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Parth Dhameliya
In this 2-hour long guided-project course, you will load a pretrained state of the art model CNN and you will train in PyTorch to classify facial expressions. The data that you will use, consists of 48 x 48 pixel grayscale images of faces and there are seven targets (angry, disgust, fear, happy, sad, surprise, neutral). Furthermore, you will apply augmentation for classification task to augment images. Moreover, you are going to create train and evaluator function which will be helpful to write training loop. Lastly, you will use best trained model to classify expression given any input image.
Register for this course and see more details by visiting:
OpenCourser.com/course/aip4no/facial
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What's inside
Syllabus
Project Overview
In this 2-hour long guided-project course, you will load a pretrained state of the art model CNN and you will train in PyTorch to classify facial expressions. The data that you will use, consists of 48 x 48 pixel grayscale images of faces and there are seven targets (angry, disgust, fear, happy, sad, surprise, neutral). Furthermore, you will apply augmentation for classification task to augment images. Moreover, you are going to create train and evaluator function which will be helpful to write training loop. Lastly, you will use best trained model to classify expression given any input image.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces how to apply augmentation for classification of images, which is highly relevant to the task of facial expression recognition
Develops and evaluates models for facial expression recognition, a topic with applications in human-computer interaction and affective computing
Utilizes the PyTorch framework and a pretrained Convolutional Neural Network (CNN) model, aligning with industry-standard tools
Provides hands-on experience with loading, training, and evaluating models, building practical skills for machine learning and deep learning
Taught by Parth Dhameliya, who has expertise in deep learning and facial expression recognition research
Prerequisites are not explicitly stated, but some familiarity with Python, PyTorch, and deep learning concepts would be beneficial
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Save Facial Expression Recognition with PyTorch to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Facial Expression Recognition with PyTorch with these
activities:
Practice classifying emotions using the trained model
Show steps
Practicing emotion classification using the trained model will solidify your understanding of the model's capabilities and limitations.
Show steps
-
Use the trained model to classify emotions from images or videos.
-
Analyze the model's predictions and identify any potential biases or errors.
Load pretrained model and apply augmentation
Show steps
The course involves working with a pretrained model for emotion recognition. This drill will familiarize you with loading pretrained models and applying augmentation, which are key techniques used in deep learning for emotion recognition.
Show steps
-
Load the pretrained PyTorch model for image classification.
-
Apply image augmentation techniques to increase the dataset size and improve model performance.
Build and train CNN model
Show steps
The course covers building and training a Convolutional Neural Network (CNN) for emotion recognition. This drill will give you hands-on experience in implementing a CNN architecture and training it on the provided dataset.
Show steps
-
Build a CNN architecture using PyTorch.
-
Define the training loop and train the CNN model.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Evaluate the trained model
Show steps
Evaluating the trained model's performance is crucial for assessing its effectiveness. This drill will guide you through calculating accuracy and other metrics to evaluate your model's performance.
Show steps
-
Use a validation set to evaluate the model's performance.
-
Calculate and analyze metrics such as accuracy, precision, and recall.
Explore different CNN architectures for emotion recognition
Show steps
Experimenting with different CNN architectures will provide you with a deeper understanding of their impact on model performance for emotion recognition.
Show steps
-
Research and select different CNN architectures for emotion recognition.
-
Implement and compare the performance of these architectures on the provided dataset.
Create a poster presentation of your findings
Show steps
Creating a poster presentation will allow you to synthesize your learning, present your results, and engage in scientific communication.
Show steps
-
Summarize the key findings of your project.
-
Design a visually appealing poster that effectively communicates your results.
Write a report summarizing your project findings
Show steps
Writing a report will provide you with an opportunity to document your learning, reflect on your progress, and share your findings with others.
Show steps
-
Summarize the key steps involved in your project.
-
Present your results and discuss their implications.
Contribute to an open-source emotion recognition project
Show steps
Contributing to an open-source project allows you to apply your skills practically, collaborate with others, and make a meaningful contribution to the field of emotion recognition.
Show steps
-
Identify an open-source project related to emotion recognition on GitHub.
-
Review the project's documentation and identify areas where you can contribute.
Practice classifying emotions using the trained model
Show steps
Practicing emotion classification using the trained model will solidify your understanding of the model's capabilities and limitations.
Show steps
Show steps
Show steps
- Use the trained model to classify emotions from images or videos.
- Analyze the model's predictions and identify any potential biases or errors.
Load pretrained model and apply augmentation
Show steps
The course involves working with a pretrained model for emotion recognition. This drill will familiarize you with loading pretrained models and applying augmentation, which are key techniques used in deep learning for emotion recognition.
Show steps
Show steps
Show steps
- Load the pretrained PyTorch model for image classification.
- Apply image augmentation techniques to increase the dataset size and improve model performance.
Build and train CNN model
Show steps
The course covers building and training a Convolutional Neural Network (CNN) for emotion recognition. This drill will give you hands-on experience in implementing a CNN architecture and training it on the provided dataset.
Show steps
Show steps
Show steps
- Build a CNN architecture using PyTorch.
- Define the training loop and train the CNN model.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Evaluate the trained model
Show steps
Evaluating the trained model's performance is crucial for assessing its effectiveness. This drill will guide you through calculating accuracy and other metrics to evaluate your model's performance.
Show steps
Show steps
Show steps
- Use a validation set to evaluate the model's performance.
- Calculate and analyze metrics such as accuracy, precision, and recall.
Explore different CNN architectures for emotion recognition
Show steps
Experimenting with different CNN architectures will provide you with a deeper understanding of their impact on model performance for emotion recognition.
