Pretrained models

To load pretrained model you need to define model_name (size), dataset it was trained on, train_type whether it is pretrain-only (ptn), large-margin finetuning (ft_lm), finetuning with cutmix+mixup augmentations (ft_mix).

import torch

model_name='M' # ~b3-b4 size
train_type='ft_mix'
dataset='vb2+vox2+cnc'

model = torch.hub.load('IDRnD/ReDimNet', 'ReDimNet', 
                       model_name=model_name, 
                       train_type=train_type, 
                       dataset=dataset)

All models configurations with corresponding metrics can be found in following table:

Model name (size)	Train dataset	Train type	Vox1-O EER(%)	Vox1-E EER(%)	Vox1-H EER(%)	SITW EER(%)	VOICES EER(%)	CN-Celeb EER(%)
M	vb2+vox2+cnc	ft_mix	0.835	0.745	1.284	1.203	2.703	7.474
M	vb2	ptn	1.319	1.128	2.000	1.482	4.116	9.012
S	vb2+vox2+cnc	ft_mix	0.936	0.874	1.510	1.310	2.774	8.043
S	vb2	ptn	1.542	1.408	2.505	1.781	3.987	9.592
b0	vox2	ft_lm	1.16	1.25	2.20	-	-	-
b0	vox2	ptn	-	-	-	-	-	-
b1	vox2	ft_lm	0.85	0.97	1.73	-	-	-
b1	vox2	ptn	-	-	-	-	-	-
b2	vox2	ft_lm	0.57	0.76	1.32	-	-	-
b2	vox2	ptn	-	-	-	-	-	-
b3	vox2	ft_lm	0.50	0.73	1.33	-	-	-
b3	vox2	ptn	-	-	-	-	-	-
b4	vox2	ft_lm	0.51	0.68	1.26	-	-	-
b4	vox2	ptn	-	-	-	-	-	-
b5	vox2	ft_lm	0.43	0.61	1.08	-	-	-
b5	vox2	ptn	-	-	-	-	-	-
b6	vox2	ft_lm	0.40	0.55	1.05	-	-	-
b6	vox2	ptn	-	-	-	-	-	-

Paper metrics

Model	Params	GMACs	LM	AS-Norm	Vox1-O EER(%)	Vox1-E EER(%)	Vox1-H EER(%)
⬦ ReDimNet-B0	1.0M	0.43	✓	✗	1.16	1.25	2.20
⬥ ReDimNet-B0			✓	✓	1.07	1.18	2.01
NeXt-TDNN-l (C=128,B=3)	1.6M	0.29*	✗	✓	1.10	1.24	2.12
NeXt-TDNN (C=128,B=3)	1.9M	0.35*	✗	✓	1.03	1.17	1.98
⬦ ReDimNet-B1	2.2M	0.54	✓	✗	0.85	0.97	1.73
⬥ ReDimNet-B1			✓	✓	0.73	0.89	1.57
ECAPA (C=512)	6.4M	1.05	✗	✓	0.94	1.21	2.20
NeXt-TDNN-l (C=256,B=3)	6.0M	1.13*	✗	✓	0.81	1.04	1.86
CAM++	7.2M	1.15	✓	✗	0.71	0.85	1.66
NeXt-TDNN (C=256,B=3)	7.1M	1.35*	✗	✓	0.79	1.04	1.82
⬦ ReDimNet-B2	4.7M	0.90	✓	✗	0.57	0.76	1.32
⬥ ReDimNet-B2			✓	✓	0.52	0.74	1.27
ECAPA (C=1024)	14.9M	2.67	✓	✗	0.98	1.13	2.09
DF-ResNet56	4.5M	2.66	✗	✓	0.96	1.09	1.99
Gemini DF-ResNet60	4.1M	2.50*	✗	✓	0.94	1.05	1.80
⬦ ReDimNet-B3	3.0M	3.00	✓	✗	0.50	0.73	1.33
⬥ ReDimNet-B3			✓	✓	0.47	0.69	1.23
ResNet34	6.6M	4.55	✓	✗	0.82	0.93	1.68
Gemini DF-ResNet114	6.5M	5.00	✗	✓	0.69	0.86	1.49
⬦ ReDimNet-B4	6.3M	4.80	✓	✗	0.51	0.68	1.26
⬥ ReDimNet-B4			✓	✓	0.44	0.64	1.17
Gemini DF-ResNet183	9.2M	8.25	✗	✓	0.60	0.81	1.44
DF-ResNet233	12.3M	11.17	✗	✓	0.58	0.76	1.44
⬦ ReDimNet-B5	9.2M	9.87	✓	✗	0.43	0.61	1.08
⬥ ReDimNet-B5			✓	✓	0.39	0.59	1.05
ResNet293	23.8M	28.10	✓	✗	0.53	0.71	1.30
ECAPA2	27.1M	187.00*	✓	✗	0.44	0.62	1.15
⬦ ReDimNet-B6	15.0M	20.27	✓	✗	0.40	0.55	1.05
⬥ ReDimNet-B6			✓	✓	0.37	0.53	1.00

* - means values have been estimated.