("train", row["filename"])
+    label = row["vertices"]
+    # 処理...
+```
+
+---
+
+## よくある変換パターン
+
+### MNIST/CIFAR → Shape Polygons
+
+| MNIST/CIFAR | Shape Polygons |
+|-------------|----------------|
+| `torchvision.datasets.MNIST(...)` | `ShapePolygonsDataset(root, split="train")` |
+| `dataset.classes` | `["Triangle", "Quadrilateral", "Pentagon", "Hexagon", "Heptagon", "Octagon"]` |
+| `len(dataset.classes)` = 10 | `num_classes = 6` |
+| グレースケール/RGB | RGB（黒背景にカラーポリゴン） |
+
+### 設定変更のチェックリスト
+
+書き換え時に確認すべき項目：
+
+- [ ] `num_classes` を 6 に変更
+- [ ] データセットのパスを変更
+- [ ] 画像の前処理（Normalize等）を確認
+- [ ] train/test/validation の分割方法を確認
+- [ ] クラス名のリストを更新
+- [ ] 評価指標が分類タスクに適切か確認
+
+---
+
+## データセット統計
+
+参考情報として、各クラスの分布：
+
+```
+Train Set (60,000 images):
+  3 vertices (Triangle):     約10,000枚
+  4 vertices (Quadrilateral): 約10,000枚
+  5 vertices (Pentagon):      約10,000枚
+  6 vertices (Hexagon):       約10,000枚
+  7 vertices (Heptagon):      約10,000枚
+  8 vertices (Octagon):       約10,000枚
+```
+
+※ ほぼ均等に分布
+
+---
+
+## 注意事項
+
+1. **画像サイズ**: 小さい画像なので、大きなモデルではリサイズが必要な場合あり
+2. **背景**: 黒背景（RGB: 0, 0, 0）にカラーポリゴン
+3. **合成データ**: すべてプログラムで生成された合成データ
+4. **ファイル名**: 5桁のゼロパディング（00001.png 〜 60000.png）
+
+---
+
+## サンプルコード（そのまま使用可能）
+
+`example.py` に以下の機能があります：
+
+- `ShapePolygonsDataset` クラス（PyTorch Dataset）
+- `load_dataset()` 関数（Pandas DataFrame返却）
+- `load_image()` 関数（PIL Image返却）
+- 可視化関数各種
+
+```python
+# example.py をインポートして使用
+from example import ShapePolygonsDataset, load_dataset, load_image
+```
+

LICENSE

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+MIT License
+
+Copyright (c) 2024
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+

README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+  - image-classification
+  - object-detection
+language:
+  - en
+tags:
+  - computer-vision
+  - polygons
+  - shapes
+  - synthetic-data
+  - image-generation
+pretty_name: Shape Polygons Dataset
+size_categories:
+  - 10K<n<100K
+---
+
+# Shape Polygons Dataset
+
+A synthetic dataset containing 70,000 images of various colored polygons (triangles to octagons) rendered on black backgrounds.
+
+## Dataset Description
+
+This dataset consists of programmatically generated polygon images with full metadata about each shape's properties. It's designed for tasks such as:
+
+- **Shape Classification**: Classify polygons by number of vertices (3-8)
+- **Regression Tasks**: Predict shape properties (size, angle, position, color)
+- **Object Detection**: Locate and identify shapes within images
+- **Generative Models**: Train models to generate geometric shapes
+
+### Dataset Statistics
+
+| Split | Number of Images |
+|-------|------------------|
+| Train | 60,000 |
+| Test  | 10,000 |
+| **Total** | **70,000** |
+
+### Shape Types
+
+The dataset includes 6 different polygon types:
+- **Triangle** (3 vertices)
+- **Quadrilateral** (4 vertices)
+- **Pentagon** (5 vertices)
+- **Hexagon** (6 vertices)
+- **Heptagon** (7 vertices)
+- **Octagon** (8 vertices)
+
+## Dataset Structure
+
+```
+shape-polygons-dataset/
+├── train/
+│   ├── images/
+│   │   ├── 00001.png
+│   │   ├── 00002.png
+│   │   └── ... (60,000 images)
+│   └── metadata.csv
+├── test/
+│   ├── images/
+│   │   ├── 00001.png
+│   │   ├── 00002.png
+│   │   └── ... (10,000 images)
+│   └── metadata.csv
+└── README.md
+```
+
+### Metadata Fields
+
+Each `metadata.csv` contains the following columns:
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `filename` | string | Image filename (e.g., "00001.png") |
+| `size` | float | Relative size of the polygon (0.0 - 1.0) |
+| `angle` | float | Rotation angle in degrees (0.0 - 360.0) |
+| `vertices` | int | Number of vertices (3-8) |
+| `center_x` | float | X-coordinate of center (0.0 - 1.0, normalized) |
+| `center_y` | float | Y-coordinate of center (0.0 - 1.0, normalized) |
+| `color_r` | float | Red color component (0.0 - 1.0) |
+| `color_g` | float | Green color component (0.0 - 1.0) |
+| `color_b` | float | Blue color component (0.0 - 1.0) |
+
+## Sample Images
+
+Here are some example images from the dataset:
+
+<div style="display: flex; gap: 10px; flex-wrap: wrap;">
+  <img src="train/images/00001.png" width="64" height="64" alt="Sample 1">
