How to Validate a Deep Learning System in Manufacturing

Deep learning has become an indispensable technology in manufacturing, but a significant amount of work must be done up front to validate the system.

Although labor- and time-intensive, validation is critical to ensuring the success of your deep learning deployment. There are four primary phases involved in the process:

Planning
Data Collection and Ground Truth Labeling
Optimization
Factory Acceptance Testing

This white paper details each phase, providing the guidance you need to optimize deep learning in your manufacturing environment.

deep learning validation white paper flipbook image

Enter your email for free access to Cognex.com white papers, literature, software and downloads.

Email Address

If you have registered before, please use the same email address.

Do you have a vision project?

Yes, within 6 months
Yes, within 6 - 12 months
Yes, longer term
No

Type of application?

Would you like information on product pricing?

Да

I would like to sign up to receive phone, email and other electronic updates from Cognex

At Cognex, your privacy is important to us. Any personal data is used for legitimate business purposes. If you'd like to know more about how we use your personal information, read our privacy statement.