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Report: Computer vision teams worldwide say projects are delayed by insufficient data

Digital eye as an example of computer vision.
Image Credit: Yuichiro Chino // Getty Images

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According to new research by Datagen, 99% of computer vision (CV) teams have had a machine learning (ML) project canceled due to insufficient training data. Delays, meanwhile, appear truly ubiquitous, with 100% of teams reporting experiencing significant project delays due to insufficient training data. The research also indicates that these training data challenges come in many forms and affect CV teams in near-equal measure. The top issues experienced by CV teams include poor annotation (48%), inadequate domain coverage (47%), and simple scarcity (44%).

The scarcity of robust, domain-specific training data is only compounded by the fact that the field of computer vision is lacking many well-defined standards or best practices. When asked how training data is typically gathered at their organizations, respondents revealed a patchwork of sources and methodologies are being employed both across the field and within individual organizations. Whether synthetic or real, collected in-house or sourced from public datasets, organizations appear to be utilizing any and all data they can in order to train their computer vision models.

However, computer vision teams have already identified and begun to embrace synthetic data as a solution. Ninety-six percent of CV teams reported having already adopted the use of synthetic data to help train their AI/ML models. Nevertheless, the quality, source, and proportion of synthetic data that’s used remains highly variable across the field, and only 6% of teams currently use synthetic data exclusively.

Bar graph. Has your team experienced issues with training models? 52% said it was wasted time or resources caused by a need to retrain the system often. 48% said poor annotation resulting in quality issues. 47% said poor coverage of our domain in the collection process. 44% said lack of sufficient amount of data.

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This wave of synthetic data adoption is consistent with a number of recent industry reports predicting that 2022 will be a breakout year for synthetic data. This growing consensus certainly bodes well for computer vision’s many, eagerly awaited applications. In fact, it’s possible that these technologies are much closer to fruition than they may seem. Who knows? Perhaps we’re just a few good datasets away from a driverless world.

The report draws on the findings of an online survey of 300 computer vision professionals representing 300 unique businesses.

Read the full report by Datagen.