Computer Vision for Industrial Inspection

Computer Vision for Industrial InspectionCVIINVNvidiaNV-CVII1.0<ul> <li>Extract meaningful insights from the provided data set using Pandas DataFrame.</li><li>Apply transfer-learning to a deep learning classification model.</li><li>Fine-tune the deep learning model and set up evaluation metrics.</li><li>Deploy and measure model performance.</li><li>Experiment with various inference configurations to optimize model performance.</li></ul><ul> <li>Experience with Python; basic understanding of data processing and deep learning.</li><li>To gain experience with Python, we suggest this Python tutorial.</li><li>To get a basic understanding of data processing and deep learning, we suggest DLI’s Fundamentals of Deep Learning.</li></ul>Introduction <ul> <li>Meet the instructor.</li><li>Create an account at courses.nvidia.com/join</li></ul>Data Exploration and Pre-Processing with DALI <ul> <li>Learn how to extract valuable insights from a data set and pre-process image data for deep learning model consumption.</li><li>Explore data set with Pandas</li><li>Pre-process data with DALI</li><li>Assess scope for feasibility testing</li></ul>Efficient Model Training with TAO Toolkit <ul> <li>Learn how to efficiently train a classification model for the purpose of defect detection using transfer learning techniques</li><li>Train a deep learning model with TAO Toolkit</li><li>Evaluate the accuracy of the model</li><li>Iterate model training to improve accuracy</li></ul>Model Deployment for Inference <ul> <li>Learn how to deploy and measure the performance of a deep learning model</li><li>Optimize deep learning models with TensorRT</li><li>Deploy model with Triton Inference Server</li><li>Explore and assess the impact of various inference configurations</li></ul>Assessment and Q&A- Extract meaningful insights from the provided data set using Pandas DataFrame. - Apply transfer-learning to a deep learning classification model. - Fine-tune the deep learning model and set up evaluation metrics. - Deploy and measure model performance. - Experiment with various inference configurations to optimize model performance.- Experience with Python; basic understanding of data processing and deep learning. - To gain experience with Python, we suggest this Python tutorial. - To get a basic understanding of data processing and deep learning, we suggest DLI’s Fundamentals of Deep Learning.Introduction - Meet the instructor. - Create an account at courses.nvidia.com/join Data Exploration and Pre-Processing with DALI - Learn how to extract valuable insights from a data set and pre-process image data for deep learning model consumption. - Explore data set with Pandas - Pre-process data with DALI - Assess scope for feasibility testing Efficient Model Training with TAO Toolkit - Learn how to efficiently train a classification model for the purpose of defect detection using transfer learning techniques - Train a deep learning model with TAO Toolkit - Evaluate the accuracy of the model - Iterate model training to improve accuracy Model Deployment for Inference - Learn how to deploy and measure the performance of a deep learning model - Optimize deep learning models with TensorRT - Deploy model with Triton Inference Server - Explore and assess the impact of various inference configurations Assessment and Q&A1 jour500.00500.00500.00500.00500.00420.00500.00690.00