Data Remains the Key Challenge In Computer Vision Projects

Datagen recently surveyed about 300 professionals in computer vision about the value of data. The survey comes at a time of renewed focus on the importance of tools for helping ML teams address data related challenges. Data-centric AI represents a recent shift among researchers, away from focusing on models and toward the underlying data used in model training and model evaluation.  The good news is that research papers and workshops in data-centric AI are already leading to compelling tools that help practitioners address their data-related challenges.

The fact that focusing on data is more impactful than focusing on modeling has long been known to ML engineers and other data professionals. Numerous surveys through the years have shown that data teams spend most of their time working on acquiring, cleaning, and augmenting their data sets. The new survey from Datagen reinforces the importance of investing in tools and services to address data challenges (in computer vision and beyond):

Data from “Synthetic Data: Key to Production-Ready AI in 2022” by

To alleviate the challenges posed by the lack of high-quality training data, results from survey indicate that computer vision teams are beginning to turn to synthetic data – data generated by computer simulations or algorithms. You can download the full survey HERE.

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