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ML Accelerated Data Annotation - Towards human-centric efficiency improvement in image annotation
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What's in a metric? Towards human-centric efficiency improvement in image annotation.
Through watching the webinar, you will discover factors influencing the length & effort required to produce annotated data & how a team can structure the annotation work to allow for incremental development & continuous progress. Learn about the combination of human input & the use of the DEXTR approach.
During this webinar, Samasource shared a list of factors influencing the length and effort required to produce annotated data. Through watching, you can discover how a team can structure the annotation work to allow for incremental development and continuous progress. With a focus on quality and scale, they will also cover the combination of human input and the use of the DEXTR approach with a raster-to-polygon conversion algorithm to yield high-quality polygons efficiently.
Key Takeaways from the webinar:
1. Discover how data annotation is key to ML project success
2. Learn about ML Assisted annotation and advanced analytics to improve efficiency
3. Learn how annotation instruction type has a large impact on quality and efficiency
Through watching the webinar, you will discover factors influencing the length & effort required to produce annotated data & how a team can structure the annotation work to allow for incremental development & continuous progress. Learn about the combination of human input & the use of the DEXTR approach.
During this webinar, Samasource shared a list of factors influencing the length and effort required to produce annotated data. Through watching, you can discover how a team can structure the annotation work to allow for incremental development and continuous progress. With a focus on quality and scale, they will also cover the combination of human input and the use of the DEXTR approach with a raster-to-polygon conversion algorithm to yield high-quality polygons efficiently.
Key Takeaways from the webinar:
1. Discover how data annotation is key to ML project success
2. Learn about ML Assisted annotation and advanced analytics to improve efficiency
3. Learn how annotation instruction type has a large impact on quality and efficiency
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