Age And Gender Classification On Adience

Métriques

Accuracy (5-fold)

Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title
MiVOLO-V2	97.39	Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation
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
RetinaFace + ArcFace + MLP + Skip connections	90.66	Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections
CPG (single crop, pytorch)	89.66	Compacting, Picking and Growing for Unforgetting Continual Learning
PAENet (single crop, tensorflow)	89.08	Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning
Levi_Hassner CNN ( over-sample, caffe)	86.8	Age and Gender Classification using Convolutional Neural Networks
Levi_Hassner CNN (single crop, caffe)	85.9	Age and Gender Classification using Convolutional Neural Networks
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
Levi_Hassner CNN (single crop, tensorflow)	82.52	Age and Gender Classification using Convolutional Neural Networks

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