README.md

metadata

license: mit
task_categories:
  - feature-extraction
language:
  - en
tags:
  - code
pretty_name: MNIST Visual Curation
size_categories:
  - 10K<n<100K

Curation of the famous MNIST Dataset

The curation was done using qualitative analysis of the dataset, following visualization techniques like PCA and UMAP and score-based categorization of the samples using metrics like hardness, mistakenness, or uniqueness.

The code of the curation can be found on GitHub:
👉 https://github.com/Conscht/MNIST_Curation_Repo/tree/main

This curated version of MNIST introduces an additional IDK (“I Don’t Know”) label for digits that are ambiguous, noisy, or of low quality. It is intended for experiments on robust classification, dataset curation, and handling uncertain or hard-to-classify examples.

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🔍 Overview

Compared to the original MNIST dataset, this curated version:

• keeps the original digit classes 0–9
• adds an 11th class: IDK
• moves visually ambiguous or questionable digits into the IDK class

Questionable digits include:

• distorted or spaghetti-like shapes
• digits that are hard even for humans to classify
• strong outliers in the embedding space
• samples often misclassified by the baseline model

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🧠 How the Curation Was Done

The curation process combined qualitative inspection and quantitative metrics:

1. Train a LeNet-5 classifier on the original MNIST digits.
2. Extract embeddings from the penultimate layer of the network.
3. Visualize these embeddings with PCA and UMAP in FiftyOne to identify clusters, outliers, and ambiguous regions.
4. Compute several FiftyOne Brain metrics:
   • hardness
   • mistakenness
   • uniqueness
   • representativeness
5. Use these metrics to surface suspicious samples:
   • highly mistaken or hard examples
   • high-uniqueness outliers
   • misclassified samples
6. Inspect these subsets inside the FiftyOne App and manually decide which samples should be relabeled as IDK.

Example of visualized embedding space:

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📁 Dataset Structure

The dataset is exported in ImageClassificationDirectoryTree format:

root/
├── train/
│   ├── 0/
│   ├── 1/
│   ├── ...
│   ├── 9/
│   └── IDK/
└── test/
    ├── 0/
    ├── 1/
    ├── ...
    ├── 9/
    └── IDK/

@article{lecun1998gradient,
  title={Gradient-based learning applied to document recognition},
  author={LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
  journal={Proceedings of the IEEE},
  volume={86},
  number={11},
  pages={2278--2324},
  year={1998},
  publisher={IEEE}
}