Overview of OpenVINO™ Toolkit Public Pre-Trained Models

OpenVINO toolkit provides a set of public pre-trained models that you can use for learning and demo purposes or for developing deep learning software. Most recent version is available in the repo on Github. The table Public Pre-Trained Models Device Support summarizes devices supported by each model.

You can download models and convert them into Inference Engine format (*.xml + *.bin) using the OpenVINO™ Model Downloader and other automation tools.

Classification

Model Name Model Version

Implementation

Top 1 Accuracy

Top 5 Accuracy

GFlops

mParams

AlexNet alexnet

Caffe

56.60%

79.81%

1.5

60.965

AntiSpoofNet anti-spoof-mn3

PyTorch

3.81%

0.15

3.02

CaffeNet caffenet

Caffe

56.71%

79.91%

1.5

60.965

DenseNet 121 densenet-121

Caffe

74.42%

92.14%

5.724

7.971

DenseNet 121 densenet-121-tf

TensorFlow

74.46%

92.13%

5.7287

7.9714

DenseNet 121 densenet-121-caffe2

Caffe2

74.90%

92.19%

5.723

7.971

DenseNet 161 densenet-161

Caffe

77.55%

93.92%

15.561

28.666

DenseNet 161 densenet-161-tf

TensorFlow

76.45%

93.23%

14.128

28.666

DenseNet 169 densenet-169

Caffe

76.11%

93.11%

6.788

14.139

DenseNet 169 densenet-169-tf

TensorFlow

76.14%

93.12%

6.7932

14.1389

DenseNet 201 densenet-201

Caffe

76.89%

93.56%

8.673

20.001

DenseNet 201 densenet-201-tf

TensorFlow

76.93%

93.56%

8.6786

20.0013

DLA 34 dla-34

PyTorch

74.64%

92.06%

6.1368

15.7344

EfficientNet B0 efficientnet-b0

TensorFlow

75.70%

92.76%

0.819

5.268

EfficientNet B0 efficientnet-b0-pytorch

PyTorch

76.91%

93.21%

0.819

5.268

EfficientNet B0 AutoAugment efficientnet-b0_auto_aug

TensorFlow

76.43%

93.04%

0.819

5.268

EfficientNet B5 efficientnet-b5

TensorFlow

83.33%

96.67%

21.252

30.303

EfficientNet B5 efficientnet-b5-pytorch

PyTorch

83.69%

96.71%

21.252

30.303

EfficientNet B7 efficientnet-b7-pytorch

PyTorch

84.42%

96.91%

77.618

66.193

EfficientNet B7 AutoAugment efficientnet-b7_auto_aug

TensorFlow

84.68%

97.09%

77.618

66.193

HBONet 1.0 hbonet-1.0

PyTorch

73.10%

91.00%

0.6208

4.5443

HBONet 0.5 hbonet-0.5

PyTorch

67.00%

86.90%

0.1977

2.5287

HBONet 0.25 hbonet-0.25

PyTorch

57.30%

79.80%

0.0758

1.9299

Inception (GoogleNet) V1 googlenet-v1

Caffe

68.93%

89.14%

3.266

6.999

Inception (GoogleNet) V1 googlenet-v1-tf

TensorFlow

69.81%

89.60%

3.016

6.619

Inception (GoogleNet) V2 googlenet-v2

Caffe

72.02%

90.84%

4.058

11.185

Inception (GoogleNet) V2 googlenet-v2-tf

TensorFlow

74.09%

91.80%

4.058

11.185

Inception (GoogleNet) V3 googlenet-v3

TensorFlow

77.90%

93.81%

11.469

23.819

Inception (GoogleNet) V3 googlenet-v3-pytorch

PyTorch

77.69%

93.70%

11.469

23.817

Inception (GoogleNet) V4 googlenet-v4-tf

TensorFlow

