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Meta_Album_MD_MIX_Micro

Meta_Album_MD_MIX_Micro

active ARFF CC BY-NC 4.0 Visibility: public Uploaded 11-10-2022 by Meta Album
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## Meta-Album OmniPrint-MD-mix Dataset (Micro) * OmniPrint-MD-mix dataset consists of 28 240 images (128x128, RGB) from 706 categories. The images are synthesized with OmniPrint, and 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, rotation is within -60 and 60 degrees, horizontal shear is within -0.5 and 0.5, brightness is within 0.8333 and 1.2, contrast is within 0.8333 and 1.2, color enhancement is within 0.8333 and 1.2. The other parameters vary between images. We designed 20 settings, each setting is used to synthesize 2 images. All images/textures consists of photos taken by a personal mobile phone. ### Dataset Details ![](https://meta-album.github.io/assets/img/samples/MD_MIX.png) Meta Album ID: OCR.MD_MIX Meta Album URL: [https://meta-album.github.io/datasets/MD_MIX.html](https://meta-album.github.io/datasets/MD_MIX.html) Domain ID: OCR Domain Name: Optical Character Recognition Dataset ID: MD_MIX Dataset Name: OmniPrint-MD-mix 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/44287)

69 features

CATEGORY (target)numeric20 unique values
0 missing
FILE_NAMEstring800 unique values
0 missing
textstring20 unique values
0 missing
font_filestring235 unique values
0 missing
backgroundstring4 unique values
0 missing
background_colorstring135 unique values
400 missing
brightnessnumeric799 unique values
0 missing
color_enhancenumeric799 unique values
0 missing
contrastnumeric799 unique values
0 missing
font_sizenumeric1 unique values
0 missing
font_weightnumeric1 unique values
0 missing
foregroundstring2 unique values
0 missing
image_blending_methodstring2 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_horizontalnumeric161 unique values
0 missing
offset_verticalnumeric175 unique values
0 missing
original_image_height_resolutionnumeric189 unique values
0 missing
original_image_width_resolutionnumeric189 unique values
0 missing
perspective_paramsstring792 unique values
0 missing
pre_elasticnumeric1 unique values
0 missing
rotationnumeric300 unique values
0 missing
shear_xnumeric258 unique values
120 missing
stroke_fillstring228 unique values
400 missing
SUPER_CATEGORYstring14 unique values
0 missing
family_namestring169 unique values
0 missing
style_namestring19 unique values
0 missing
postscript_namestring230 unique values
0 missing
variable_font_weightnominal2 unique values
0 missing
outlinestring2 unique values
440 missing
outline_sizenumeric2 unique values
440 missing
morph_gradient_kernel_shapestring1 unique values
560 missing
morph_gradient_kernel_sizenumeric1 unique values
560 missing
foreground_image_crop_xnumeric225 unique values
400 missing
foreground_image_crop_x_plus_widthnumeric225 unique values
400 missing
foreground_image_crop_ynumeric222 unique values
400 missing
foreground_image_crop_y_plus_heightnumeric222 unique values
400 missing
foreground_image_namestring26 unique values
400 missing
foreground_image_original_heightnumeric7 unique values
400 missing
foreground_image_original_widthnumeric5 unique values
400 missing
foreground_image_resized_heightnumeric7 unique values
400 missing
foreground_image_resized_widthnumeric5 unique values
400 missing
outline_image_crop_xnumeric103 unique values
640 missing
outline_image_crop_x_plus_widthnumeric103 unique values
640 missing
outline_image_crop_ynumeric103 unique values
640 missing
outline_image_crop_y_plus_heightnumeric103 unique values
640 missing
outline_image_namestring25 unique values
640 missing
outline_image_original_heightnumeric7 unique values
640 missing
outline_image_original_widthnumeric5 unique values
640 missing
outline_image_resized_heightnumeric7 unique values
640 missing
outline_image_resized_widthnumeric5 unique values
640 missing
background_polygon_fill_colorstring33 unique values
760 missing
background_polygon_outline_colorstring34 unique values
760 missing
background_random_color_composition_paramsstring40 unique values
760 missing
background_image_crop_xnumeric250 unique values
400 missing
background_image_crop_x_plus_widthnumeric250 unique values
400 missing
background_image_crop_ynumeric240 unique values
400 missing
background_image_crop_y_plus_heightnumeric240 unique values
400 missing
background_image_namestring26 unique values
400 missing
background_image_original_heightnumeric7 unique values
400 missing
background_image_original_widthnumeric5 unique values
400 missing
background_image_resized_heightnumeric7 unique values
400 missing
background_image_resized_widthnumeric5 unique values
400 missing
shear_ynumeric62 unique values
680 missing

19 properties

800
Number of instances (rows) of the dataset.
69
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
18840
Number of missing values in the dataset.
800
Number of instances with at least one value missing.
46
Number of numeric attributes.
1
Number of nominal attributes.
1.45
Percentage of binary attributes.
100
Percentage of instances having missing values.
-2732.29
Average class difference between consecutive instances.
34.13
Percentage of missing values.
0.09
Number of attributes divided by the number of instances.
66.67
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
1.45
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.

1 tasks

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