University of California, Davis
Hands-on Data Centric Visual AI
University of California, Davis

Hands-on Data Centric Visual AI

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

21 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

21 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace

Details to know

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Recently updated!

September 2024

Assessments

12 assignments

Taught in English

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There are 4 modules in this course

At the end of this module, you will be able to describe the data-centric AI paradigm and its importance in modern deep learning workflows. You will be able to explain the data and model feedback loop in the context of object detection and instance segmentation tasks. You'll be able to apply FiftyOne to evaluate initial model performance for object detection and instance segmentation tasks. You'll be able to interpret common evaluation metrics for object detection and instance segmentation models.

What's included

7 videos18 readings3 assignments1 discussion prompt

After this module, you will be able to analyze dataset statistic to gain a holistic understanding of the data. You will be able to identify and assess various image quality issues that can impact model performance. You will be able to use FiftyOne to detect and visualize image quality problems, outliers, and diversity issues. And finally, you'll be able to develop strategies to address identified image quality and diversity issues.

What's included

11 videos21 readings5 assignments5 discussion prompts

After this module, you will be able to assess the quality of annotations for object detection tasks. You'll be able to identify common labeling issues such as mislabeled data, hard samples, and occlusions. You will be able to analyze the impact of bounding box on model performance and develop strategies to improve label quality and consistency.

What's included

6 videos12 readings4 assignments3 discussion prompts

After this module, you will be able to apply advanced data-centric AI techniques such as data augmentation and active learning. You will be able to implement an end-to-end workflow for iterative model improvement using FiftyOne. You will be able to develop a strategy for maintaining dataset quality over time and finally be able to synthesize and apply techniques to improve model performance on a given dataset.

What's included

3 videos6 readings2 discussion prompts

Instructor

Harpreet Sahota
University of California, Davis
1 Course28 learners

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