Optimizing Pose Estimation for Real-Time Performance

Description

This is an absolute must-attend for anyone serious about real-time pose estimation. Deploying pose estimation models in real-world applications often faces challenges related to computational constraints and real-time performance requirements. Additionally, varying environmental conditions and diverse human appearances can degrade the model's accuracy and robustness in practical scenarios. Join Eugene Khvedchenya, a Kaggle Grandmaster and deep learning engineer at Deci, for a live webinar that delves deep into the intricacies of pose estimation and offers unparalleled insights on how to optimize it for better accuracy and real-time performance. Join us to: • Discover common challenges in deployment of pose estimation models: Understand the limitations and ways to overcome them. • Learn the latest techniques & best practices: Stay updated with the most effective strategies in the industry. • Interactive Q&A: Have your pressing questions answered by our experts. Fill up the form to save your spot! Meet the speaker: Eugene Khvedchenia, Deep Learning Engineer, Deci.