Technology
FER2013
FER2013 is a benchmark dataset of 35,887 grayscale facial images used to train and evaluate emotion recognition models across seven distinct categories.
Pierre-Luc Carrier and Aaron Courville designed this dataset for the ICML 2013 Challenges in Representation Learning. It contains 48x48 pixel images labeled with seven emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral. While the dataset is known for its high noise levels and label ambiguity, it remains a critical baseline for testing convolutional neural networks (CNNs) in real-time facial expression analysis.
What builders pair with FER2013
Projects using both technologies. Select a pairing to see a project.
Pairing: Dropout
Modèle CNN comme Deep Learning pour la reconnaissance des expressions faciales
Pairing: Mixup
Modèle CNN comme Deep Learning pour la reconnaissance des expressions faciales
Pairing: Python
Modèle CNN comme Deep Learning pour la reconnaissance des expressions faciales
Pairing: ResNet-18
Modèle CNN comme Deep Learning pour la reconnaissance des expressions faciales
Pairing: ResNet-50
Modèle CNN comme Deep Learning pour la reconnaissance des expressions faciales
Pairing: Transfer learning
Modèle CNN comme Deep Learning pour la reconnaissance des expressions faciales
Recent Talks & Demos
Showing 1-1 of 1
