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Meta_Album_MD_6_Micro

Meta_Album_MD_6_Micro

active ARFF CC BY-NC 4.0 Visibility: public Uploaded 28-10-2022 by Meta Album
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## Meta-Album OmniPrint-MD-6 Dataset (Micro) * OmniPrint-MD-6 dataset consists of 28 120 images (128x128, RGB) from 703 categories. The images are synthesized with OmniPrint, no further processing was done. The OmniPrint synthesis parameters are stated as follows: font size is 192, image size is 128, the strength of random perspective transformation is 0.04, left/right/top/bottom margins are all 20% of the image size, the strength of pre-rasterization elastic transformation is 0.035, random translation is activated both horizontally and vertically, image blending method is Poisson Image Editing, rotation is within -60 and 60 degrees, horizontal shear is within -0.5 and 0.5, both foreground and background are images taken from a personal mobile phone. ### Dataset Details ![](https://meta-album.github.io/assets/img/samples/MD_6.png) Meta Album ID: OCR.MD_6 Meta Album URL: [https://meta-album.github.io/datasets/MD_6.html](https://meta-album.github.io/datasets/MD_6.html) Domain ID: OCR Domain Name: Optical Character Recognition Dataset ID: MD_6 Dataset Name: OmniPrint-MD-6 Short Description: Character images with a specific set of nuisance parameters \# Classes: 20 \# Images: 800 Keywords: ocr Data Format: images Image size: 128x128 License (original data release): CC BY 4.0 License URL(original data release): https://creativecommons.org/licenses/by/4.0/ License (Meta-Album data release): CC BY 4.0 License URL (Meta-Album data release): [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) Source: OmniPrint Source URL: https://github.com/SunHaozhe/OmniPrint Original Author: Haozhe Sun Original contact: sunhaozhe275940200@gmail.com Meta Album author: Haozhe Sun Created Date: 25 June 2021 Contact Name: Haozhe Sun Contact Email: meta-album@chalearn.org Contact URL: [https://meta-album.github.io/](https://meta-album.github.io/) ### Cite this dataset ``` @inproceedings{sun2021omniprint, title={OmniPrint: A Configurable Printed Character Synthesizer}, author={Haozhe Sun and Wei-Wei Tu and Isabelle M Guyon}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)}, year={2021}, url={https://openreview.net/forum?id=R07XwJPmgpl} } ``` ### Cite Meta-Album ``` @inproceedings{meta-album-2022, title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification}, author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, url = {https://meta-album.github.io/}, year = {2022} } ``` ### More For more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) For details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) Supporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) Meta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) ### Other versions of this dataset [[Mini]](https://www.openml.org/d/44310)

47 features

CATEGORY (target)numeric20 unique values
0 missing
FILE_NAMEstring800 unique values
0 missing
textstring20 unique values
0 missing
font_filestring322 unique values
0 missing
backgroundstring1 unique values
0 missing
background_image_crop_xnumeric677 unique values
0 missing
background_image_crop_x_plus_widthnumeric677 unique values
0 missing
background_image_crop_ynumeric667 unique values
0 missing
background_image_crop_y_plus_heightnumeric667 unique values
0 missing
background_image_namestring26 unique values
0 missing
background_image_original_heightnumeric7 unique values
0 missing
background_image_original_widthnumeric5 unique values
0 missing
background_image_resized_heightnumeric7 unique values
0 missing
background_image_resized_widthnumeric5 unique values
0 missing
font_sizenumeric1 unique values
0 missing
font_weightnumeric1 unique values
0 missing
foregroundstring1 unique values
0 missing
foreground_image_crop_xnumeric660 unique values
0 missing
foreground_image_crop_x_plus_widthnumeric660 unique values
0 missing
foreground_image_crop_ynumeric676 unique values
0 missing
foreground_image_crop_y_plus_heightnumeric676 unique values
0 missing
foreground_image_namestring26 unique values
0 missing
foreground_image_original_heightnumeric7 unique values
0 missing
foreground_image_original_widthnumeric5 unique values
0 missing
foreground_image_resized_heightnumeric7 unique values
0 missing
foreground_image_resized_widthnumeric5 unique values
0 missing
image_blending_methodstring1 unique values
0 missing
image_height_resolutionnumeric1 unique values
0 missing
image_modestring1 unique values
0 missing
image_width_resolutionnumeric1 unique values
0 missing
margin_bottomnumeric1 unique values
0 missing
margin_leftnumeric1 unique values
0 missing
margin_rightnumeric1 unique values
0 missing
margin_topnumeric1 unique values
0 missing
offset_horizontalnumeric179 unique values
0 missing
offset_verticalnumeric196 unique values
0 missing
original_image_height_resolutionnumeric177 unique values
0 missing
original_image_width_resolutionnumeric177 unique values
0 missing
perspective_paramsstring800 unique values
0 missing
pre_elasticnumeric1 unique values
0 missing
rotationnumeric720 unique values
0 missing
shear_xnumeric720 unique values
0 missing
SUPER_CATEGORYstring12 unique values
0 missing
family_namestring221 unique values
0 missing
style_namestring28 unique values
0 missing
postscript_namestring311 unique values
0 missing
variable_font_weightnominal2 unique values
0 missing

19 properties

800
Number of instances (rows) of the dataset.
47
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
32
Number of numeric attributes.
1
Number of nominal attributes.
0.06
Number of attributes divided by the number of instances.
68.09
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
2.13
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
2.13
Percentage of binary attributes.
0
Percentage of instances having missing values.
-3853.05
Average class difference between consecutive instances.
0
Percentage of missing values.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: CATEGORY
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