80.20%

95.21%

24.584

42.648

Inception-ResNet V2 inception-resnet-v2-tf

TensorFlow

80.14%

95.10%

22.227

30.223

MixNet L mixnet-l

TensorFlow

78.30%

93.91%

0.565

7.3

MobileNet V1 0.25 128 mobilenet-v1-0.25-128

Caffe

40.54%

65.00%

0.028

0.468

MobileNet V1 0.5 160 mobilenet-v1-0.50-160

Caffe

59.86%

82.04%

0.156

1.327

MobileNet V1 0.5 224 mobilenet-v1-0.50-224

Caffe

63.04%

84.93%

0.304

1.327

MobileNet V1 1.0 224 mobilenet-v1-1.0-224

Caffe

69.50%

89.22%

1.148

4.221

MobileNet V1 1.0 224 mobilenet-v1-1.0-224-tf

TensorFlow

71.03%

89.94%

1.148

4.221

MobileNet V2 1.0 224 mobilenet-v2

Caffe

71.22%

90.18%

0.876

3.489

MobileNet V2 1.0 224 mobilenet-v2-1.0-224

TensorFlow

71.85%

90.69%

0.615

3.489

MobileNet V2 1.0 224 mobilenet-v2-pytorch

PyTorch

71.90%

90.30%

0.615

3.489

MobileNet V2 1.4 224 mobilenet-v2-1.4-224

TensorFlow

74.09%

91.97%

1.183

6.087

MobileNet V3 Small 1.0 mobilenet-v3-small-1.0-224-tf

TensorFlow

67.36%

87.45%

0.121

2.537

MobileNet V3 Large 1.0 mobilenet-v3-large-1.0-224-tf

TensorFlow

75.70%

92.76%

0.4536

5.4721

NFNet F0 nfnet-f0

PyTorch

83.34%

96.56%

24.8053

71.4444

DenseNet 121, alpha=0.125 octave-densenet-121-0.125

MXNet

76.07%

93.05%

4.883

7.977

RegNetX-3.2GF regnetx-3.2gf

PyTorch

78.17%

94.08%

6.3893

15.2653

ResNet 26, alpha=0.25 octave-resnet-26-0.25

MXNet

76.08%

92.58%

3.768

15.99

ResNet 50, alpha=0.125 octave-resnet-50-0.125

MXNet

78.19%

93.86%

7.221

25.551

ResNet 101, alpha=0.125 octave-resnet-101-0.125

MXNet

79.18%

94.42%

13.387

44.543

ResNet 200, alpha=0.125 octave-resnet-200-0.125

MXNet

79.99%

94.87%

25.407

64.667

ResNeXt 50, alpha=0.25 octave-resnext-50-0.25

MXNet

78.77%

94.18%

6.444

25.02

ResNeXt 101, alpha=0.25 octave-resnext-101-0.25

MXNet

79.56%

94.44%

11.521

44.169

SE-ResNet 50, alpha=0.125 octave-se-resnet-50-0.125

MXNet

78.71%

94.09%

7.246

28.082

open-closed-eye-0001 open-closed-eye-0001

PyTorch

95.84%

0.0014

0.0113

RepVGG A0 repvgg-a0

PyTorch

72.40%

90.49%

2.7286

8.3094

RepVGG B1 repvgg-b1

PyTorch

78.37%

94.09%

23.6472

51.8295

RepVGG B3 repvgg-b3

PyTorch

80.50%

95.25%

52.4407

110.9609

ResNeSt 50 resnest-50-pytorch

PyTorch

81.11%

95.36%

10.8148

27.4493

ResNet 18 resnet-18-pytorch

PyTorch

69.75%

89.09%

3.637

11.68

ResNet 34 resnet-34-pytorch

PyTorch

73.30%

91.42%

7.3409

21.7892

ResNet 50 resnet-50-pytorch

PyTorch

76.13%

92.86%

8.216

25.53

ResNet 50 resnet-50-caffe2

Caffe2

76.38%

93.19%

8.216

25.53

ResNet 50 resnet-50-tf

TensorFlow

76.17%

92.98%

8.2164

25.53

ReXNet V1 x1.0 rexnet-v1-x1.0

PyTorch

77.86%

93.87%

0.8325

4.7779

SE-Inception se-inception

Caffe

76.00%

92.96%

4.091

11.922

SE-ResNet 50 se-resnet-50

Caffe

77.60%

