A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset
Sorry, I'm new to Deep Learning.
May I know what are the hyperparameters you used to get VGG19 of Test_acc: 94.646% and Resnet18 of Test_acc: 94.040%
Mine seem to always be getting an average accuracy of 88% after 10 folds for both.
I used lr 0.01, bs 128, epoch 40, learning_rate_decay_start = 20, learning_rate_decay_every = 1 and learning_rate_decay_rate = 0.8
Sorry, I'm new to Deep Learning. May I know what are the hyperparameters you used to get VGG19 of Test_acc: 94.646% and Resnet18 of Test_acc: 94.040% Mine seem to always be getting an average accuracy of 88% after 10 folds for both. I used lr 0.01, bs 128, epoch 40, learning_rate_decay_start = 20, learning_rate_decay_every = 1 and learning_rate_decay_rate = 0.8