In this 2-hour long guided project, we will use a ResNet-18 model and train it on a COVID-19 Radiography dataset. This dataset has nearly 3000 Chest X-Ray scans which are categorized in three classes - Normal, Viral Pneumonia and COVID-19. Our objective in this project is to create an image classification model that can predict Chest X-Ray scans that belong to one of the three classes with a reasonably high accuracy. Please note that this dataset, and the model that we train in the project, can not be used to diagnose COVID-19 or Viral Pneumonia. We are only using this data for educational purpose.
Detecting COVID-19 with Chest X-Ray using PyTorch
Instructor: Amit Yadav
Sponsored by Coursera Learning Team
12,657 already enrolled
(334 reviews)
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What you'll learn
Create custom Dataset and DataLoader in PyTorch
Train a ResNet-18 model in PyTorch to perform Image Classification
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction
Importing Libraries
Creating Custom Dataset
Image Transformations
Prepare DataLoader
Data Visualization
Creating the Model
Training the Model
Final Results
Recommended experience
Prior programming experience in Python. Theoretical knowledge of Convolutional Neural Networks, and gradient descent.
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How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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Reviewed on Aug 22, 2020
Lecturer needs to let students know how to access dataset and code from in the beginning of the video lecture. It was hard to find code/ data download website
Reviewed on Oct 5, 2020
Excellent course.
Reviewed on Aug 27, 2020
It's a nice project, but I think more explanation about the concepts (ex- imagenet dataset, restnet18 model, etc.) must be provided to make the understanding more clearer.
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