README.md

metadata

license: mit
task_categories:
  - image-classification
language:
  - en
tags:
  - mnist
  - image
  - digit
  - synthetic
  - houdini
pretty_name: MNIST Bakery Dataset
size_categories:
  - 10K<n<100K

🧁 MNIST Bakery Dataset

A procedurally synthesized variant of the classic MNIST dataset, created using SideFX Houdini and designed for experimentation in data augmentation, synthetic data generation, and model robustness research. See the ML-Research repository on GitHub for Python notebooks, experiments and the Houdini scene files.

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🎯 Purpose

This dataset demonstrates how procedural generation pipelines in 3D tools like Houdini can be used to create high-quality, synthetic training data for machine learning tasks. It is intended for:

• Benchmarking model performance using synthetic vs. real data
• Training models in low-data or zero-shot environments
• Developing robust classifiers that generalize beyond typical datasets
• Evaluating augmentation and generalization strategies in vision models

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🛠️ Generation Pipeline

All data was generated using the .hip scene:

./houdini/digitgen_v02.hip

🧪 Methodology

1. Procedural Digit Assembly

• Each digit 0–9 is generated using a random font in each frame via Houdini’s Font SOP.
• Digits are arranged in a clean 8×8 grid, forming sprite sheets with 64 digits per render.

2. Scene Variability

• Fonts are randomly selected per frame.
• Procedural distortions are applied including:
  • Rotation
  • Translation
  • Skew
  • Mountain noise displacement
• This ensures high variability across samples.

3. Rendering

• Scene renders are executed via Mantra or Karma.
• Output format: grayscale 224×224 px sprite sheets (.exr or .jpg).

4. Compositing & Cropping

• A COP2 network slices the sprite sheet into 28×28 digit tiles.
• Each tile is labeled by its original digit and saved to:

./output/0/img_00001.jpg
./output/1/img_00001.jpg
...

🧾 Dataset Structure

mnist_bakery_data/
├── 0/
│   ├── img_00001.jpg
│   ├── ...
├── 1/
│   ├── img_00001.jpg
│   └── ...
...
├── 9/
│   └── img_00001.jpg

• All images: grayscale .jpg, 28×28 resolution
• Total: 40,960 samples
• ~4,096 samples per digit

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📊 Statistics

Set	Samples	Mean	StdDev
MNIST	60,000	0.1307	0.3081
Synthetic	40,960	0.01599	0.07722

Combine mean/std using weighted averaging if mixing both datasets.

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📚 Usage Example

from torchvision import transforms, datasets

transform = transforms.Compose([
    transforms.Grayscale(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.01599], std=[0.07722])  # Approximate weighted normalization
])

dataset = datasets.ImageFolder('./mnist_bakery_data', transform=transform)

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🧠 Credits

Author: Aaron T. Carter

Organization: Arkaen Solutions

Tools Used: Houdini, PyTorch, PIL

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