Explore pose estimation techniques and their implementation using the OpenVino Toolkit in this 50-minute video tutorial. Dive into the world of computer vision as you learn about Pose Introduction and examine practical examples utilizing MoveNet and Multi-person 3D human pose estimation, all executed on CPU. Follow along with hands-on demonstrations and access the accompanying notebook for a deeper understanding of the concepts. Gain valuable insights into MLOps practices and enhance your skills in data science and machine learning through this comprehensive exploration of pose estimation and inference techniques.
MLOps: OpenVino Toolkit for Pose Estimation and Inference on CPU
The Machine Learning Engineer via YouTube
Overview
Syllabus
MLOps: OpenVino Toolkit Pose Estimation and Inference in CPU #datascience #machinelearning
Taught by
The Machine Learning Engineer
Related Courses
-
Converting Qwen2-VL 2B Model to OpenVino IR Format for CPU Inference
-
LLMOps: Comparison of OpenVino, ONNX, TensorRT, and PyTorch Inference
-
MLOps: OpenVino Quantized Pipeline for Grammatical Error Correction
-
CPU Inference with ViT ONNX Model in Azure ML Managed Endpoint - AKS
-
BitNet.cpp - Inferencia de Modelos de hasta 100B en CPU
-
BitNet.cpp - CPU Inference Framework for 1-bit Large Language Models
