tensorflow / tensorflow/datasets

[data request] QMNIST Dataset

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dataset request
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Python
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Description

  • Name of dataset: QMNIST
  • URL of dataset: https://github.com/facebookresearch/qmnist
  • License of dataset: BSD License
  • Short description of dataset and use case(s):
    The QMNIST dataset was generated from the original data found in the NIST Special Database 19 with the goal to match the MNIST preprocessing as closely as possible.
    The exact preprocessing steps used to construct the MNIST dataset have long been lost. This leaves us with no reliable way to associate its characters with the ID of the writer and little hope to recover the full MNIST testing set that had 60K images but was never released. The official MNIST testing set only contains 10K randomly sampled images and is often considered too small to provide meaninful confidence intervals.
  • MORE: Screenshot 2019-06-01 at 9 46 48 AM

Folks who would also like to see this dataset in tensorflow/datasets, please thumbs-up so the developers can know which requests to prioritize.

PS: I would like to contribute with this dataset!

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reviewing the QMNIST dataset source at https://github.com/facebookresearch/qmnist and the repository's existing dataset contribution entry points. Confirm the required integration and validation steps; the work is done when QMNIST is available through TensorFlow Datasets with its source, license, description, and use cases represented.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, tensorflow
Domain
data, machine-learning
Issue type
Feature
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
35/100

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