metadata

annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license:
  - mit
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - extended|other-nist
task_categories:
  - image-classification
task_ids:
  - multi-class-image-classification
paperswithcode_id: mnist
pretty_name: MNIST
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': '0'
            '1': '1'
            '2': '2'
            '3': '3'
            '4': '4'
            '5': '5'
            '6': '6'
            '7': '7'
            '8': '8'
            '9': '9'
    - name: embedding_foundation
      sequence: float32
    - name: embedding_ft
      sequence: float32
    - name: outlier_score_ft
      dtype: float64
    - name: outlier_score_foundation
      dtype: float64
    - name: nn_image
      struct:
        - name: bytes
          dtype: binary
        - name: path
          dtype: 'null'
  splits:
    - name: train
      num_bytes: 404136444
      num_examples: 60000
  download_size: 472581433
  dataset_size: 404136444

Dataset Card for "mnist-outlier"

📚 This dataset is an enriched version of the MNIST Dataset.

The workflow is described in the medium article: Changes of Embeddings during Fine-Tuning of Transformers.

Explore the Dataset

The open source data curation tool Renumics Spotlight allows you to explorer this dataset. You can find a Hugging Face Space running Spotlight with this dataset here: https://huggingface.co/spaces/renumics/mnist-outlier.

Or you can explorer it locally:

!pip install renumics-spotlight datasets
from renumics import spotlight
import datasets

ds = datasets.load_dataset("renumics/mnist-outlier", split="train")
df = ds.rename_columns({"label":"labels"}).to_pandas()
df["label_str"] = df["labels"].apply(lambda x: ds.features["label"].int2str(x))
dtypes = {
    "nn_image": spotlight.Image,
    "image": spotlight.Image,
    "embedding_ft": spotlight.Embedding,
    "embedding_foundation": spotlight.Embedding,
}
spotlight.show(
    df,
    dtype=dtypes,
    layout="https://spotlight.renumics.com/resources/layout_pre_post_ft.json",
)