93.85%

7.775

28.061

SE-ResNet 101 se-resnet-101

Caffe

78.25%

94.21%

15.239

49.274

SE-ResNet 152 se-resnet-152

Caffe

78.51%

94.45%

22.709

66.746

SE-ResNeXt 50 se-resnext-50

Caffe

78.97%

94.63%

8.533

27.526

SE-ResNeXt 101 se-resnext-101

Caffe

80.17%

95.19%

16.054

48.886

Shufflenet V2 x1.0 shufflenet-v2-x1.0

PyTorch

69.36%

88.32%

0.2957

2.2705

SqueezeNet v1.0 squeezenet1.0

Caffe

57.68%

80.38%

1.737

1.248

SqueezeNet v1.1 squeezenet1.1

Caffe

58.38%

81.00%

0.785

1.236

SqueezeNet v1.1 squeezenet1.1-caffe2

Caffe2

56.50%

79.58%

0.785

1.236

VGG 16 vgg16

Caffe

70.97%

89.88%

30.974

138.36

VGG 19 vgg19

Caffe

71.06%

89.83%

39.3

143.667

VGG 19 vgg19-caffe2

Caffe2

71.06%

89.83%

39.3

143.667

Semantic Segmentation

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

DeepLab V3 deeplabv3

TensorFlow

66.85%

11.469

23.819

HRNet V2 C1 Segmentation hrnet-v2-c1-segmentation

PyTorch

77.69%

81.993

66.4768

Fastseg MobileV3Large LR-ASPP, F=128 fastseg-large

PyTorch

72.67%

140.9611

3.2

Fastseg MobileV3Small LR-ASPP, F=128 fastseg-small

PyTorch

67.15%

69.2204

1.1

PSPNet R-50-D8 pspnet-pytorch

PyTorch

70.60%

357.1719

46.5827

Instance Segmentation

Model Name Model Version

Implementation

Accuracy Metric 1

Accuracy Metric 2

GFlops

mParams

Mask R-CNN Inception ResNet V2 mask_rcnn_inception_resnet_v2_atrous_coco

TensorFlow

39.86%

35.36%

675.314

92.368

Mask R-CNN Inception V2 mask_rcnn_inception_v2_coco

TensorFlow

27.12%

21.48%

54.926

21.772

Mask R-CNN ResNet 50 mask_rcnn_resnet50_atrous_coco

TensorFlow

29.75%

27.46%

294.738

50.222

Mask R-CNN ResNet 101 mask_rcnn_resnet101_atrous_coco

TensorFlow

34.92%

31.30%

674.58

69.188

YOLACT ResNet 50 FPN yolact-resnet50-fpn-pytorch

PyTorch

28.00%

30.69%

118.575

36.829

3D Semantic Segmentation

Model Name Model Version

Implementation

Mean Accuracy

Median Accuracy

GFlops

mParams

Brain Tumor Segmentation brain-tumor-segmentation-0001

MXNet

92.40%

93.17%

409.996

38.192

Brain Tumor Segmentation 2 brain-tumor-segmentation-0002

PyTorch

91.48%

92.70%

300.801

4.51

Object Detection

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

CTPN ctpn

TensorFlow

73.67%

55.813

17.237

CenterNet (CTDET with DLAV0) 384x384 ctdet_coco_dlav0_384

ONNX

41.61%

34.994

17.911

CenterNet (CTDET with DLAV0) 512x512 ctdet_coco_dlav0_512

ONNX

44.28%

62.211

17.911

EfficientDet-D0 efficientdet-d0-tf

TensorFlow

31.95%

2.54

3.9

EfficientDet-D1 efficientdet-d1-tf

TensorFlow

37.54%

6.1

6.6

FaceBoxes faceboxes-pytorch

PyTorch

83.57%

1.8975

1.0059

Face Detection Retail face-detection-retail-0044

Caffe

83.00%

1.067

0.588

Faster R-CNN with Inception-ResNet v2 faster_rcnn_inception_resnet_v2_atrous_coco

