TinyTorch/datasets/tiny

Tiny Datasets for TinyTorch

Small, curated datasets that ship with TinyTorch - no downloads required!

These datasets are committed to the repository for instant, offline-friendly learning.

---

📊 Available Datasets

8×8 Handwritten Digits

File: digits_8x8.npz
Size: ~67 KB
Samples: 1,797 images
Shape: (8, 8) grayscale
Classes: 10 digits (0-9)
Source: UCI ML Repository via sklearn

Perfect for:

• Learning DataLoader mechanics
• Quick CNN testing
• Offline development
• Educational demos

Usage:

import numpy as np
from tinytorch import Tensor
from tinytorch.data.loader import TensorDataset, DataLoader

# Load the dataset
data = np.load('datasets/tiny/digits_8x8.npz')
images = Tensor(data['images'])
labels = Tensor(data['labels'])

# Create dataset and loader
dataset = TensorDataset(images, labels)
loader = DataLoader(dataset, batch_size=32, shuffle=True)

# Iterate through batches
for batch_images, batch_labels in loader:
    print(f"Batch: {batch_images.shape}, Labels: {batch_labels.shape}")

Visual Sample:

Digit "5":        Digit "3":        Digit "8":
░░████░░         ░█████░          ░█████░░
░█░░░█░          ░░░░░█░          █░░░░░█░
░░░░█░░          ░░███░░          ░█████░░
░░░█░░░          ░░░░░█░          █░░░░░█░
░░█░░░░          ░░████░░          ░█████░░

---

🎯 Philosophy

Why ship tiny datasets?

1. Zero friction - Students start learning immediately
2. Offline-first - Works in classrooms, planes, anywhere
3. Fast iteration - No wait times, instant feedback
4. Educational focus - Sized for learning, not production

Progression:

• Tiny datasets (here) → Learn DataLoader mechanics
• Downloaded datasets (../mnist/, ../cifar10/) → Real applications
• Custom datasets → Production skills

---

📂 File Format

All datasets use NumPy's .npz format (compressed):

data = np.load('dataset.npz')
images = data['images']  # Shape: (N, H, W) or (N, H, W, C)
labels = data['labels']  # Shape: (N,)

Benefits:

• Fast loading
• Compressed storage
• Python-native
• Easy inspection

---

🔧 Creating New Tiny Datasets

See create_digits_8x8.py for example extraction script.

Guidelines:

• Max size: ~100 KB per dataset
• Format: .npz with images and labels keys
• Normalize: Images in [0, 1] range
• License: Verify public domain / open source

---

📚 Dataset Information

Digits 8×8 Credits

Original Source:

• E. Alpaydin, C. Kaynak (1998)
• UCI Machine Learning Repository
• "Optical Recognition of Handwritten Digits"

Preprocessing:

• Extracted via sklearn.datasets.load_digits()
• Normalized from [0-16] to [0-1]
• Saved as float32 for efficiency

License: Public domain

---

🚀 Next Steps

After mastering DataLoader with tiny datasets:

1. Module 08 → Build DataLoader with digits_8x8
2. Milestone 03 → Train MLP on full MNIST
3. Milestone 04 → Train CNN on CIFAR-10
4. Custom datasets → Apply to your own data

Tiny datasets teach the mechanics.
Real datasets teach the systems.
Custom datasets teach the engineering.