From 57fba037d519e45488599288f7753cb7a3cd32aa Mon Sep 17 00:00:00 2001 From: adamhrv Date: Fri, 12 Apr 2019 09:09:13 +0200 Subject: merging --- site/public/datasets/msceleb/index.html | 139 -------------------------------- 1 file changed, 139 deletions(-) delete mode 100644 site/public/datasets/msceleb/index.html (limited to 'site/public/datasets/msceleb') diff --git a/site/public/datasets/msceleb/index.html b/site/public/datasets/msceleb/index.html deleted file mode 100644 index fd64189c..00000000 --- a/site/public/datasets/msceleb/index.html +++ /dev/null @@ -1,139 +0,0 @@ - - - - MegaPixels - - - - - - - - - - - -
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MegaPixels
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Microsoft Celeb
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- Datasets - About -
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MS Celeb is a dataset of web images used for training and evaluating face recognition algorithms
The MS Celeb dataset includes over 10,000,000 images and 93,000 identities of semi-public figures collected using the Bing search engine -
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Published
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2016
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Images
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1,000,000
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Identities
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100,000
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Purpose
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Large-scale face recognition
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Created by
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Microsoft Research
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Funded by
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Microsoft Research
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Website
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Microsoft Celeb Dataset (MS Celeb)

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[ PAGE UNDER DEVELOPMENT ]

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Who used Microsoft Celeb?

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- This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. -

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Biometric Trade Routes

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- To help understand how Microsoft Celeb has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Microsoft Celebrity Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. -

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  • Academic
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  • Commercial
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  • Military / Government
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Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
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Dataset Citations

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- The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. -

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Supplementary Information

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Additional Information

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  • "readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385.

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  • Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598.

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  • Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.

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