Training data cleaning method, portrait segmentation network training method and portrait segmentation method
The invention is suitable for the technical field of data processing, and provides a training data cleaning method, a portrait segmentation network training method and a portrait segmentation method, and the training data cleaning method comprises the steps: obtaining a first training data set; wher...
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creator | MO YAOYANG |
description | The invention is suitable for the technical field of data processing, and provides a training data cleaning method, a portrait segmentation network training method and a portrait segmentation method, and the training data cleaning method comprises the steps: obtaining a first training data set; wherein the first training data set comprises training images containing face information; performing face detection on the training image to obtain face labeling information and face orientation information corresponding to the face labeling information; the training images with the face orientation information not meeting the preset orientation condition in the first training data set are removed, and a second training data set is obtained; calculating a human face area proportion corresponding to the training image in the second training data set according to the human face labeling information; and the training images of which the face area proportions do not meet a preset proportion condition in the second trainin |
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wherein the first training data set comprises training images containing face information; performing face detection on the training image to obtain face labeling information and face orientation information corresponding to the face labeling information; the training images with the face orientation information not meeting the preset orientation condition in the first training data set are removed, and a second training data set is obtained; calculating a human face area proportion corresponding to the training image in the second training data set according to the human face labeling information; and the training images of which the face area proportions do not meet a preset proportion condition in the second trainin</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20221202&DB=EPODOC&CC=CN&NR=115424325A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25563,76418</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20221202&DB=EPODOC&CC=CN&NR=115424325A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>MO YAOYANG</creatorcontrib><title>Training data cleaning method, portrait segmentation network training method and portrait segmentation method</title><description>The invention is suitable for the technical field of data processing, and provides a training data cleaning method, a portrait segmentation network training method and a portrait segmentation method, and the training data cleaning method comprises the steps: obtaining a first training data set; wherein the first training data set comprises training images containing face information; performing face detection on the training image to obtain face labeling information and face orientation information corresponding to the face labeling information; the training images with the face orientation information not meeting the preset orientation condition in the first training data set are removed, and a second training data set is obtained; calculating a human face area proportion corresponding to the training image in the second training data set according to the human face labeling information; and the training images of which the face area proportions do not meet a preset proportion condition in the second trainin</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZMgNKUrMzMvMS1dISSxJVEjOSU0E83JTSzLyU3QUCvKLSoAqShSKU9NzU_NKEksy8_MU8lJLyvOLshVKYJohyhUS81Jw6IAo4GFgTUvMKU7lhdLcDIpuriHOHrqpBfnxqcUFicmpQKPjnf0MDU1NjEyMjUwdjYlRAwDwbUMv</recordid><startdate>20221202</startdate><enddate>20221202</enddate><creator>MO YAOYANG</creator><scope>EVB</scope></search><sort><creationdate>20221202</creationdate><title>Training data cleaning method, portrait segmentation network training method and portrait segmentation method</title><author>MO YAOYANG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN115424325A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>MO YAOYANG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>MO YAOYANG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Training data cleaning method, portrait segmentation network training method and portrait segmentation method</title><date>2022-12-02</date><risdate>2022</risdate><abstract>The invention is suitable for the technical field of data processing, and provides a training data cleaning method, a portrait segmentation network training method and a portrait segmentation method, and the training data cleaning method comprises the steps: obtaining a first training data set; wherein the first training data set comprises training images containing face information; performing face detection on the training image to obtain face labeling information and face orientation information corresponding to the face labeling information; the training images with the face orientation information not meeting the preset orientation condition in the first training data set are removed, and a second training data set is obtained; calculating a human face area proportion corresponding to the training image in the second training data set according to the human face labeling information; and the training images of which the face area proportions do not meet a preset proportion condition in the second trainin</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTING COUNTING PHYSICS |
title | Training data cleaning method, portrait segmentation network training method and portrait segmentation method |
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