Greyparrot undertook the task of expanding its waste recognition library to encompass 89 categories, enabling a finer analysis of various waste streams.
SUPA's technological infrastructure optimized the data labeling pipeline, slashing the startup time from 2-3 weeks to a mere 24 hours, all while maintaining stringent data quality standards.
Drawing upon SUPA's proficiency in data annotation, Greyparrot extended its waste recognition library from 49 to 89 categories; and it doesn’t stop there.
“With the expansion to 89 classes, this granular data unlocks further digitisation and automation capabilities for recycling operations as well as provides rich data for the waste ecosystem. SUPA's labeling support has been instrumental in our accelerated progress.”
– Dominic Calina, Head of Data at Greyparrot.ai
Recognizing waste objects accurately is pivotal in optimizing recycling processes. Greyparrot faced the challenge of enhancing its waste recognition library to provide more granular analysis of waste streams, enabling recycling facilities to improve efficiency and sustainability efforts.
Partnering exclusively with SUPA, Greyparrot embarked on a journey to enrich its AI models with high-quality labeled data. SUPA's specialized annotators, equipped with domain knowledge, collaborated closely with Greyparrot to continuously scale classes, ensuring the accuracy and relevance of the data. Additionally, SUPA's tech infrastructure streamlined the data labeling pipeline, reducing start-up time from 2-3 weeks to just 24 hours, while upholding data quality standards.
The ongoing collaboration between Greyparrot and SUPA yielded profound results:
This exclusive partnership propelled Greyparrot to expand its waste recognition capabilities, consequently reshaping the waste recycling industry.
SUPA scales high-quality annotation output during seasonal data surges by 170% for a global agritech company that manages over 200 million trees
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