When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Computer Vision for Industrial Inspection
Whether companies are manufacturing semiconductor chips, airplanes, automobiles, smartphones, or food or beverages, quality and throughput are key benefits of optimization. Poor quality and...
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$500 USD
Course Code
NV-CV-II
Duration
1 day
Available Formats
Classroom
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.
Skills Gained
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.
Prerequisites
- 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"
Course Details
Workshop Outline
Introduction
Data Exploration and Pre-Processing with DALI
- Explore data set with Pandas
- Pre-process data with DALI
- Assess scope for feasibility testing
Efficient Model Training with TAO Toolkit
- Train a deep learning model with TAO Toolkit
- Evaluate the accuracy of the model
- Iterate model training to improve accuracy
Model Deployment for Inference
- Optimize deep learning models with TensorRT
- Deploy model with Triton Inference Server
- Explore and assess the impact of various inference configurations
Assessment and Q&A
