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.
Facial Expression Recognition with PyTorch
Facial Expression Recognition with PyTorch
Instructor: Parth Dhameliya
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Beginner level
Recommended experience
2 hours
Learn at your own pace
Hands-on learning
What you'll learn
Load pretrained state of the art model
Create train and eval function to write the training loop
Skills you'll practice
Tools you'll use
Details to know
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Taught in English
No downloads or installation required
Only available on desktop
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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:
Set up colab runtime
Configurations
Load Dataset
Load dataset into batches
Create Model
Create Train and Eval Function
Training Loop
Recommended experience
Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)
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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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