๐ Guest post: How to Prioritize Data Quality for Computer Vision: An Expert Primer*
thesequence.substack.com
In this article, Superb AIโs team gives a tour of the data quality tooling landscape and proposes ideas to design a robust data quality tool for computer vision applications. With the rise of the data-centric AI movement (of which computer vision is a subset), the spotlight has been shifting from algorithm design to dataset development. Data is the highest contributor to model performance for many modern neural network architectures. Adding layers to the network, skipping connections, or tuning certain hyperparameters have limited model performance effects. Many practitioners spend countless hours creating and curating labeled data to train state-of-the-art architectures at the penalty of algorithm development. Additionally, dataset creation is one of the most costly and demanding components of the entire computation pipeline. Therefore, good practices for data quality are critical to ensuring successful outcomes.
๐ Guest post: How to Prioritize Data Quality for Computer Vision: An Expert Primer*
๐ Guest post: How to Prioritize Data Qualityโฆ
๐ Guest post: How to Prioritize Data Quality for Computer Vision: An Expert Primer*
In this article, Superb AIโs team gives a tour of the data quality tooling landscape and proposes ideas to design a robust data quality tool for computer vision applications. With the rise of the data-centric AI movement (of which computer vision is a subset), the spotlight has been shifting from algorithm design to dataset development. Data is the highest contributor to model performance for many modern neural network architectures. Adding layers to the network, skipping connections, or tuning certain hyperparameters have limited model performance effects. Many practitioners spend countless hours creating and curating labeled data to train state-of-the-art architectures at the penalty of algorithm development. Additionally, dataset creation is one of the most costly and demanding components of the entire computation pipeline. Therefore, good practices for data quality are critical to ensuring successful outcomes.