Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
10K - 100K
License:
Search is not available for this dataset
image imagewidth (px) 28 28 | label class label 10
classes | embedding_foundation sequencelengths 768 768 | embedding_ft sequencelengths 768 768 | outlier_score_ft float64 0.58 1 | outlier_score_foundation float64 0.68 0.98 | nn_image dict |
|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
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",
)
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