TensorFlow

40.69%

30.687

13.307

Faster R-CNN with Inception v2 faster_rcnn_inception_v2_coco

TensorFlow

26.24%

30.687

13.307

Faster R-CNN with ResNet 50 faster_rcnn_resnet50_coco

TensorFlow

31.09%

57.203

29.162

Faster R-CNN with ResNet 101 faster_rcnn_resnet101_coco

TensorFlow

35.72%

112.052

48.128

MobileFace Detection V1 mobilefacedet-v1-mxnet

MXNet

78.75%

3.5456

7.6828

MTCNN mtcnn (mtcnn-p)

Caffe

62.26%

3.366

0.007

MTCNN mtcnn (mtcnn-r)

Caffe

62.26%

0.003

0.1

MTCNN mtcnn (mtcnn-o)

Caffe

62.26%

0.026

0.389

Pelee pelee-coco

Caffe

21.98%

1.290

5.98

RetinaFace with ResNet 50 retinaface-resnet50-pytorch

PyTorch

91.78%

88.8627

27.2646

RetinaNet with Resnet 50 retinanet-tf

TensorFlow

33.15%

238.9469

64.9706

R-FCN with Resnet-101 rfcn-resnet101-coco-tf

TensorFlow

45.02%

53.462

171.85

SSD 300 ssd300

Caffe

87.09%

62.815

26.285

SSD 512 ssd512

Caffe

91.07%

180.611

27.189

SSD with MobileNet mobilenet-ssd

Caffe

67.00%

2.316

5.783

SSD with MobileNet ssd_mobilenet_v1_coco

TensorFlow

23.32%

2.494

6.807

SSD with MobileNet FPN ssd_mobilenet_v1_fpn_coco

TensorFlow

35.55%

123.309

36.188

SSD with MobileNet V2 ssd_mobilenet_v2_coco

TensorFlow

24.95%

3.775

16.818

SSD lite with MobileNet V2 ssdlite_mobilenet_v2

TensorFlow

24.29%

1.525

4.475

SSD with ResNet-50 V1 FPN ssd_resnet50_v1_fpn_coco

TensorFlow

38.46%

178.6807

59.9326

SSD with ResNet 34 1200x1200 ssd-resnet34-1200-onnx

PyTorch

39.28%

433.411

20.058

Ultra Lightweight Face Detection RFB 320 ultra-lightweight-face-detection-rfb-320

PyTorch

84.78%

0.2106

0.3004

Ultra Lightweight Face Detection slim 320 ultra-lightweight-face-detection-slim-320

PyTorch

83.32%

0.1724

0.2844

Vehicle License Plate Detection Barrier vehicle-license-plate-detection-barrier-0123

