Special Edition - Scaling dataset labeling with quality and speed

Getting quality labeled data, and getting it fast, is often the obstacle to getting good results from experimental models. In order to stay agile, research teams and startups alike need access to the best training data possible in the shortest amount of time.

When lumber prices rose drastically during the Covid-19 pandemic, measuring the weight, dimensions, and quality of lumber became more important than ever before. Measuring, or scaling lumber has historically been done manually. It’s a dangerous process, requiring individuals to work in environments where logs are piled high, people and heavy machinery work in close proximity. TimberEye provides a mobile application that leverages the latest in computer vision and LiDAR mapping technology to enable lumber suppliers as well as buyers to categorize and scale logs up to 20x faster, more safely and consistently, and with better accuracy. Typical human operators will differ from verified measurements by 1.2 - 1.5cm, but the TimberEye app delivered highly accurate measurements with an average difference to verified manual measurements of just 0.3cm.

To continue to enhance the app’s log scaling capabilities, the TimberEye team wanted to experiment with an instance segmentation model. But semantically segmenting images was a laborious and time-consuming process that delayed experimentation.

Scott Gregg, CEO and Founder of TimberEye said that they kept kicking the segmentation work to the back burner, almost abandoning it. But, three days after kicking off the project with Rapid, they had all the data they needed in perfect shape.

Scale was founded to solve the challenge of scaling data labeling pipelines to production-level volumes.

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