---
{
  "id": "B-001",
  "title": "Data Pipeline and Preprocessing",
  "slug": "data-pipeline-and-preprocessing",
  "dir": "blocks/B-001-data-pipeline-and-preprocessing",
  "status": "implemented",
  "depends_on": [],
  "related_blocks": [
    "B-002",
    "B-003"
  ],
  "source_plan_refs": [
    "Goal",
    "System Requirements",
    "Data Handling",
    "Inference"
  ],
  "paper_ids": [
    "P-001",
    "P-002"
  ],
  "created_at": "2026-07-21T17:57:47.887Z",
  "updated_at": "2026-07-22T23:40:40.183Z",
  "source_excerpt": "Goal: build a handwritten digit classifier from MNIST.\nSystem Requirements: load MNIST, preprocess images, train, evaluate, and provide inference.\nData Handling: download/load images and labels, use train/test split, normalize pixels, reshape images.\nInference: preprocess new single images the same way as training data."
}
---







# Data Pipeline and Preprocessing

## Purpose
Load MNIST, normalize it, split it, and prepare tensors/images in the exact shape required by the classifier.

## Source From Original Plan
Goal: build a handwritten digit classifier from MNIST.
System Requirements: load MNIST, preprocess images, train, evaluate, and provide inference.
Data Handling: download/load images and labels, use train/test split, normalize pixels, reshape images.
Inference: preprocess new single images the same way as training data.

## Responsibilities
- Download or load MNIST images and labels.
- Use the dataset's train/test split correctly.
- Normalize pixel values for model training and inference.
- Handle grayscale image shape consistently.
- Format labels for the chosen loss function.
- Provide reusable preprocessing for both training and inference.

## Inputs
- MNIST images and labels.
- Raw single-image input for inference preprocessing.

## Outputs
- Training dataset.
- Test dataset.
- Normalized image tensors.
- Encoded labels.
- Shared preprocessing function.

## Dependencies
TBD

## Related Blocks
- [B-002](../../blocks/B-002-cnn-model-training/block.md)
- [B-003](../../blocks/B-003-evaluation-inference-and-run-instructions/block.md)

## Research Questions
- What is the standard normalization used for MNIST CNN baselines?
- What input tensor shape should the selected framework use for 28x28 grayscale images?
- Is data augmentation useful or unnecessary for a simple MNIST baseline?

## Implementation Criteria
- Data loads reliably from the selected framework or dataset source.
- Pixel values are normalized consistently.
- Model receives 28x28x1 images or the framework-equivalent channel format.
- Training and inference share the same preprocessing logic.

## Open Questions
TBD