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2 posts tagged with "deep dive"

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· 6 min read
Ted Sandler

At Groundlight, we train each detector's machine learning (ML) model on images that have been manually labeled with correct responses. However, collecting labels at scale becomes expensive because it requires human review. Given that detectors are frequently applied to streams of images that change slowly over time, reviewing all images as they arrive is likely to result in effort wasted on labeling similar images that add little information to the training set.

· 24 min read
Ted Sandler
Leo Dirac

At Groundlight, we put careful thought into measuring the correctness of our machine learning detectors. In the simplest case, this means measuring detector accuracy. But our customers have vastly different performance needs since our platform allows them to train an ML model for nearly any Yes/No visual question-answering task. A single metric like accuracy is unlikely to provide adequate resolution for all such problems. Some customers might care more about false positive mistakes (precision) whereas others might care more about false negatives (recall).