Duplicate from Consscht/MNIST-Curation

Co-authored-by: Constantin <Consscht@users.noreply.huggingface.co>

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README.md

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+---
+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.
+
+---
+
+## 🔍 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  
+
+---
+
+## 🧠 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:
+![UMAP](https://cdn-uploads.huggingface.co/production/uploads/673e7e7c4dca9bce31534dd7/bHTbMCZ0QL3ETONxoUT4S.png)
+
+---
+
+## 📁 Dataset Structure
+
+The dataset is exported in **ImageClassificationDirectoryTree** format:
+
+```text
+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}
+}