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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 TitleRepository
Levi_Hassner CNN (single crop, caffe)85.9Age and Gender Classification using Convolutional Neural Networks
ViT-hSeq96.56A Hybrid Transformer-Sequencer approach for Age and Gender classification from in-wild facial images-
MiVOLO-D196.51MiVOLO: Multi-input Transformer for Age and Gender Estimation-
Levi_Hassner CNN (single crop, tensorflow)82.52Age and Gender Classification using Convolutional Neural Networks
Levi_Hassner CNN ( over-sample, caffe)86.8Age and Gender Classification using Convolutional Neural Networks
RetinaFace + ArcFace + MLP + Skip connections90.66Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections-
PAENet (single crop, tensorflow)89.08Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning
MiVOLO-V297.39Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation-
CPG (single crop, pytorch)89.66Compacting, Picking and Growing for Unforgetting Continual Learning-
LMTCNN-2-1 (single crop, tensorflow)85.16Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications-
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Age And Gender Classification On Adience | SOTA | HyperAI