Benchmark Datasets - free edition

What is the InfoFrames free edition?

InfoFrames free editions is a FREE library that you can use to experiment with well known data sets in a new format (see InfoFrames Benchmark Datasets). Discover how InfoFrames summaries can speed up your work.

What are InfoFrames Benchmark Datasets?

We provide a list of most popular benchmark datasets available in the form of InfoFrames summaries, so that you can experiment with AI/ML in no time.

Quick Start

Pip

1  # update pip
2  pip update
3  # Install the package
4  # into python virtual environment
5  pip install -U infoframes
6
7  python
8  >>>import infoframes as if_api

Available datasets:

Original
Dataset
InfoFrame’s
Dataset (*)
Compression
Ratio (**)
Model (***)
Quality
Difference (****)
Training Time
Difference (5*)
MNIST
Version 1
183
183XGBoost
93,7% -> 89,2%
28,07 s -> 2,94 s
MNIST
Version 2
183
XGBoost
93,7% -> 89,2%
28,07 s -> 2,76 s
Fashion – MNIST
Version 1
120
XGBoost
85,4% -> 80,9%
31,65 s -> 2,67 s
Fashion – MNIST
Version 2
120
XGBoost
85,4% -> 80,9%
32,31 s -> 3,09 s
Original
Dataset
Info Frame’s
Dataset ⓘOriginal dataset transformed info InfoFrames format
Compression
Ratio ⓘOriginal dataset size divided by InfoFrames summaries size
Model ⓘModel used in the benchmark
Quality
Difference ⓘAccuracy difference between models trained on the original dataset and on the InfoFrames summaries
Training Time
Difference ⓘTraining time difference between models trained on the original dataset and on the InfoFrames summaries
MNIST
Version 1
183
183XGBoost
93,7% -> 89,2%
28,07 s -> 2,94 s
MNIST
Version 2
183
XGBoost
93,7% -> 89,2%
28,07 s -> 2,76 s
Fashion – MNIST
Version 1
120
XGBoost
85,4% -> 80,9%
31,65 s -> 2,67 s
Fashion – MNIST
Version 2
120
XGBoost
85,4% -> 80,9%
32,31 s -> 3,09 s
MNIST
Version 1
183
183XGBoost
93,7% -> 89,2%
28,07 s -> 2,94 s
MNIST
Version 2
183
XGBoost
93,7% -> 89,2%
28,07 s -> 2,76 s
Fashion – MNIST
Version 1
120
XGBoost
85,4% -> 80,9%
31,65 s -> 2,67 s
Fashion – MNIST
Version 2
120
XGBoost
85,4% -> 80,9%
32,31 s -> 3,09 s

* Original dataset transformed into InfoFrames format
** Original dataset size divided by InfoFrames summaries size
*** Model used in the benchmark
4* Accuracy difference between models trained on the original dataset and on the InfoFrames summaries
5* Training time difference between models trained on the original dataset and on the InfoFrames summaries

MNIST
Version 1
183
183XGBoost
93,7% -> 89,2%
28,07 s -> 2,94 s
MNIST
Version 2
183
XGBoost
93,7% -> 89,2%
28,07 s -> 2,76 s
Fashion – MNIST
Version 1
120
XGBoost
85,4% -> 80,9%
31,65 s -> 2,67 s
Fashion – MNIST
Version 2
120
XGBoost
85,4% -> 80,9%
32,31 s -> 3,09 s

Notebooks with examples on how to use InfoFrames summaries are now available on GitHub.

If you didn’t find a set that you are interested in, contact us at [email protected]

How to use InfoFrames edition

Mateusz Wnuk about If in Eng

Do you have any questions? Please write to us

Do you have any questions?
Please write to us