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InsightFace Model Zoo

🔔 ALL models are available for non-commercial research purposes only.

0. Python Package models

To check the detail of insightface python package, please see here.

To install: pip install -U insightface

To use the specific model pack:

model_pack_name = 'buffalo_l'
app = FaceAnalysis(name=model_pack_name)

Name in bold is the default model pack in latest version.

Name Detection Model Recognition Model Alignment Attributes Model-Size
antelopev2 RetinaFace-10GF ResNet100@Glint360K 2d106 & 3d68 Gender&Age 407MB
buffalo_l RetinaFace-10GF ResNet50@WebFace600K 2d106 & 3d68 Gender&Age 326MB
buffalo_m RetinaFace-2.5GF ResNet50@WebFace600K 2d106 & 3d68 Gender&Age 313MB
buffalo_s RetinaFace-500MF MBF@WebFace600K 2d106 & 3d68 Gender&Age 159MB
buffalo_sc RetinaFace-500MF MBF@WebFace600K - - 16MB

Recognition accuracy of python library model packs:

Name MR-ALL African Caucasian South Asian East Asian LFW CFP-FP AgeDB-30 IJB-C(E4)
buffalo_l 91.25 90.29 94.70 93.16 74.96 99.83 99.33 98.23 97.25
buffalo_s 71.87 69.45 80.45 73.39 51.03 99.70 98.00 96.58 95.02

buffalo_m has the same accuracy with buffalo_l.

buffalo_sc has the same accuracy with buffalo_s.

(Note that almost all ONNX models in our model_zoo can be called by python library.)

1. Face Recognition models.

Definition:

The default training loss is margin based softmax if not specified.

MFN: MobileFaceNet

MS1MV2: MS1M-ArcFace

MS1MV3: MS1M-RetinaFace

MS1M_MegaFace: MS1MV2+MegaFace_train

_pfc: using Partial FC, with sample-ratio=0.1

MegaFace: MegaFace identification test, with gallery=1e6.

IJBC: IJBC 1:1 test, under FAR<=1e-4.

BDrive: BaiduDrive

GDrive: GoogleDrive

List of models by MXNet and PaddlePaddle:

Backbone Dataset Method LFW CFP-FP AgeDB-30 MegaFace Link.
R100 (mxnet) MS1MV2 ArcFace 99.77 98.27 98.28 98.47 BDrive, GDrive
MFN (mxnet) MS1MV1 ArcFace 99.50 88.94 95.91 - BDrive, GDrive
MFN (paddle) MS1MV2 ArcFace 99.45 93.43 96.13 - pretrained model, inference model
iResNet50 (paddle) MS1MV2 ArcFace 99.73 97.43 97.88 - pretrained model, inference model

List of models by various depth IResNet and training datasets:

Backbone Dataset MR-ALL African Caucasian South Asian East Asian Link(onnx)
R100 Casia 42.735 39.666 53.933 47.807 21.572 GDrive
R100 MS1MV2 80.725 79.117 87.176 85.501 55.807 GDrive
R18 MS1MV3 68.326 62.613 75.125 70.213 43.859 GDrive
R34 MS1MV3 77.365 71.644 83.291 80.084 53.712 GDrive
R50 MS1MV3 80.533 75.488 86.115 84.305 57.352 GDrive
R100 MS1MV3 84.312 81.083 89.040 88.082 62.193 GDrive
R18 Glint360K 72.074 68.230 80.575 75.852 47.831 GDrive
R34 Glint360K 83.015 79.907 88.620 86.815 60.604 GDrive
R50 Glint360K 87.077 85.272 91.617 90.541 66.813 GDrive
R100 Glint360K 90.659 89.488 94.285 93.434 72.528 GDrive

List of models by IResNet-50 and different training datasets:

