To provide participants with a comprehensive understanding of the importance of training data, methods to obtain it, tools for annotation, and potential data sources.
Breakdown
Introduction to Training Data
What is training data and why is it crucial?
Differences between labeled and unlabeled data
The Importance of Quality Annotations
How annotations impact model performance
Common challenges: Inconsistent annotations, class imbalance, etc.
Strategies to ensure high-quality annotations: Guidelines, multiple annotators, quality checks
Methods to Obtain Training Data
Creating your own dataset: Pros, cons, and considerations
Using pre-existing datasets: Benefits and potential pitfalls
Data augmentation: Expanding dataset size and diversity
Transfer learning and pre-trained models: Leveraging external knowledge
Annotation Tools
Overview of popular annotation tools: Labelbox, VGG Image Annotator (VIA), RectLabel, etc.
Features to consider: Collaboration, format export options, automation capabilities
Hands-on demo: Annotating a sample image using a chosen tool
Data Sources for RTS and Arctic Science (15 minutes)
Public datasets relevant to arctic science and RTS
Collaborative efforts and data-sharing initiatives in the research community
Ethical considerations: lesson 12
Q&A and Discussion
Encouraging sharing of personal experiences or challenges with data annotation
Discussing potential future developments in annotation tools and techniques
Data annotations for deep learning
Goal
To provide participants with a comprehensive understanding of the importance of training data, methods to obtain it, tools for annotation, and potential data sources.
Breakdown