+  <img src="train/images/00003.png" width="64" height="64" alt="Sample 2">
+  <img src="train/images/00005.png" width="64" height="64" alt="Sample 3">
+  <img src="train/images/00016.png" width="64" height="64" alt="Sample 4">
+</div>
+
+## Usage
+
+### Loading with Hugging Face Datasets
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("your-username/shape-polygons-dataset")
+
+# Access train and test splits
+train_data = dataset["train"]
+test_data = dataset["test"]
+
+# Get a sample
+sample = train_data[0]
+print(f"Vertices: {sample['vertices']}, Size: {sample['size']:.2f}")
+```
+
+### Loading with Pandas
+
+```python
+import pandas as pd
+from PIL import Image
+import os
+
+# Load metadata
+train_metadata = pd.read_csv("train/metadata.csv")
+test_metadata = pd.read_csv("test/metadata.csv")
+
+# Load an image
+img_path = os.path.join("train/images", train_metadata.iloc[0]["filename"])
+image = Image.open(img_path)
+image.show()
+
+# Filter by number of vertices (e.g., triangles only)
+triangles = train_metadata[train_metadata["vertices"] == 3]
+print(f"Number of triangles: {len(triangles)}")
+```
+
+### PyTorch DataLoader Example
+
+```python
+import torch
+from torch.utils.data import Dataset, DataLoader
+from PIL import Image
+import pandas as pd
+import os
+
+class PolygonDataset(Dataset):
+    def __init__(self, root_dir, split="train", transform=None):
+        self.root_dir = root_dir
+        self.split = split
+        self.transform = transform
+        self.metadata = pd.read_csv(os.path.join(root_dir, split, "metadata.csv"))
+    
+    def __len__(self):
+        return len(self.metadata)
+    
+    def __getitem__(self, idx):
+        row = self.metadata.iloc[idx]
+        img_path = os.path.join(self.root_dir, self.split, "images", row["filename"])
+        image = Image.open(img_path).convert("RGB")
+        
+        if self.transform:
+            image = self.transform(image)
+        
+        # Number of vertices as classification label (0-5 for 3-8 vertices)
+        label = row["vertices"] - 3
+        
+        return image, label
+
+# Create dataset and dataloader
+dataset = PolygonDataset("path/to/dataset", split="train")
+dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
+```
+
+## Use Cases
+
+1. **Beginner-Friendly ML Projects**: Simple dataset for learning image classification
+2. **Shape Recognition Systems**: Training models to identify geometric shapes
+3. **Property Regression**: Predicting continuous values (size, angle, position)
+4. **Multi-Task Learning**: Combining classification and regression objectives
+5. **Data Augmentation Research**: Studying effects of synthetic data on model performance
+6. **Benchmark Dataset**: Evaluating new architectures on a controlled, balanced dataset
+
+## License
+
+This dataset is released under the [MIT License](LICENSE).
+
+## Citation
+
+If you use this dataset in your research, please cite it as:
+
+```bibtex
+@dataset{shape_polygons_dataset,
+  title={Shape Polygons Dataset},
+  year={2024},
+  url={https://huggingface.co/datasets/your-username/shape-polygons-dataset},
+  note={A synthetic dataset of 70,000 polygon images for computer vision tasks}
+}
+```
+
+## Contributing
+
+Contributions are welcome! Feel free to:
+- Report issues
+- Suggest improvements
+- Submit pull requests
+
+## Contact
+
+For questions or feedback, please open an issue on the repository.
+

example.py

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+"""
+Shape Polygons Dataset - Usage Examples
+
+This script demonstrates various ways to load and use the Shape Polygons Dataset.
+"""
+
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+from PIL import Image
+
+# =============================================================================
+# Basic Usage: Loading Metadata and Images
+# =============================================================================
+
+def load_dataset(data_dir=".", split="train"):
+    """Load metadata and return as pandas DataFrame."""
+    metadata_path = os.path.join(data_dir, split, "metadata.csv")
+    return pd.read_csv(metadata_path)
+
+
+def load_image(data_dir, split, filename):
+    """Load a single image from the dataset."""
