Vision models use cases with AI

The goal of this course is to teach participants how to prepare their own training data primarily for image segmentation, possibly also for image classification, and to show them creating of an own model of a neural network that specifically addresses their own image data and task.

Throughout the course, basic terms and theory will be explained and, above all, shared practical experience will be provided through short, light-weight lectures.

Benefits for the attendees, what will they learn:

• Prepare and annotate their own image datasets for segmentation and classification.
• Use Fiji and Labkit for manual and AI-assisted image segmentation.
• Train a neural network tailored to their own images and research tasks.
• Deploy trained models in Labkit or use them independently, both locally and remotely.
• Build privacy-conscious workflows using open-source tools suitable for commercial use.

Level

Intermediate

Technical requirements

• Data to work on will be freely available, though participants are welcome to bring their own data.
• Participants shall bring their own laptops. Power plugs will be available, operating systems Windows, Linux, and Macs are all supported, 5 GB of disk space shall be available for the course. 
• The laptops need to be set up prior to the course according to the provided video instructions. 
• Basic computer literacy is sufficient for creating annotations.