How to Curate, Annotate, and Improve Computer Vision Datasets with FiftyOne and Labelbox

The integration of FiftyOne and Labelbox provides an efficient solution for building high-quality image and video datasets essential for modern computer vision projects. By leveraging FiftyOne's capabilities in dataset curation and model analysis alongside Labelbox's annotation tools, users can streamline the process of preparing data for model training. The workflow involves installing both tools, setting up accounts, and loading raw data, which can be sourced from public datasets like the Open Images dataset or downloaded locally. FiftyOne allows users to visualize and assess data quality, ensuring an even distribution of samples for annotation. The integration simplifies annotation by enabling seamless data transfer between FiftyOne and Labelbox, with advanced customization options for annotation tasks. Once annotated, datasets can be exported for model training, and FiftyOne's querying capabilities help evaluate model performance and identify areas for dataset improvement. This iterative process enhances model accuracy by addressing annotation errors and refining data quality, ultimately leading to the development of high-performing models.