When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Hands-on Data Centric Visual AI

4 days left! Save on skills that make you shine with 40% off 3 months of Coursera Plus. Save now

Hands-on Data Centric Visual AI

Instructor: Harpreet Sahota

Included with

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

AI Workflows

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments

Taught in English

91% of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

There are 4 modules in this course

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.

Module details

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

15 videos9 readings3 assignments1 plugin

15 videosTotal 109 minutes

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

9 readingsTotal 110 minutes

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

3 assignmentsTotal 75 minutes

FiftyOne Quiz15 minutes
FiftyOne Quiz30 minutes
Module 1 Quiz30 minutes

1 pluginTotal 6 minutes

Course Introduction and Getting Started6 minutes

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

17 videos10 readings5 assignments4 discussion prompts

17 videosTotal 139 minutes

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

10 readingsTotal 120 minutes

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

5 assignmentsTotal 75 minutes

FiftyOne Quiz15 minutes
FiftyOne Quiz10 minutes
FiftyOne Quiz15 minutes
FiftyOne Quiz10 minutes
Module 2 Quiz25 minutes

4 discussion promptsTotal 40 minutes

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

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

11 videos6 readings4 assignments3 discussion prompts

11 videosTotal 79 minutes

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

6 readingsTotal 55 minutes

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

4 assignmentsTotal 50 minutes

FiftyOne Quiz15 minutes
FiftyOne Quiz10 minutes
FiftyOne Quiz10 minutes
Module 3 Quiz15 minutes

3 discussion promptsTotal 25 minutes

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

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

5 videos4 readings1 discussion prompt

5 videosTotal 19 minutes

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

4 readingsTotal 35 minutes

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