Video

Webinar: Optimizing Pose Estimation for Real-Time Performance

This is an absolute must-watch 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.

Watch now 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: Learn from pressing questions answered by our experts.


If you want to learn more about optimizing your computer vision applications, book a demo here.

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")