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1、TheiNaturalistSpeciesClassificationandDetectionDatasetGrantVanHorn1OisinMacAodha1YangSong2YinCui3ChenSun2AlexShepard4HartwigAdam2PietroPerona1SergeBelongie31Caltech2Google3CornellTech4iNaturalistAbstractExistingimageclassificationdatasetsusedincomputervisiontendtohaveauniformdistributiono
2、fimagesacrossobjectcategories.Incontrast,thenaturalworldisheavilyimbalanced,assomespeciesaremoreabundantandeasiertophotographthanothers.ToencouragefurtherprogressinchallengingrealworldconditionswepresenttheiNatural-istspeciesclassificationanddetectiondataset,consistingof859,000imagesfrom
3、over5,000differentspeciesofplantsandanimals.Itfeaturesvisuallysimilarspecies,capturedinawidevarietyofsituations,fromallovertheworld.Im-ageswerecollectedwithdifferentcameratypes,havevary-ingimagequality,featurealargeclassimbalance,andhavebeenverifiedbymultiplecitizenscientists.Wediscussth
4、ecollectionofthedatasetandpresentextensivebaselineex-Figure1.TwovisuallysimilarspeciesfromtheiNat2017dataset.perimentsusingstate-of-the-artcomputervisionclassifica-Throughcloseinspection,wecanseethattheladybugonthelefttionanddetectionmodels.Resultsshowthatcurrentnon-hastwospotswhiletheon
5、eontherighthasseven.ensemblebasedmethodsachieveonly67%toponeclassi-ficationaccuracy,illustratingthedifficultyofthedataset.Itisestimatedthatthenaturalworldcontainsseveralmil-Specifically,weobservepoorresultsforclasseswithsmalllionspecieswitharound1.2millionofthesehavingalreadynumbersoftrain
6、ingexamplessuggestingmoreattentionisbeenformallydescribed[26].Forsomespecies,itmayonlyneededinlow-shotlearning.bepossibletodeterminethespeciesviageneticsorbydis-section.Fortherest,visualidentificationinthewild,whilepossible,canbeextremelychallenging.Thiscanbedueto1.Introductionthesheernu
7、mberofvisuallysimilarcategoriesthatanindi-vidualwouldberequiredtorememberalongwiththechal-Performanceonexistingimageclassificationbench-lenginginter-classsimilarity;seeFig.1.Asaresult,theremarkssuchas[32]isclosetobeingsaturatedbythecur-isacriticalneedforrobustandaccurateautomate