+    img_path = os.path.join(data_dir, split, "images", filename)
+    return Image.open(img_path)
+
+
+# =============================================================================
+# Example 1: Explore Dataset Statistics
+# =============================================================================
+
+def explore_statistics(data_dir="."):
+    """Print dataset statistics."""
+    print("=" * 50)
+    print("Shape Polygons Dataset Statistics")
+    print("=" * 50)
+    
+    for split in ["train", "test"]:
+        df = load_dataset(data_dir, split)
+        print(f"\n{split.upper()} Split:")
+        print(f"  Total images: {len(df)}")
+        print(f"\n  Vertices distribution:")
+        for v in range(3, 9):
+            count = len(df[df["vertices"] == v])
+            print(f"    {v} vertices: {count} ({count/len(df)*100:.1f}%)")
+        
+        print(f"\n  Size statistics:")
+        print(f"    Min: {df['size'].min():.4f}")
+        print(f"    Max: {df['size'].max():.4f}")
+        print(f"    Mean: {df['size'].mean():.4f}")
+
+
+# =============================================================================
+# Example 2: Visualize Sample Images
+# =============================================================================
+
+def visualize_samples(data_dir=".", n_samples=12, split="train"):
+    """Visualize random samples from the dataset."""
+    df = load_dataset(data_dir, split)
+    samples = df.sample(n=min(n_samples, len(df)))
+    
+    n_cols = 4
+    n_rows = (len(samples) + n_cols - 1) // n_cols
+    
+    fig, axes = plt.subplots(n_rows, n_cols, figsize=(12, 3 * n_rows))
+    axes = axes.flatten() if n_samples > 1 else [axes]
+    
+    for idx, (_, row) in enumerate(samples.iterrows()):
+        img = load_image(data_dir, split, row["filename"])
+        axes[idx].imshow(img)
+        axes[idx].set_title(f"{row['vertices']} vertices\nsize={row['size']:.2f}")
+        axes[idx].axis("off")
+    
+    # Hide empty subplots
+    for idx in range(len(samples), len(axes)):
+        axes[idx].axis("off")
+    
+    plt.tight_layout()
+    plt.savefig("samples_visualization.png", dpi=150, bbox_inches="tight")
+    print(f"Saved visualization to 'samples_visualization.png'")
+    plt.show()
+
+
+# =============================================================================
+# Example 3: Visualize by Shape Type
+# =============================================================================
+
+def visualize_by_shape_type(data_dir=".", split="train"):
+    """Show one example of each shape type."""
+    df = load_dataset(data_dir, split)
+    shape_names = {
+        3: "Triangle",
+        4: "Quadrilateral",
+        5: "Pentagon",
+        6: "Hexagon",
+        7: "Heptagon",
+        8: "Octagon"
+    }
+    
+    fig, axes = plt.subplots(2, 3, figsize=(12, 8))
+    axes = axes.flatten()
+    
+    for idx, vertices in enumerate(range(3, 9)):
+        sample = df[df["vertices"] == vertices].iloc[0]
+        img = load_image(data_dir, split, sample["filename"])
+        axes[idx].imshow(img)
+        axes[idx].set_title(f"{shape_names[vertices]}\n({vertices} vertices)")
+        axes[idx].axis("off")
+    
+    plt.suptitle("Shape Types in Dataset", fontsize=14, fontweight="bold")
+    plt.tight_layout()
+    plt.savefig("shape_types.png", dpi=150, bbox_inches="tight")
+    print(f"Saved visualization to 'shape_types.png'")
+    plt.show()
+
+
+# =============================================================================
+# Example 4: PyTorch Dataset Class
+# =============================================================================
+
+# Optional imports for PyTorch functionality
+try:
+    import torch
+    from torch.utils.data import Dataset, DataLoader
+    from torchvision import transforms
+    PYTORCH_AVAILABLE = True
+except ImportError:
+    PYTORCH_AVAILABLE = False
+
+
+class ShapePolygonsDataset:
+    """PyTorch Dataset for Shape Polygons.
