Useful information and code to help open science AI/ML efforts for marine ecosystems
Workshop Goals:
- Equip users with a shared understanding of AI/ML workflows and fundamentals
- Define how annotations fit into the model lifecycle
- Build & train basic models using marine datasets.
- Explore training results and graphs.
- Deploy models
- Quick primer on object detection pipeline (data → annotation → model training → deployment)
- Role of annotations in model performance (importance of quality & consistency)
- Where to find data, resouces, and so on?
- Best practices for efficient annotation workflow
- Review tools(CVAT, Label Studio, Viame) and discuss pros/cons
- Discuss integrating QA/QC checks and techniques
- Exploratory data analysis(EDA)
- Annotation
- Dataset Prep
- Model Development & Training
- Model Deployment
This repository is a scientific product and is not official communication of the National Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA GitHub project content is provided on an ‘as is’ basis and the user assumes responsibility for its use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from the use of this GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.
- Details in the LICENSE.md file.