TensorFlow

99.52%

0.271

0.547

YOLO v1 Tiny yolo-v1-tiny-tf

TensorFlow.js

54.79%

6.9883

15.8587

YOLO v2 Tiny yolo-v2-tiny-tf

Keras

29.12%

5.4236

11.2295

YOLO v2 yolo-v2-tf

Keras

56.48%

63.0301

50.9526

YOLO v3 yolo-v3-tf

Keras

67.72%

65.9843

61.9221

YOLO v3 Tiny yolo-v3-tiny-tf

Keras

39.70%

5.582

8.848

YOLO v4 yolo-v4-tf

Keras

77.40%

129.5567

64.33

YOLO v4 Tiny yolo-v4-tiny-tf

Keras

0.463%

6.9289

6.0535

Face Recognition

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

FaceNet facenet-20180408-102900

TensorFlow

99.14%

2.846

23.469

LResNet100E-IR,ArcFace@ms1m-refine-v2 face-recognition-resnet100-arcface-onnx

MXNet

99.68%

24.2115

65.1320

SphereFace Sphereface

Caffe

98.83%

3.504

22.671

Human Pose Estimation

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

human-pose-estimation-3d-0001 human-pose-estimation-3d-0001

PyTorch

100.45mm

18.998

5.074

single-human-pose-estimation-0001 single-human-pose-estimation-0001

PyTorch

69.05%

60.125

33.165

higher-hrnet-w32-human-pose-estimation higher-hrnet-w32-human-pose-estimation

PyTorch

64.64%

92.8364

28.6180

Monocular Depth Estimation

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

midasnet midasnet

PyTorch

0.07071

207.25144

104.081

FCRN ResNet50-Upproj fcrn-dp-nyu-depth-v2-tf

TensorFlow

0.573

63.5421

34.5255

Image Inpainting

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

GMCNN Inpainting gmcnn-places2-tf

TensorFlow

33.47Db

691.1589

12.7773

Style Transfer

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

fast-neural-style-mosaic-onnx fast-neural-style-mosaic-onnx

ONNX

12.04dB

15.518

1.679

Action Recognition

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

RGB-I3D, pretrained on ImageNet i3d-rgb-tf

TensorFlow

86.01%

278.9815

12.6900

common-sign-language-0001 common-sign-language-0001

PyTorch

93.58%

4.2269

4.1128

Colorization

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

colorization-v2 colorization-v2

PyTorch

26.99dB

83.6045

32.2360

colorization-siggraph colorization-siggraph

PyTorch

27.73dB

150.5441

34.0511

Sound Classification

Model Name Model Version

Implementation

Top 1 Accuracy

Top 5 Accuracy

GFlops

mParams

ACLNet aclnet

PyTorch

86%

92%

1.4

2.7

ACLNet-int8 aclnet-int8

PyTorch

87%

93%

1.41

2.71

Speech Recognition

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

DeepSpeech V0.6.1 mozilla-deepspeech-0.6.1

TensorFlow

7.55%

0.0472

47.2

DeepSpeech V0.8.2 mozilla-deepspeech-0.8.2

TensorFlow

6.13%

0.0472

47.2

QuartzNet quartznet-15x5-en

Pytorch

3.86%

2.4195

18.8857

Image Translation

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

CoCosNet cocosnet

PyTorch

12.93dB

1080.7032

167.9141

Optical Character Recognition

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

license-plate-recognition-barrier-0007 license-plate-recognition-barrier-0007

TensorFlow

98%

0.347

1.435

Place Recognition

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

NetVLAD netvlad-tf

TensorFlow

82.03%

36.6374

149.0021

Deblurring

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

DeblurGAN-v2 deblurgan-v2

PyTorch

28.25Db

80.8919

2.1083

Salient object detection

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

F3Net f3net

PyTorch

84.21%

31.2883

25.2791

Text Recognition

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

Resnet-FC text-recognition-resnet-fc

PyTorch

90.94%

40.3704

177.9668

Text to Speech

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

ForwardTacotron forward-tacotron-duration-prediction

PyTorch

6.66

13.81

ForwardTacotron forward-tacotron-regression

PyTorch

4.91

3.05

WaveRNN wavernn-upsampler

PyTorch

0.37

0.4

WaveRNN wavernn-rnn

PyTorch

0.06

3.83

Named Entity Recognition

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

bert-base-NER bert-base-ner

PyTorch

94.45%

22.3874

107.4319

Vehicle Reidentification

Model Name Model Version

Implementation

Accuracy

GFlops

mParams

vehicle-reid-0001 vehicle-reid-0001

PyTorch

96.31%

2.643

2.183