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Description:

hasy 0.3.1

Please refer to the HASY paper for details
about the dataset. If you want to report problems of the HASY dataset, please
send an email to info@martin-thoma.de or file an issue at
https://github.com/MartinThoma/HASY
Errata are listed in the git repository as well as the actual hasy package.
Contents
The contents of the HASYv2 dataset are:

hasy-data: 168236 png images, each 32px x 32px
hasy-data-labels.csv: Labels for all images.
classification-task: 10 folders (fold-1, fold-2, ..., fold-10) which
contain a train.csv and a test.csv each. Every line of the csv files
points to one of the png images (relative to itself). If those files are
used, then the hasy-data-labels.csv is not necessary.
verification-task: A train.csv and three different test files. All files
should be used in exactly the same way, but the accuracy should be reported
for each one.
The task is to decide for a pair of two 32px x 32px images if they belong
to the same symbol (binary classification).
symbols.csv: All classes
README.txt: This file

How to evaluate
Classification Task
Use the pre-defined 10 folds for 10-fold cross-validation. Report the
average accuracy as well as the minumum and maximum accuracy.
Verification Task
Use the train.csv for training. Use test-v1.csv, test-v2.csv, test-v3.csv` for evaluation. Report TP, TN, FP, FN and accuracy for each
of the three test groups.
hasy package
hasy can be used in two ways: (1) as a shell script (2) as a Python
module.
If you want to get more information about the shell script options, execute
$ hasy --help
usage: hasy [-h] [--dataset DATASET] [--verify] [--overview] [--analyze_color]
[--class_distribution] [--distances] [--pca] [--variance]
[--correlation] [--count-users] [--analyze-cm CM]

optional arguments:
-h, --help show this help message and exit
--dataset DATASET specify which data to use (default: None)
--verify verify PNG files (default: False)
--overview Get overview of data (default: False)
--analyze_color Analyze the color distribution (default: False)
--class_distribution Analyze the class distribution (default: False)
--distances Analyze the euclidean distance distribution (default:
False)
--pca Show how many principal components explain 90% / 95% /
99% of the variance (default: False)
--variance Analyze the variance of features (default: False)
--correlation Analyze the correlation of features (default: False)
--count-users Count how many different users have created the
dataset (default: False)
--analyze-cm CM Analyze a confusion matrix in JSON format. (default:
False)

If you want to use hasy as a Python package, see
python -c "import hasy.hasy_tools;help(hasy.hasy_tools)"

Changelog

14.05.2020, hasy Python package: Major refactoring of this repository
24.01.2017, HASYv2: Points were not rendered in HASYv1; improved hasy_tools
https://doi.org/10.5281/zenodo.259444
18.01.2017, HASYv1: Initial upload
https://doi.org/10.5281/zenodo.250239