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Dataset research report

Fashion Mnist research report

A reproducible data report with schema notes, generated chart evidence, suggested follow-up questions, and export-ready Helix queries.

storageHf descriptionfashion-mnist.parquet view_list500 rows

Executive Summary

Dataset Card for FashionMNIST Dataset Summary Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing… See the full description on the dataset page: https://huggingface.co/datasets/zalando-datasets/fashion_mnist.

Finding 1The dataset has 500 rows available in the catalog.
Finding 2The catalog exposes 2 documented or inferred columns.
Finding 3Helix has 5 ready query prompts for this dataset.
Finding 4This report includes 3 generated chart views.

Research Context

Fashion Mnist: 500 rows by 2 columns. These exploratory charts are generated automatically from the data - open the dataset in Helix to ask your own questions.

Data Profile

Rows500
Columns2
Numeric cols1
Categorical cols1

Chart Evidence

These views are generated from the dataset profile. Each chart is paired with a Helix query so it can be opened, adjusted, and exported.

Follow-Up Queries

Preview Rows

# imagetext labelinteger
1 <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x75513FF156E0>9
2 <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x75513FF15BA0>0
3 <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x75513FF156E0>0
4 <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x75513FF15BA0>3
5 <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x75513FF156E0>0
6 <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x75513FF15BA0>2

Data Dictionary

  • image categorical
  • label numeric

Method And Limits

  • Load the catalog entry and preview rows from the processed dataset file.
  • Infer numeric, categorical, time, and location fields from real columns.
  • Generate a small set of defensive Plotly chart specifications from that profile.
  • Expose each chart idea as a query link so the report can be rerun or exported in Helix.

This report is intentionally reproducible. It uses the local catalog metadata and generated chart specifications rather than claiming external conclusions beyond the dataset.

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