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The labels mentioned in the models readme is this - emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, 'anger':4, 'disgust':5, 'fear':6, 'contempt':7}
It does not match with the labels mentioned in the dataset source (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral)
@ebarsoum So I would be right in taking these as actual labels - {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, 'anger':4, 'disgust':5, 'fear':6, 'contempt':7}
And taking the output as activations which need to pass through a softmax function to get the probability distribution of the different emotions?
@anirudhacharya Yes, you will need to route the output of the model through a softmax function to get the probabilities across the 8 classes (this ferplus model uses the above 8 classes and not the original 7)
The labels mentioned in the models readme is this -
emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, 'anger':4, 'disgust':5, 'fear':6, 'contempt':7}
It does not match with the labels mentioned in the dataset source
(0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral)
Can this be clarified @ebarsoum
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