πŸ› οΈ
Tool

datasets

by tensorflow ID: gh-tool--tensorflow--datasets

TensorFlow Datasets provides many public datasets as . To install and use TFDS, we strongly encourage to start with our **getting started guide**. Try it interactively in a Colab notebook. Our documentation contains: * Tutorials and guides * List of all available datasets * The API reference TFDS ha...

Python Lang
Open Source 4.5K Stars
1.0.0 Version
Alpha Reliability
Tool Information Summary
Entity Passport
Registry ID gh-tool--tensorflow--datasets
Provider github
πŸ“œ

Cite this tool

Academic & Research Attribution

BibTeX
@misc{gh_tool__tensorflow__datasets,
  author = {tensorflow},
  title = {datasets Tool},
  year = {2026},
  howpublished = {\url{https://github.com/tensorflow/datasets}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
tensorflow. (2026). datasets [Tool]. Free2AITools. https://github.com/tensorflow/datasets

πŸ”¬Technical Deep Dive

Full Specifications [+]

⚑ Quick Commands

πŸ™ GitHub Clone
git clone https://github.com/tensorflow/datasets
🐍 PIP Install
pip install datasets

βš–οΈ Free2AI Nexus Index

Methodology β†’ πŸ“˜ What is FNI?
0.0
Top 19% Overall Impact
πŸ”₯ Popularity (P) 0
πŸš€ Velocity (V) 0
πŸ›‘οΈ Credibility (C) 0
πŸ”§ Utility (U) 0
Nexus Verified Data

πŸ’¬ Why this score?

The Nexus Index for datasets aggregates Popularity (P:0), Velocity (V:0), and Credibility (C:0). The Utility score (U:0) represents deployment readiness, context efficiency, and structural reliability within the Nexus ecosystem.

Data Verified πŸ• Last Updated: Not calculated
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πŸ“‹ Specs

Language
Python
License
Open Source
Version
1.0.0
πŸ“¦

Usage documentation not yet indexed for this tool.

πŸ”— View Source Code β†—

Technical Documentation

TensorFlow Datasets

TensorFlow Datasets provides many public datasets as tf.data.Datasets.

Unittests
PyPI version
Python 3.10+
Tutorial
API
Catalog

Documentation

To install and use TFDS, we strongly encourage to start with our
getting started guide. Try
it interactively in a
Colab notebook.

Our documentation contains:

# !pip install tensorflow-datasets
import tensorflow_datasets as tfds
import tensorflow as tf

Construct a tf.data.Dataset

ds = tfds.load('mnist', split='train', as_supervised=True, shuffle_files=True)

Build your input pipeline

ds = ds.shuffle(1000).batch(128).prefetch(10).take(5) for image, label in ds: pass

TFDS core values

TFDS has been built with these principles in mind:

  • Simplicity: Standard use-cases should work out-of-the box
  • Performance: TFDS follows
    best practices
    and can achieve state-of-the-art speed
  • Determinism/reproducibility: All users get the same examples in the same
    order
  • Customisability: Advanced users can have fine-grained control

If those use cases are not satisfied, please send us
feedback.

Want a certain dataset?

Adding a dataset is really straightforward by following
our guide.

Request a dataset by opening a
Dataset request GitHub issue.

And vote on the current
set of requests
by adding a thumbs-up reaction to the issue.

Citation

Please include the following citation when using tensorflow-datasets for a
paper, in addition to any citation specific to the used datasets.

@misc{TFDS,
  title = {{TensorFlow Datasets}, A collection of ready-to-use datasets},
  howpublished = {\url{https://www.tensorflow.org/datasets}},
}

Disclaimers

This is a utility library that downloads and prepares public datasets. We do
not host or distribute these datasets, vouch for their quality or fairness, or
claim that you have license to use the dataset. It is your responsibility to
determine whether you have permission to use the dataset under the dataset's
license.

If you're a dataset owner and wish to update any part of it (description,
citation, etc.), or do not want your dataset to be included in this
library, please get in touch through a GitHub issue. Thanks for your
contribution to the ML community!

If you're interested in learning more about responsible AI practices, including
fairness, please see Google AI's Responsible AI Practices.

tensorflow/datasets is Apache 2.0 licensed. See the
LICENSE file.

Social Proof

GitHub Repository
4.5KStars
1.6KForks
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AI Summary: Based on GitHub metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Tool Transparency Report

Verified data manifest for traceability and transparency.

100% Data Disclosure Active

πŸ†” Identity & Source

id
gh-tool--tensorflow--datasets
source
github
author
tensorflow
tags
datadatasetdatasetsjaxmachine-learningnumpytensorflowpython

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
other

πŸ“Š Engagement & Metrics

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github stars
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Free2AITools Constitutional Data Pipeline: Curated disclosure mode active. (V15.x Standard)