Whether companies are manufacturing semiconductor chips, airplanes, automobiles, smartphones, or food or beverages, quality and throughput are key benefits of optimization. Poor quality and throughput can result in significant operational, financial, and reputational costs. Deep learning-based computer vision technology enables manufacturers to perform automated visual inspection. Compared to traditional visual inspection processes—which are often manual and rules-based—visual inspection AI can improve efficiency, reduce operating costs, and deliver more consistent results.
In this Deep Learning Institute (DLI) workshop, developers will learn how to create an end-to-end hardware-accelerated industrial inspection pipeline to automate defect detection. Using NVIDIA’s own real production data set as an example, we’ll illustrate how the application can be easily applied to a variety of manufacturing use cases. Developers will also learn to identify and mitigate common pitfalls in deep learning-based computer vision tasks, and be able to deploy and measure the effectiveness of their AI solution.
All workshop attendees get access to fully configured, GPU-accelerated servers in the cloud, guidance from a DLI certified instructor, and the opportunity to network with other developers, data scientists, and researchers attending the workshop. Attendees can also earn a certificate to prove subject matter competency and support professional growth.
Learning Objectives
By participating in this workshop, you’ll learn how to:
- 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.
Download workshop datasheet (PDF 39.1 KB)
