| # 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. | |