Facial Expression Recognition with PyTorch
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.
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- Level Foundation
- المدة 2 ساعات hours
- الطبع بواسطة Coursera Project Network
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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.الوحدات
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Learn to classify facial expressions using a pretrained CNN model in PyTorch with this 2-hour guided project. Perfect for beginners in IT & Computer Science, you’ll handle grayscale images, apply augmentation techniques, and create training loops. Offered by Coursera, this free course is an excellent introduction to facial expression recognition.
Parth Dhameliya