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Deep learning engineers are some of the most highly paid professionals in the world. This is because their time and skills are in high demand. Businesses and organizations know that deep learning is a powerful tool, and they are willing to pay top dollar for experts who can harness its power.
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BOUNDING.AI COMPUTERVISIONDATASETS
ComputerVisionDatasets Deep learning engineers are some of the most highly paid professionals in the world. This is because their time and skills are in high demand. Businesses and organizations know that deeplearningisapowerfultool,andtheyarewillingtopaytop dollarforexpertswhocanharnessitspower.Butdeeplearning engineers need one thing to be effective – training data. By providingaone-stop-shopfortrainingdata,Bounding.aimakes AIteamsmoreeffective.
CuratedDatasetsForComputer Vision CORROSIONDETECTIONDATASET ADatasetsOf FortunellaMargarita ANTDETECTIONANDTRACKINGDATASET
CorrosionDetectionDataset Thedatasetcontainsimageof metalobjectsthathavebeen corrodedtovaryingdegrees. The objective is to detect the extentofcorrosionineach,in ordertoassesstheseverityof theproblem.
ADatasetOfFortunella Margarita TheFortunellaMargarita dataset is composed of fruit at different growth stages. The dataset is sorted into two types of files: (1) Growth stage classificationimagesand(2)Manuallabelingand annotationfiles. The Growth stage classification contains that have been divided into seven categories: (a) mature, (b) immature, (C) growing, (d) containing mature and immatureinoneimage,(e)matureandgrowing,(f) immatureandgrowing.
AntDetectionAndTracking Dataset Thisdatasetcontainssequential of an ant colony in both indoor andoutdoorenvironments. Images were collected over severalweeks.Thisdatasetcanbe usedtotrainmulti-targettracking AImodels.dasubheading
WhyareComputerVisionDatasets importantforAI? DataimprovestheaccuracyofAIalgorithms.Computer visionalgorithmsshouldbetrainedonlargedatasets. Training datasets provides a benchmark for differentAIalgorithms.Whennewcomputer visionalgorithmsarecreated. Moredataimprovestherobustnessofcomputer vision algorithms. This is because training data typicallycontainsavarietyofimages.
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