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Product
Image AnnotationVideo AnnotationText AnnotationProject Management
AutomationData CurationQuality ManagementSDK Integration

Services
Industries
AgricultureAutonomous DrivingRoboticsAerial ImageryNLP
industryHealthcareSportsInsuranceSecurity

Pricing
Resources
DocumentationProduct UpdatesBlogWebinarPodcast

Company
About UsSecurityCareersPress

Sign inRequest demo


November 1, 9am PST / 5pm CET


DATA-CENTRIC APPROACH TO CV DATASETS

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WEBINAR HOSTS


OSCAR ÖRNBERG

Solutions Engineering Team Lead at SuperAnnotate


DAVID ARAKELYAN

ML Project Coordinator


VAHAN PETROSYAN

Co-founder & CTO at SuperAnnotate


WEBINAR SPEAKER


SALLY-ANN DELUCIA

AI Solutions Engineer at SuperAnnotate

Training dataset health and overall conditions often are not the focal points
for many computer vision teams. In our experience, when exploring ways of
improving model performance CV teams will most likely opt for solutions like
tuning hyperparameters than exploring datasets for flows.

At SuperAnnotate we tried taking a data-centric view and see what impact that
will have on our computer vision model’s performance.

During this webinar, we’ll be looking at the state-of-art methods for getting
the most out of training data and observing the impact that will have on the
model’s performance.

 * Named Entity Recognition/NLP (bounding box annotation of named entities)
 * Optical Character Recognition/OCR (character annotation of the words from the
   annotated bounding box)


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