---
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.