Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
extended|other-nist
Tags:
License:
| 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: 404136444.0 | |
| num_examples: 60000 | |
| download_size: 472581433 | |
| dataset_size: 404136444.0 | |
| # Dataset Card for "mnist-outlier" | |
| 📚 This dataset is an enriched version of the [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). | |
| The workflow is described in the medium article: [Changes of Embeddings during Fine-Tuning of Transformers](https://medium.com/@markus.stoll/changes-of-embeddings-during-fine-tuning-c22aa1615921). | |
| ## Explore the Dataset | |
| The open source data curation tool [Renumics Spotlight](https://github.com/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: | |
| ```python | |
| !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", | |
| ) | |
| ``` |