How to Improve Data and Modeling for Computer Vision Apps
In this session, Snorkel's own ML Research Scientist Ravi Teja Mullapudi explores the latest advancements in computer vision that enable data-centric image classification model development. He showcases how visual prompts and fast parameter-efficient models built on top of foundation models provide immediate feedback to rapidly iterate on data quality and model performance resulting in significant time-savings and performance improvements. Moreover, he delves into the importance of adapting model representations via large-scale fine-tuning on weakly labeled data to address the limitations of fast but small models trained on fixed features. Finally, he discusses the necessary scaling and model adaptations needed to transition from image-level classification to object-level detection and segmentation.
Overall, Ravi aims to provide insights into how computer vision data and models can be effectively improved in tandem and adjusted for downstream applications.
More related videos: • Foundation Models: The Future of AI
More related videos: • The Future of Data-Centric AI 2023
More related videos: • Data-centric AI | Snorkel AI
More related videos: • AI Data Development: Building Better ...
#computervision #machinelearning #datacentricai
