3D on Adience Age

Metric: Accuracy (5-fold) (higher is better)

LeaderboardDataset

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#	Model↕	Accuracy (5-fold)▼	Augmentations	Paper	Date↕	Code
1	ViT-hSeq	84.91	No	A Hybrid Transformer-Sequencer approach for Age ...	2024-03-19	-
2	MiVOLO-V2	69.43	Yes	Beyond Specialization: Assessing the Capabilitie...	2024-03-04	Code
3	MiVOLO-D1	68.69	Yes	MiVOLO: Multi-input Transformer for Age and Gend...	2023-07-10	Code
4	AL-ResNets-34 + IMDB-WIKI	67.47	Yes	Fine-Grained Age Estimation in the wild with Att...	2018-05-26	-
5	R-SAAFc2 +IMDB-WIKI	67.3	Yes	-	-	-
6	RoR-34 + IMDB-WIKI	66.74	Yes	Age Group and Gender Estimation in the Wild with...	2017-10-09	-
7	MWR	62.6	No	Moving Window Regression: A Novel Approach to Or...	2022-03-24	Code
8	UNIORD-ResNet-101 (single crop, pytorch)	61	No	Deep Ordinal Regression using Optimal Transport ...	2020-11-15	-
9	RetinaFace + ArcFace + MLP + IC + Skip connections	60.86	Yes	Generalizing MLPs With Dropouts, Batch Normaliza...	2021-08-18	Code
10	CPG (single crop, pytorch)	57.66	Yes	Compacting, Picking and Growing for Unforgetting...	2019-10-15	Code
11	PAENet (single crop, tensorflow)	57.3	Yes	-	-	Code
12	MegaAge	56.01	Yes	Quantifying Facial Age by Posterior of Age Compa...	2017-08-31	Code
13	Levi_Hassner CNN (over-sample, caffe)	50.7	No	-	-	Code
14	Levi_Hassner CNN (single crop, caffe)	49.5	No	-	-	Code
15	LMTCNN-2-1 (single crop, tensorflow)	44.26	No	Joint Estimation of Age and Gender from Unconstr...	2018-06-06	Code
16	Levi_Hassner CNN (single crop, tensorflow)	44.14	No	-	-	Code

#1ViT-hSeqSOTA
84.91
Accuracy (5-fold)· 2024-03-19
A Hybrid Transformer-Sequencer approach for Age and Gender classification from in-wild facial images
#2MiVOLO-V2SOTA
69.43
Accuracy (5-fold)· Augmentations· 2024-03-04
Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation Code
#3MiVOLO-D1SOTA
68.69
Accuracy (5-fold)· Augmentations· 2023-07-10
MiVOLO: Multi-input Transformer for Age and Gender Estimation Code
#4AL-ResNets-34 + IMDB-WIKISOTA
67.47
Accuracy (5-fold)· Augmentations· 2018-05-26
Fine-Grained Age Estimation in the wild with Attention LSTM Networks
#5R-SAAFc2 +IMDB-WIKI
67.3
Accuracy (5-fold)· Augmentations
No paper
#6RoR-34 + IMDB-WIKISOTA
66.74
Accuracy (5-fold)· Augmentations· 2017-10-09
Age Group and Gender Estimation in the Wild with Deep RoR Architecture
#7MWR
62.6
Accuracy (5-fold)· 2022-03-24
Moving Window Regression: A Novel Approach to Ordinal Regression Code
#8UNIORD-ResNet-101 (single crop, pytorch)
61
Accuracy (5-fold)· 2020-11-15
Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities
#9RetinaFace + ArcFace + MLP + IC + Skip connections
60.86
Accuracy (5-fold)· Augmentations· 2021-08-18
Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections Code
#10CPG (single crop, pytorch)
57.66
Accuracy (5-fold)· Augmentations· 2019-10-15
Compacting, Picking and Growing for Unforgetting Continual Learning Code
#11PAENet (single crop, tensorflow)
57.3
Accuracy (5-fold)· Augmentations
No paperCode
#12MegaAgeSOTA
56.01
Accuracy (5-fold)· Augmentations· 2017-08-31
Quantifying Facial Age by Posterior of Age Comparisons Code
#13Levi_Hassner CNN (over-sample, caffe)
50.7
Accuracy (5-fold)
No paperCode
#14Levi_Hassner CNN (single crop, caffe)
49.5
Accuracy (5-fold)
No paperCode
#15LMTCNN-2-1 (single crop, tensorflow)
44.26
Accuracy (5-fold)· 2018-06-06
Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications Code
#16Levi_Hassner CNN (single crop, tensorflow)
44.14
Accuracy (5-fold)
No paperCode