+    
+    Requires: torch, torchvision
+    """
+    
+    def __init__(self, root_dir, split="train", transform=None, task="classification"):
+        """
+        Args:
+            root_dir: Root directory of the dataset
+            split: "train" or "test"
+            transform: Optional torchvision transforms
+            task: "classification" for vertex count, "regression" for size prediction, "multi" for all properties
+        """
+        if not PYTORCH_AVAILABLE:
+            raise ImportError("PyTorch is required. Install with: pip install torch torchvision")
+        
+        self.root_dir = root_dir
+        self.split = split
+        self.transform = transform
+        self.task = task
+        self.metadata = pd.read_csv(os.path.join(root_dir, split, "metadata.csv"))
+        
+        if self.transform is None:
+            self.transform = transforms.Compose([
+                transforms.ToTensor(),
+                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+            ])
+    
+    def __len__(self):
+        return len(self.metadata)
+    
+    def __getitem__(self, idx):
+        row = self.metadata.iloc[idx]
+        img_path = os.path.join(self.root_dir, self.split, "images", row["filename"])
+        image = Image.open(img_path).convert("RGB")
+        
+        if self.transform:
+            image = self.transform(image)
+        
+        if self.task == "classification":
+            # Class label: 0-5 for 3-8 vertices
+            label = torch.tensor(row["vertices"] - 3, dtype=torch.long)
+        elif self.task == "regression":
+            # Predict size
+            label = torch.tensor(row["size"], dtype=torch.float32)
+        elif self.task == "multi":
+            # Multi-task: return all properties
+            label = {
+                "vertices": torch.tensor(row["vertices"] - 3, dtype=torch.long),
+                "size": torch.tensor(row["size"], dtype=torch.float32),
+                "angle": torch.tensor(row["angle"], dtype=torch.float32),
+                "center": torch.tensor([row["center_x"], row["center_y"]], dtype=torch.float32),
+                "color": torch.tensor([row["color_r"], row["color_g"], row["color_b"]], dtype=torch.float32)
+            }
+        else:
+            raise ValueError(f"Unknown task: {self.task}")
+        
+        return image, label
+
+
+def demo_pytorch_dataloader(data_dir="."):
+    """Demonstrate PyTorch DataLoader usage."""
+    if not PYTORCH_AVAILABLE:
+        print("PyTorch is not installed. Install with: pip install torch torchvision")
+        return
+    
+    print("Creating PyTorch Dataset and DataLoader...")
+    dataset = ShapePolygonsDataset(data_dir, split="train", task="classification")
+    dataloader = DataLoader(dataset, batch_size=32, shuffle=True, num_workers=0)
+    
+    # Get one batch
+    images, labels = next(iter(dataloader))
+    print(f"Batch shape: {images.shape}")
+    print(f"Labels shape: {labels.shape}")
+    print(f"Label values (vertices - 3): {labels[:10].tolist()}")
+    print(f"Actual vertex counts: {[l + 3 for l in labels[:10].tolist()]}")
+
+
+# =============================================================================
+# Example 5: Color Analysis
+# =============================================================================
+
+def analyze_colors(data_dir=".", split="train"):
+    """Analyze color distribution in the dataset."""
+    df = load_dataset(data_dir, split)
+    
+    fig, axes = plt.subplots(1, 3, figsize=(15, 4))
+    colors = ["red", "green", "blue"]
+    columns = ["color_r", "color_g", "color_b"]
+    
+    for idx, (color, col) in enumerate(zip(colors, columns)):
+        axes[idx].hist(df[col], bins=50, color=color, alpha=0.7, edgecolor="black")
+        axes[idx].set_xlabel(f"{color.capitalize()} Value")
+        axes[idx].set_ylabel("Frequency")
+        axes[idx].set_title(f"{color.capitalize()} Channel Distribution")
+    
+    plt.suptitle(f"Color Distribution in {split.capitalize()} Set", fontsize=14, fontweight="bold")
+    plt.tight_layout()
+    plt.savefig("color_distribution.png", dpi=150, bbox_inches="tight")
+    print(f"Saved visualization to 'color_distribution.png'")
+    plt.show()
+
+
+# =============================================================================
+# Main
+# =============================================================================
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="Shape Polygons Dataset Examples")
+    parser.add_argument("--data-dir", type=str, default=".", help="Path to dataset root")
+    parser.add_argument(
+        "--example",
+        type=str,
+        choices=["stats", "samples", "shapes", "pytorch", "colors", "all"],
+        default="all",
+        help="Which example to run"
+    )
+    args = parser.parse_args()
+    
+    examples = {
+        "stats": ("Dataset Statistics", lambda: explore_statistics(args.data_dir)),
+        "samples": ("Sample Visualization", lambda: visualize_samples(args.data_dir)),
+        "shapes": ("Shape Types", lambda: visualize_by_shape_type(args.data_dir)),
+        "pytorch": ("PyTorch DataLoader Demo", lambda: demo_pytorch_dataloader(args.data_dir)),
+        "colors": ("Color Analysis", lambda: analyze_colors(args.data_dir)),
+    }
+    
+    if args.example == "all":
+        for name, (desc, func) in examples.items():
+            print(f"\n{'=' * 50}")
+            print(f"Example: {desc}")
+            print("=" * 50)
+            func()
+    else:
+        name = args.example
+        desc, func = examples[name]
+        print(f"Running Example: {desc}")
+        func()
+