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Official Press Release

Recycleye taps Keymakr to train ML models, reducing reusable material in landfills

Keymakr’s video and annotation tool empowers Recycleye to identify edge cases that have led to a reduction in recyclable material going into landfills

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Holon, Israel, December, 2022Keymakr, a data-annotation service provider delivering high quality datasets to AI innovators, partners with Recycleye, a developer of digital tools for detecting and providing analytics on waste management, to help reduce the amount of recyclable materials that go into landfills. Through the collaboration, Keymakr helps Recycleye further optimize its waste management sorting tech by developing machine learning (ML) models to label and identify “edge cases.”

Despite the growing prevalence of recycling facilities and infrastructure in many parts of the world, an estimated 91 percent of plastics end up in landfills. Waste-management sorting facilities aim to reduce the amount of waste getting buried in landfills by separating anything that can be reused, including for industrial and agricultural purposes. With the goal of promoting a healthier environment and being thrifty, sorting waste comes with many inherent risks. Dividing waste from reusable materials is a grueling and dangerous task that leaves workers vulnerable to harmful substances and contaminants, workplace injuries, and other dangers.


Recycleye leverages Keymakr’s advanced computer vision AI to assist in running its ML models. Recycleye sends Keymakr relevant data to be labeled prior to it being returned and then integrated into the ML models. Part of this process involves Keymakr helping identify edge cases that occur during the sorting process, allowing the ML models to better recognize objects that otherwise would have been sorted incorrectly. Through this collaboration, waste management sorting facilities are able to recycle more materials.  

The partnership with Recycleye comes on the heels of Keymakr’s partnership with SeeChange and the launch of Keylabs, its purpose-built SaaS platform. Keylabs enables all companies, regardless of whether they have data-annotation teams, to leverage Keymakr’s uniquely effective annotation capabilities. Keylabs’s easy-to-use interface improves the speed of annotation and data sets while ensuring accuracy through its QA abilities.  

“It’s a pleasure to work with such an amazing project like that of Recycleye. Not only are they an innovative startup developing an amazing product, but they are making no small contribution to humanity by helping keep our planet healthy by optimizing recycling,” says Arie Zilberman, CEO and Co-Founder of Keymakr. “With our training data for computer vision AI and Recycleye’s expertise in making waste management sorting efficient, we are revolutionizing waste sorting.”


About Keymakr:

Founded in 2015, Keymakr was created as a response to a need for high-quality and affordable training data for computer vision-based AI. Keymakr’s mission is to contribute to building and

shaping better technology. They believe AI can perform only as well as the training data that is used to create it, and that always starts with a human touch, and if done properly, data annotation has limitless potential. 

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About Recycleye: 

Founded in 2019 by Victor Dewulf and Peter Hedley, Recycleye uses advanced machine learning, computer vision, and robotics to bring transparency, traceability, and accountability to the waste management industry. Recycleye prevents valuable recyclates from being downcycled, a result of inefficient and ineffective manual labor. The company empowers recycling facilities to increase the purity and subsequent value of their output, increasing the resale value of bales fivefold. By lowering operational expenditure, Recycleye can save facilities up to £2 million annually. The company counts Microsoft, NVIDIA, Imperial College London, and FANUC as key technological and strategic partners. 

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Arie Zilberman - CEO and Co-Founder of Keymakr
Michael Seldin - CTO & Co-Founder of Keymakr
Michael Abramov - Co-CEO of Keylabs

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