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Age And Gender Classification On Adience

Métriques

Accuracy (5-fold)

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy (5-fold)	Paper Title	Repository
Levi_Hassner CNN (single crop, caffe)	85.9	Age and Gender Classification using Convolutional Neural Networks
ViT-hSeq	96.56	A Hybrid Transformer-Sequencer approach for Age and Gender classification from in-wild facial images	-
MiVOLO-D1	96.51	MiVOLO: Multi-input Transformer for Age and Gender Estimation
Levi_Hassner CNN (single crop, tensorflow)	82.52	Age and Gender Classification using Convolutional Neural Networks
Levi_Hassner CNN ( over-sample, caffe)	86.8	Age and Gender Classification using Convolutional Neural Networks
RetinaFace + ArcFace + MLP + Skip connections	90.66	Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections
PAENet (single crop, tensorflow)	89.08	Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning
MiVOLO-V2	97.39	Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation
CPG (single crop, pytorch)	89.66	Compacting, Picking and Growing for Unforgetting Continual Learning
LMTCNN-2-1 (single crop, tensorflow)	85.16	Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications

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