Facial Expression Recognition with PyTorch
14 ratings

Load pretrained state of the art model
Create train and eval function to write the training loop
Showcase this hands-on experience in an interview
14 ratings
Load pretrained state of the art model
Create train and eval function to write the training loop
Showcase this hands-on experience in an interview
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.
Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)
Deep Learning
Convolutional Neural Network
pytorch
classification
Computer Vision
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
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
by AW
Sep 4, 2022This course is good for practing python scripts by creating a facial recognition AI. The course offers an exercise in python, nothing more.
by DD
Aug 13, 2022It is a good approach to create a facial expression regonition and code explanation is very well, i am happy to learn
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Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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