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TinyTorch/datasets
Vijay Janapa Reddi 0ed16a1553 Update release documentation and advanced modules
- Updated release checklist and December 2024 release notes
- Updated student version tooling documentation
- Modified modules 15-19 (memoization, quantization, compression, benchmarking)
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- Added checkpoint tests for modules 15-20
- Added activation script and book configuration
2025-11-09 16:51:55 -05:00
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TinyTorch Datasets

This directory contains datasets for TinyTorch examples and training.

Directory Structure

datasets/
├── tiny/           ← Tiny datasets shipped with repo (~100KB each)
│   └── digits_8x8.npz (1,797 samples, 67KB)
├── mnist/          ← Full MNIST (downloaded, gitignored)
├── cifar10/        ← Full CIFAR-10 (downloaded, gitignored)
└── download_*.py   ← Download scripts for large datasets

Quick Start

For learning (instant, offline):

# Use tiny shipped datasets
import numpy as np
data = np.load('datasets/tiny/digits_8x8.npz')

For serious training (download once):

python datasets/download_mnist.py

MNIST Dataset

The mnist/ directory should contain the MNIST or Fashion-MNIST dataset files:

  • train-images-idx3-ubyte.gz - Training images (60,000 samples)
  • train-labels-idx1-ubyte.gz - Training labels
  • t10k-images-idx3-ubyte.gz - Test images (10,000 samples)
  • t10k-labels-idx1-ubyte.gz - Test labels

Downloading the Dataset

Run the provided download script:

cd datasets
python download_mnist.py

This will download Fashion-MNIST (which has the same format as MNIST but is more accessible).

Dataset Format

Both MNIST and Fashion-MNIST use the same IDX file format:

  • Images: 28x28 grayscale pixels
  • Labels: Integer values 0-9
  • Gzipped for compression

Fashion-MNIST classes:

  • 0: T-shirt/top
  • 1: Trouser
  • 2: Pullover
  • 3: Dress
  • 4: Coat
  • 5: Sandal
  • 6: Shirt
  • 7: Sneaker
  • 8: Bag
  • 9: Ankle boot

The examples will work with either original MNIST digits or Fashion-MNIST items.