Nanopublications

@prefix this: <https://w3id.org/np/RAiGNY8Fcx_z-eCud19CqsOCgjPI-SzwWLXagaNQGICjY> .
@prefix sub: <https://w3id.org/np/RAiGNY8Fcx_z-eCud19CqsOCgjPI-SzwWLXagaNQGICjY/> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix nt: <https://w3id.org/np/o/ntemplate/> .
@prefix npx: <http://purl.org/nanopub/x/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix orcid: <https://orcid.org/> .
@prefix ns1: <http://purl.org/np/> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .

sub:Head {
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    np:hasProvenance sub:provenance ;
    np:hasPublicationInfo sub:pubinfo ;
    a np:Nanopublication .
}

sub:assertion {
  sub:assertion dct:creator orcid:0000-0002-1784-2920 ;
    <http://purl.org/spar/cito/citesAsRecommendedReading> <https://doi.org/10.1109/CVPR.2009.5206848> ;
    rdfs:comment "This 2009 paper presents ImageNet, a large-scale image database with 3.2M images across 5,247 categories organized using WordNet's hierarchy. Using web crawling and Amazon Mechanical Turk for quality control, they achieved 99.7% labeling accuracy. The authors demonstrate ImageNet's utility through object recognition, hierarchical classification, and localization experiments, providing a valuable resource for computer vision research and algorithm benchmarking." .
}

sub:provenance {
sub:assertion prov:wasAttributedTo orcid:0000-0002-1784-2920 .
}

sub:pubinfo {
  orcid:0000-0002-1784-2920 foaf:name "Anne Fouilloux" .
  this: dct:created "2025-07-03T12:15:12.533Z"^^xsd:dateTime ;
    dct:creator orcid:0000-0002-1784-2920 ;
    dct:license <https://creativecommons.org/licenses/by/4.0/> ;
    npx:hasNanopubType <http://purl.org/spar/cito/cites> ;
    npx:wasCreatedAt <https://nanodash.knowledgepixels.com/> ;
    rdfs:label "cites as recommended reading CVPR.2009.5206848" ;
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