Dataset MR-ALL African Caucasian South Asian East Asian LFW CFP-FP AgeDB-30 IJB-C(E4) Link(onnx)
CISIA 36.794 42.550 55.825 49.618 19.611 99.450 95.214 94.900 87.220 GDrive
CISIA_pfc 37.107 38.934 53.823 48.674 19.927 99.367 95.429 94.600 84.970 GDrive
VGG2 38.578 35.259 54.304 44.081 24.095 99.550 97.410 95.080 91.220 GDrive
VGG2_pfc 40.673 36.767 60.180 49.039 24.255 99.683 98.529 95.400 92.490 GDrive
GlintAsia 62.663 49.531 64.829 57.984 61.743 99.583 93.186 95.400 91.500 GDrive
GlintAsia_pfc 63.149 50.366 65.227 57.936 61.820 99.650 93.029 95.233 91.140 GDrive
MS1MV2 77.696 74.596 84.126 82.041 51.105 99.833 98.083 98.083 96.140 GDrive
MS1MV2_pfc 77.738 74.728 84.883 82.798 52.507 99.783 98.071 98.017 96.080 GDrive
MS1M_MegaFace 78.372 74.138 82.251 77.223 60.203 99.750 97.557 97.400 95.350 GDrive
MS1M_MegaFace_pfc 78.773 73.690 82.947 78.793 57.566 99.800 97.870 97.733 95.400 GDrive
MS1MV3 82.522 77.172 87.028 86.006 60.625 99.800 98.529 98.267 96.580 GDrive
MS1MV3_pfc 81.683 78.126 87.286 85.542 58.925 99.800 98.443 98.167 96.430 GDrive
Glint360k 86.789 84.749 91.414 90.088 66.168 99.817 99.143 98.450 97.130 GDrive
Glint360k_pfc 87.077 85.272 91.616 90.541 66.813 99.817 99.143 98.450 97.020 GDrive
WebFace600K 90.566 89.355 94.177 92.358 73.852 99.800 99.200 98.100 97.120 GDrive
WebFace600K_pfc 89.951 89.301 94.016 92.381 73.007 99.817 99.143 98.117 97.010 GDrive
Average 69.247 65.908 77.121 72.819 52.014 99.706 97.374 96.962 93.925
Average_pfc 69.519 65.898 77.497 73.213 51.853 99.715 97.457 96.965 93.818

List of models by MobileFaceNet and different training datasets:

FLOPS: 450M FLOPs

Model-Size: 13MB

Dataset MR-ALL African Caucasian South Asian East Asian LFW CFP-FP AgeDB-30 IJB-C(E4) Link(onnx)
WebFace600K 71.865 69.449 80.454 73.394 51.026 99.70 98.00 96.58 95.02 -

2. Face Detection models.

2.1 RetinaFace

In RetinaFace, mAP was evaluated with multi-scale testing.

m025: means MobileNet-0.25

Impelmentation Easy-Set Medium-Set Hard-Set Link
RetinaFace-R50 96.5 95.6 90.4 BDrive, GDrive
RetinaFace-m025(yangfly) - - 82.5 BDrive(nzof), GDrive
BlazeFace-FPN-SSH (paddle) 91.9 89.8 81.7% pretrained model, inference model

2.2 SCRFD

In SCRFD, mAP was evaluated with single scale testing, VGA resolution.

2.5G: means the model cost 2.5G FLOPs while the input image is in VGA(640x480) resolution.

_KPS: means this model can detect five facial keypoints.

Name Easy Medium Hard FLOPs Params(M) Infer(ms) Link(pth)
SCRFD_500M 90.57 88.12 68.51 500M 0.57 3.6 GDrive
SCRFD_1G 92.38 90.57 74.80 1G 0.64 4.1 GDrive
SCRFD_2.5G 93.78 92.16 77.87 2.5G 0.67 4.2 GDrive
SCRFD_10G 95.16 93.87 83.05 10G 3.86 4.9 GDrive
SCRFD_34G 96.06 94.92 85.29 34G 9.80 11.7 GDrive
SCRFD_500M_KPS 90.97 88.44 69.49 500M 0.57 3.6 GDrive
SCRFD_2.5G_KPS 93.80 92.02 77.13 2.5G 0.82 4.3 GDrive
SCRFD_10G_KPS 95.40 94.01 82.80 10G 4.23 5.0 GDrive

3. Face Alignment models.

2.1 2D Face Alignment

Impelmentation Points Backbone Params(M) Link(onnx)
Coordinate-regression 106 MobileNet-0.5 1.2 GDrive

2.2 3D Face Alignment

Impelmentation Points Backbone Params(M) Link(onnx)
- 68 ResNet-50 34.2 GDrive

2.3 Dense Face Alignment

4. Face Attribute models.

4.1 Gender&Age

Training-Set Backbone Params(M) Link(onnx)
CelebA MobileNet-0.25 0.3 GDrive

4.2 Expression