Hands-on Data Centric Visual AI

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Hands-on Data Centric Visual AI

位教师：Harpreet Sahota

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4个模块

深入了解一个主题并学习基础知识。

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2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

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作业

12 项作业

授课语言：英语（English）

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该课程共有4个模块

This comprehensive course is a hands-on guide to developing and maintaining high-quality datasets for visual AI applications. Learners will gain in-depth knowledge and practical skills in: discovering and implementing various labeling approaches, from manual to fully automated methods; assessing and improving annotation quality for object detection tasks, including identifying and correcting common labeling issues; analyzing the impact of bounding box quality on model performance and developing strategies to enhance label consistency; use advanced tools like FiftyOne and CVAT for dataset exploration, error correction, and annotation refinement; addressing complex challenges in computer vision, such as overlapping detections, occlusions, and small object detection; implementing data augmentation techniques to improve model robustness and generalization; and applying concepts like sample hardness and entropy in the context of model training and dataset curation. Through a combination of theoretical knowledge and hands-on exercises, students will learn to create, maintain, and optimize datasets that lead to more accurate and reliable visual AI models.

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.

涵盖的内容

15个视频9篇阅读材料3个作业1个插件

15个视频总计109分钟

Module 1 Introduction2分钟
Introduction to Data Centric AI - Part 18分钟
Introduction to Data Centric AI - Part 27分钟
Understanding the Data and Model Feedback Loop - Part 16分钟
Understanding the Data and Model Feedback Loop - Part 27分钟
Understanding Visual AI Datasets - Part 18分钟
Understanding Visual AI Datasets - Part 23分钟
A Crash Course in Object Detection - Part 17分钟
A Crash Course in Object Detection - Part 27分钟
A Crash Course in Object Detection - Part 310分钟
Evaluation Metrics for Object Detection - Part 17分钟
Evaluation Metrics for Object Detection - Part 29分钟
Getting Started with FiftyOne - Part 19分钟
Getting Started with FiftyOne - Part 29分钟
Evaluating Baseline Model Performance12分钟

9篇阅读材料总计110分钟

Setting Up Your Environment30分钟
Recommended Resources10分钟
Recommended Resources10分钟
Recommended Resources10分钟
Recommended Resources10分钟
Recommended Resources10分钟
What to Expect in This Lesson10分钟
Required Reading10分钟
What to Expect in This Lesson10分钟

3个作业总计75分钟

Module 1 Quiz30分钟
FiftyOne Quiz15分钟
FiftyOne Quiz30分钟

1个插件总计6分钟

Course Introduction and Getting Started6分钟

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.

涵盖的内容

17个视频10篇阅读材料5个作业4个讨论话题

17个视频总计139分钟

Module 2 Introduction2分钟
Exploring Your Dataset with FiftyOne - Part 17分钟
Exploring Your Dataset with FiftyOne - Part 29分钟
Analyzing Image Quality - Part 16分钟
Analyzing Image Quality - Part 28分钟
Analyzing Image Quality with FiftyOne11分钟
Detecting Outliers in Your Dataset9分钟
Part 2: Detecting Outliers with FiftyOne10分钟
Finding Duplicates and Near Duplicates9分钟
Finding Duplicates and Near Duplicates with FiftyOne8分钟
Semantic Scores and Scene Diversity9分钟
Semantic Scores and Scene Diversity in FiftyOne - Part 19分钟
Semantic Scores and Scene Diversity in FiftyOne - Part 210分钟
Developing a Data Centric AI Strategy - Part 17分钟
Developing a Data Centric AI Strategy - Part 28分钟
Developing a Data Centric AI Strategy - Part 39分钟
Lesson 7: Tracking Experiments7分钟

10篇阅读材料总计120分钟

Exploring Your Dataset with FiftyOne: A Comprehensive Guide10分钟
Understanding Image Quality Metrics for Enhanced Object Detection10分钟
Recommended Resources30分钟
Detecting Outliers in Your Dataset: A Comprehensive Guide10分钟
Understanding and Managing Duplicate and Near Duplicate Images in Visual AI10分钟
Recommended Reading10分钟
Understanding Semantic Scores and Scene Diversity for Image Analysis10分钟
Recommended Reading10分钟
Crafting a Data-Centric AI Strategy for Better Visual AI Systems10分钟
What to Expect in This Lesson10分钟

5个作业总计75分钟

Module 2 Quiz25分钟
FiftyOne Quiz15分钟
FiftyOne Quiz10分钟
FiftyOne Quiz15分钟
FiftyOne Quiz10分钟

4个讨论话题总计40分钟

Image Quality Issues10分钟
Stratified Sampling10分钟
Experiment Management10分钟
Drop a Link to Your Curated Dataset10分钟

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.

涵盖的内容

11个视频6篇阅读材料4个作业3个讨论话题

11个视频总计79分钟

Module 3 Introduction2分钟
Labeling of Visual AI Datasets - Part 16分钟
Labeling of Visual AI Datasets - Part 26分钟
Handling Labeling Issues - Part 111分钟
Handling Labeling Issues - Part 210分钟
Overlapping Detections and Occlusions - Part 18分钟
Overlapping Detections and Occlusions - Part 29分钟
Overlapping Detections and Occlusions - Part 37分钟
Overlapping Detections and Occlusions - Part 47分钟
Handling Small Objects8分钟
Using SAHI in FiftyOne6分钟

6篇阅读材料总计55分钟

Recommended Reading5分钟
Hard Samples (Written Lecture)10分钟
Understanding Overlapping Detections and Occlusions in Modern Computer Vision10分钟
Understanding the Challenge of Small Object Detection10分钟
Augmentation for Small Object Detection10分钟
Recommended and Required Reading10分钟

4个作业总计50分钟

Module 3 Quiz15分钟
FiftyOne Quiz15分钟
FiftyOne Quiz10分钟
FiftyOne Quiz10分钟

3个讨论话题总计25分钟

Annotating Datasets in CVAT5分钟
Challenges Due to Occlusions10分钟
Data Augmentation Strategies for Small Objects10分钟

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.

涵盖的内容

5个视频4篇阅读材料1个讨论话题

5个视频总计19分钟

Module 4 Introduction2分钟
Rethinking Object Detection Evaluation6分钟
Finding the Optimal Confidence Threshold4分钟
Model Comparison7分钟
Course Summary1分钟

4篇阅读材料总计35分钟

Rethinking Object Detection Evaluation: Transitioning from mAP to F1 Scores10分钟
What To Expect In This Lesson10分钟
End of Course Assignment with FiftyOne10分钟
End of Course Assignment - Answer notebook5分钟

1个讨论话题总计5分钟

Share a Link to Your Curated Dataset5分钟

位教师

Harpreet Sahota

University of California, Davis

1 门课程970 名学生

提供方

University of California, Davis

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