Computer Vision for Industrial Inspection
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.
- 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.
Developers who use Python
- Experience with Python; basic understanding of data processing and deep learning.
- To get a basic understanding of data processing and deep learning, we suggest the course \"Fundamentals of Deep Learning\"
Overview of course objectives and structure
Using NVIDIA DALI for efficient data loading and augmentation
Training and fine-tuning models using NVIDIA TAO Toolkit
Optimizing and deploying trained models for production inference
Knowledge check and interactive discussion