Computer Vision with Active Learning for Social Good (CD-CapB14)

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Computer Vision with Active Learning for Social Good (CD-CapB14)

Phase:	Theme
Theme:	Theme - Capacity Building (Theme - CapB), Activity - Curriculum Development (Activity - CD)
Status:	Active
Start Date:	2021-01-27
End Date:	2021-01-27
Lead
Taylor, Matthew

HQP

Nuttall, Rohan
Radi, Hager

Project Overview

Learning Objectives: What would the user learn completing the module

Being comfortable with accessing and creating analysis pipelines for image-based data.
Learning how to clean/prepare/balance datasets for training ML models
Getting familiar with different Python tools for data analysis and visualization
Getting familiar with PyTorch/Torch Vision
Applying classification models to medical image and/or satellite data.
Applying segmentation models to medical image and/or satellite data.
Loading pre-trained image models and performing transfer learning.
Understanding what interactive machine learning is, and why/how it is important in social good applications (i.e. counting/classifying biodiversity data, providing labels for unlabeled data, and asking a park ranger to assist when it is uncertain about a classification)
Familiarity with some of the state-of-the-art literature for active learning/computer vision for social good.

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