Show steps
Show steps
Show steps
- Research and select different CNN architectures for emotion recognition.
- Implement and compare the performance of these architectures on the provided dataset.
Create a poster presentation of your findings
Show steps
Creating a poster presentation will allow you to synthesize your learning, present your results, and engage in scientific communication.
Show steps
Show steps
Show steps
- Summarize the key findings of your project.
- Design a visually appealing poster that effectively communicates your results.
Write a report summarizing your project findings
Show steps
Writing a report will provide you with an opportunity to document your learning, reflect on your progress, and share your findings with others.
Show steps
Show steps
Show steps
- Summarize the key steps involved in your project.
- Present your results and discuss their implications.
Contribute to an open-source emotion recognition project
Show steps
Contributing to an open-source project allows you to apply your skills practically, collaborate with others, and make a meaningful contribution to the field of emotion recognition.
Show steps
Show steps
Show steps
- Identify an open-source project related to emotion recognition on GitHub.
- Review the project's documentation and identify areas where you can contribute.
Career center
Learners who complete Facial Expression Recognition with PyTorch will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
As a Computer Vision Engineer, you will design and develop cutting-edge vision systems for various applications. This course can help you build a foundation in facial expression recognition, which is an important area of computer vision. By learning how to classify facial expressions using deep learning techniques, you will gain valuable skills that can be applied to a wide range of computer vision tasks.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. This course will help you build a foundation in deep learning, which is a powerful type of machine learning. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of machine learning tasks.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course will help you build a foundation in deep learning, which is a powerful tool for extracting insights from data. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of data science tasks.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course will help you build a foundation in deep learning, which is a powerful tool for solving complex problems. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of software engineering tasks.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop AI systems. This course will help you build a foundation in deep learning, which is a powerful type of AI. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of AI tasks.
Research Scientist
Research Scientist
Research Scientists conduct research to advance scientific knowledge. This course will help you build a foundation in deep learning, which is a powerful tool for solving complex problems. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of research projects.
Customer Success Manager
Customer Success Manager
Customer Success Managers ensure that customers are satisfied with their products or services. This course will help you build a foundation in deep learning, which is a powerful tool for understanding customer needs. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of customer success tasks.
User Experience Designer
User Experience Designer
User Experience Designers design and develop user interfaces. This course will help you build a foundation in deep learning, which is a powerful tool for understanding user needs. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of UX design tasks.
Product Manager
Product Manager
Product Managers manage the development and launch of new products. This course will help you build a foundation in deep learning, which is a powerful tool for understanding customer needs. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of product management tasks.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns. This course will help you build a foundation in deep learning, which is a powerful tool for understanding customer behavior. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of marketing tasks.
Sales Manager
Sales Manager
Sales Managers manage sales teams and develop sales strategies. This course will help you build a foundation in deep learning, which is a powerful tool for understanding customer needs. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of sales tasks.
Business Analyst
Business Analyst
Business Analysts analyze business needs and develop solutions. This course will help you build a foundation in deep learning, which is a powerful tool for extracting insights from data. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of business analysis tasks.
Instructional Designer
Instructional Designer
Instructional Designers design and develop learning materials. This course will help you build a foundation in deep learning, which is a powerful tool for understanding how people learn. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of instructional design tasks.
Technical Writer
Technical Writer
Technical Writers create documentation for technical products and services. This course will help you build a foundation in deep learning, which is a powerful tool for understanding complex technologies. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of technical writing tasks.
Human Resources Manager
Human Resources Manager
Human Resources Managers manage the human resources of an organization. This course will help you build a foundation in deep learning, which is a powerful tool for understanding employee needs. By learning how to apply deep learning to facial expression recognition, you will gain valuable skills that can be applied to a wide range of human resources tasks.
For more career information including salaries, visit:
OpenCourser.com/course/aip4no/facial
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Facial Expression Recognition with PyTorch .
Provides a comprehensive overview of facial expression recognition techniques. It covers a wide range of topics, including feature extraction, classification, and evaluation.
Save
Provides a comprehensive overview of the psychology of facial expression. It covers a wide range of topics, including the different facial expressions that are associated with different emotions and how they are used in communication.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers a wide range of topics, including image processing, object detection, and image classification.
Save
Provides a comprehensive overview of affective computing, which is the study of how computers can recognize, interpret, and simulate human emotions. It covers a wide range of topics, including facial expression recognition, speech recognition, and body language recognition.
Covers the fundamentals of deep learning and introduces PyTorch. It provides hands-on examples and exercises that are helpful for understanding the concepts and implementing them in PyTorch.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised and unsupervised learning, and good resource for understanding the foundations of machine learning.
Classic work on the study of human emotions. It provides a detailed account of the different facial expressions that are associated with different emotions and good resource for understanding the foundations of facial expression recognition.
Provides a comprehensive overview of deep learning and covers a wide range of topics. It good resource for understanding the foundations of deep learning and how to apply it to different problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/aip4no/facial
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Language
English
Good to know
Introduces how to apply augmentation for classification of images, which is highly relevant to the task of facial expression recognition
Develops and evaluates models for facial expression recognition, a topic with applications in human-computer interaction and affective computing
Utilizes the PyTorch framework and a pretrained Convolutional Neural Network (CNN) model, aligning with industry-standard tools
Provides hands-on experience with loading, training, and evaluating models, building practical skills for machine learning and deep learning
Taught by Parth Dhameliya, who has expertise in deep learning and facial expression recognition research
Prerequisites are not explicitly stated, but some familiarity with Python, PyTorch, and deep learning concepts would be beneficial
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