OmerTurk1/GeometricShapeDataset

Geometric Shape Dataset

Introduction to Dataset

The Geometric Shape Dataset is a large-scale, synthetically generated computer vision dataset containing 1,680,000 instances of various geometric shapes and lines. It is designed for training image classification, feature extraction, and pattern recognition models across different scales.

The dataset is available in three distinct resolution configurations: 30x30, 40x40, and 50x50 (560,000 images per resolution).

Instead of clean, binary pixel representations, this dataset introduces complex mathematical variations: every shape features dynamic scaling, random rotations, varied boundary thickness, and a unique distance-based "halo" noise gradient. Additionally, a global background noise is applied to the entire canvas. Values are strictly normalized between 0.0 and 1.0.

Shapes Distribution

The dataset is perfectly balanced. It contains 7 distinct classes, distributed equally across the 1,680,000 samples (240,000 images per class in total, or 80,000 per class per resolution).

<table style="width: 100%; text-align: left; border-collapse: collapse;"> <thead> <tr> <th>Shape Class</th> <th>Count</th> <th>Description</th> <th style="text-align: center;">Examples (50x50)</th> </tr> </thead> <tbody> <tr> <td><strong>Circle</strong></td> <td>240,000</td> <td>Randomly scaled ellipses and perfect circles.</td> <td style="white-space: nowrap; text-align: center;"> <img src="50x50/samples/circular/0000001.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/circular/0000002.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/circular/0000003.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> </td> </tr> <tr> <td><strong>Triangle</strong></td> <td>240,000</td> <td>3-sided polygons with dynamic edge stretching.</td> <td style="white-space: nowrap; text-align: center;"> <img src="50x50/samples/triangular/0000001.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/triangular/0000002.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/triangular/0000003.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> </td> </tr> <tr> <td><strong>Rectangle</strong></td> <td>240,000</td> <td>4-sided orthogonal shapes (squares and rectangles).</td> <td style="white-space: nowrap; text-align: center;"> <img src="50x50/samples/rectangular/0000001.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/rectangular/0000002.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/rectangular/0000003.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> </td> </tr> <tr> <td><strong>Pentagon</strong></td> <td>240,000</td> <td>5-sided regular and dynamically stretched polygons.</td> <td style="white-space: nowrap; text-align: center;"> <img src="50x50/samples/pentagonal/0000001.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/pentagonal/0000002.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/pentagonal/0000003.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> </td> </tr> <tr> <td><strong>Hexagon</strong></td> <td>240,000</td> <td>6-sided regular and dynamically stretched polygons.</td> <td style="white-space: nowrap; text-align: center;"> <img src="50x50/samples/hexagonal/0000001.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/hexagonal/0000002.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/hexagonal/0000003.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> </td> </tr> <tr> <td><strong>Parallelogram</strong></td> <td>240,000</td> <td>4-sided shapes with forced shear (strictly non-rectangular).</td> <td style="white-space: nowrap; text-align: center;"> <img src="50x50/samples/parallelogram/0000001.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/parallelogram/0000002.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/parallelogram/0000003.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> </td> </tr> <tr> <td><strong>Line</strong></td> <td>240,000</td> <td>Curved Quadratic Bezier lines mimicking imperfect hand-drawn strokes.</td> <td style="white-space: nowrap; text-align: center;"> <img src="50x50/samples/line/0000001.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/line/0000002.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> <img src="50x50/samples/line/0000003.png" width="50" height="50" style="display: inline-block; margin: 2px; border-radius: 4px;"> </td> </tr> <tr> <td><strong>Total</strong></td> <td><strong>1,680,000</strong></td> <td></td> <td></td> </tr> </tbody> </table> Note: For all samples, you can go to Files and Versions, then click into the respective resolution's samples folders.

Dataset Characteristics & Generation Details

To prevent models from easily memorizing shapes, several advanced data augmentation techniques were mathematically baked into the generation process:

• —Float16 Precision: Continuous pixel values rather than 8-bit integers, preserving the exact mathematical noise distribution.
• —Curved Strokes: Lines are generated using Bezier curves with varying bend amounts, perfectly simulating the imperfections of human drawing.
• —Dynamic Thickness: The solid boundaries of the shapes randomly vary between 1, 2, or 3 pixels.
• —Halo Noise Gradient: A localized noise layer surrounds the shape, inversely proportional to the distance from the solid boundary (fading out over a 4-pixel radius).
• —Global Noise: A uniform random noise between -0.1 and 0.1 applied globally (clipped to stay within [0, 1]).
• —Safety Scaling: Bounding boxes guarantee that no shape clips outside the canvas boundaries, even under extreme rotation or shear.

Storage Structure

To optimize both disk space and loading speeds, the dataset is architected with modern streaming in mind:

• —Configurations by Resolution: Choose between 30x30, 40x40, or 50x50 subsets right from the Hugging Face viewer.
• —Chunked Parquet Files: The dataset is split into smaller, highly compressed .parquet chunks (5,600 images per chunk).
• —Flattened Arrays: Parquet does not natively support 2D/3D matrix cells, so the images are safely flattened into 1D arrays. Depending on the configuration, the array length will be 900 (for 30x30), 1600 (for 40x40), or 2500 (for 50x50). They can be instantly reshaped during training.
• —Pre-Shuffled: The shapes are heavily shuffled within each chunk before saving. You will receive perfectly mixed, heterogeneous batches starting from the very first megabyte.

How to Use

Because of the chunked Parquet structure, you can either download the entire dataset or use Iterable Streaming (lazy loading) to train models without consuming any hard drive space.

1. Streaming Mode

Use streaming=True and specify the name argument (e.g., "50x50") to fetch data on the fly. Don't forget to reshape the 1D flat array back to a 2D matrix matching your chosen resolution.

from datasets import load_dataset
import numpy as np

# Load dataset configuration "50x50" without downloading to disk
iterable_dataset = load_dataset("OmerTurk1/GeometricShapeDataset", name="50x50", split="train", streaming=True)

# You can add a buffer shuffle for even more randomness during training
shuffled_dataset = iterable_dataset.shuffle(buffer_size=10000, seed=42)

for data in shuffled_dataset:
    # Extract and convert to numpy array
    flat_image = np.array(data["image"], dtype=np.float16)
    
    # Reshape back to 50x50 matrix (Change to 30, 30 if using the 30x30 config)
    image_matrix = flat_image.reshape(50, 50)
    label = data["label"]
    
    print(f"Label: {label} | Shape: {image_matrix.shape}")

    # Rest is up to you

2. Standard Download

If you have enough RAM and want to load the entire dataset into memory:

from datasets import load_dataset
import numpy as np

# This will download the 30x30 dataset configuration to your local Hugging Face cache
dataset = load_dataset("OmerTurk1/GeometricShapeDataset", name="30x30", split="train")

# Reshape back to 50x50 matrix (Change to 30, 30 if using the 30x30 config)
first_image = np.array(dataset[0]["image"], dtype=np.float16).reshape(50, 50)
first_label = dataset[0]["label"]

print(f"First image is a {first_label} with shape {first_image.shape}")

# Rest is up to you