# Geometric Shapes Dataset

This dataset contains procedurally generated images of various geometric shapes with corresponding captions. It's designed for educational purposes and testing of diffusion models.

## Dataset Overview

- **Content**: 100,000 images of geometric shapes with detailed metadata
- **Image size**: 512x512 pixels
- **Format**: PNG images with CSV metadata
- **Features**: Various shapes, colors, sizes, and descriptive captions
- **Purpose**: Educational use for training and testing single-step diffusion models

## Dataset Creation

The dataset was generated using the included Python script (`create_dataset.py`), which was created with the assistance of Claude AI (Anthropic). The script generates a wide variety of geometric shapes with different properties:

- **Shapes**: 50+ different shapes including basic shapes (circle, square, triangle) and more complex ones (hypocycloid, lemniscate, star)
- **Colors**: 30 different colors with their hex values
- **Sizes**: Randomly scaled from 10% to 100% with corresponding descriptive terms
- **Captions**: Diverse, randomly generated descriptions of the shapes in various formats

## Usage

### Using the Dataset

This dataset is designed for educational purposes only. It's particularly useful for:

- Learning to build diffusion models from scratch
- Experimenting with simple image-to-text and text-to-image models
- Testing model performance on synthetic data
- Educational demonstrations of computer vision concepts

### OPTIONAL - If you want to createw youe own images

If you want to generate your own version of the dataset or modify the parameters:

1. Install the required dependencies:
   ```bash
   pip install numpy matplotlib tqdm
   ```

2. Run the script:
   create a folder "geometric_shapes", and save create_dataset.py the in the folder, and execute it
   ```bash
   python create_dataset.py
   ```
   ```
   Generating Images: 100%|█████████████████████████████████████████████████| 100000/100000 [07:36<00:00, 218.96it/s]
   Generated 100000 images in 456.89 seconds
   Size: 100000 images
   ```
   
3. By default, the script will:
   - Generate 100,000 images in the `dataset/images` directory
   - Create a `dataset/metadata.csv` file with image filenames and captions
   - Use 10 CPU cores for parallel processing

4. You can modify these parameters in the `__main__` section of the script:
   ```python
   generate_images_multiprocess(
       num_images=100000,  # Change this number to generate fewer/more images
       max_workers=10,     # Change based on your CPU cores
       img_size=(512, 512) # Modify image dimensions
   )
   ```

5.  Ensure your dataset is organized as follows:

```
geometric_shapes/
├── README.md
├── create_dataset.py
├── dataset/
│   ├── images_001/
│   │   ├── image1.jpg
│   │   ├── image2.jpg
│   │   ├── ...
```

6. Upload Instructions

   Log in to Hugging Face:

   ```sh
   huggingface-cli login
   ```

   Upload the dataset:

   ```sh
   cd .. && huggingface-cli upload-large-folder anokimchen/geometric_shapes geometric_shapes  --repo-type dataset && cd geometric_shapes
   ```

## License

This dataset is provided for educational and research purposes only.

## Acknowledgements

- Code generated with the assistance of Claude (Anthropic)
- Dataset created for learning purposes to build and test single-step diffusion models

## Citation

If you use this dataset in your research or educational projects, please include a reference to this repository.