Optimizing Pose Estimation for
Real-Time Performance

About the Webinar

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.

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Meet the speaker