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 OpenVINO™ IR format (*.xml + *.bin) using the OpenVINO™ Model Downloader and other automation tools.

Classification Models

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

AlexNet

Caffe*

alexnet

56.598%/79.812%

1.5

60.965

AntiSpoofNet

PyTorch*

anti-spoof-mn3

3.81%

0.15

3.02

CaffeNet

Caffe*

caffenet

56.714%/79.916%

1.5

60.965

ConvNeXt Tiny

PyTorch*

convnext-tiny

82.05%/95.86%

8.9419

28.5892

DenseNet 121

Caffe* TensorFlow*

densenet-121 densenet-121-tf

74.42%/92.136% 74.46%/92.13%

5.723~5.7287

7.971

DLA 34

PyTorch*

dla-34

74.64%/92.06%

6.1368

15.7344

EfficientNet B0

TensorFlow* PyTorch*

efficientnet-b0 efficientnet-b0-pytorch

75.70%/92.76% 77.70%/93.52%

0.819

5.268

EfficientNet V2 B0

PyTorch*

efficientnet-v2-b0

78.36%/94.02%

1.4641

7.1094

EfficientNet V2 Small

PyTorch*

efficientnet-v2-s

84.29%/97.26%

16.9406

21.3816

HBONet 1.0

PyTorch*

hbonet-1.0

73.1%/91.0%

0.6208

4.5443

HBONet 0.25

PyTorch*

hbonet-0.25

57.3%/79.8%

0.0758

1.9299

Inception (GoogleNet) V1

Caffe* TensorFlow*

googlenet-v1 googlenet-v1-tf

68.928%/89.144% 69.814%/89.6%

3.016~3.266

6.619~6.999

Inception (GoogleNet) V2

Caffe* TensorFlow*

googlenet-v2 googlenet-v2-tf

72.024%/90.844% 74.084%/91.798%

4.058

11.185

Inception (GoogleNet) V3

TensorFlow* PyTorch*

googlenet-v3 googlenet-v3-pytorch

77.904%/93.808% 77.69%/93.7%

11.469

23.817

Inception (GoogleNet) V4

TensorFlow*

googlenet-v4-tf

80.204%/95.21%

24.584

42.648

Inception-ResNet V2

TensorFlow*

inception-resnet-v2-tf

77.82%/94.03%

22.227

30.223

LeViT 128S

PyTorch*

levit-128s

76.54%/92.85%

0.6177

8.2199

MixNet L

TensorFlow*

mixnet-l

78.30%/93.91%

0.565

7.3

MobileNet V1 0.25 128

Caffe*

mobilenet-v1-0.25-128

40.54%/65%

0.028

0.468

MobileNet V1 1.0 224

Caffe* TensorFlow*

mobilenet-v1-1.0-224 mobilenet-v1-1.0-224-tf

69.496%/89.224% 71.03%/89.94%

1.148

4.221

MobileNet V2 1.0 224

Caffe* TensorFlow* PyTorch*

mobilenet-v2 mobilenet-v2-1.0-224 mobilenet-v2-pytorch

71.218%/90.178% 71.85%/90.69% 71.81%/90.396%

0.615~0.876

3.489

MobileNet V2 1.4 224

TensorFlow*

mobilenet-v2-1.4-224

74.09%/91.97%

1.183

6.087

MobileNet V3 Small 1.0

TensorFlow* Paddle*

mobilenet-v3-small-1.0-224-tf mobilenet-v3-small-1.0-paddle

67.36%/87.44% 68.21%/88.04%

0.1168 0.1269

2.537 2.9339

MobileNet V3 Large 1.0

TensorFlow* Paddle*

mobilenet-v3-large-1.0-224-tf mobilenet-v3-large-1.0-224-paddle

75.30%/92.62% 75.248%/92.32%

0.4450 0.4565

5.4721 5.468

NFNet F0

PyTorch*

nfnet-f0

83.34%/96.56%

24.8053

71.4444

RegNetX-3.2GF

PyTorch*

regnetx-3.2gf

78.17%/94.08%

6.3893

15.2653

ResNet 26, alpha=0.25

MXNet*

octave-resnet-26-0.25

76.076%/92.584%

3.768

15.99

open-closed-eye-0001

PyTorch*

open-closed-eye-0001

95.84%

0.0014

0.0113

RepVGG A0

PyTorch*

repvgg-a0

72.40%/90.49%

2.7286

8.3094

RepVGG B1

PyTorch*

repvgg-b1

78.37%/94.09%

23.6472

51.8295

RepVGG B3

PyTorch*

repvgg-b3

80.50%/95.25%

52.4407

110.9609

ResNeSt 50

PyTorch*

resnest-50-pytorch

81.11%/95.36%

10.8148

27.4493

ResNet 18

PyTorch*

resnet-18-pytorch

69.754%/89.088%

3.637

11.68

ResNet 34

PyTorch*

resnet-34-pytorch

73.30%/91.42%

7.3409

21.7892

ResNet 50

PyTorch* TensorFlow*

resnet-50-pytorch resnet-50-tf

75.168%/92.212% 76.38%/93.188% 76.17%/92.98%

6.996~8.216

25.53

ReXNet V1 x1.0

PyTorch*

rexnet-v1-x1.0

77.86%/93.87%

0.8325

4.7779

SE-Inception

Caffe*

se-inception

75.996%/92.964%

4.091

11.922

SE-ResNet 50

Caffe*

se-resnet-50

77.596%/93.85%

7.775

28.061

SE-ResNeXt 50

Caffe*

se-resnext-50

78.968%/94.63%

8.533

27.526

Shufflenet V2 x0.5

Caffe*

shufflenet-v2-x0.5

58.80%/81.13%

0.08465

1.363

Shufflenet V2 x1.0

PyTorch*

shufflenet-v2-x1.0

69.36%/88.32%

0.2957

2.2705

SqueezeNet v1.0

Caffe*

squeezenet1.0

57.684%/80.38%

1.737

1.248

SqueezeNet v1.1

Caffe*

squeezenet1.1

58.382%/81%

0.785

1.236

Swin Transformer Tiny, window size=7

PyTorch*

swin-tiny-patch4-window7-224

81.38%/95.51%

9.0280

28.8173

T2T-ViT, transformer layers number=14

PyTorch*

t2t-vit-14

81.44%/95.66%

9.5451

21.5498

VGG 16

Caffe*

vgg16

70.968%/89.878%

30.974

138.358

VGG 19

Caffe*

vgg19

71.062%/89.832%

39.3

143.667

Segmentation Models

Semantic segmentation is an extension of object detection problem. Instead of returning bounding boxes, semantic segmentation models return a “painted” version of the input image, where the “color” of each pixel represents a certain class. These networks are much bigger than respective object detection networks, but they provide a better (pixel-level) localization of objects and they can detect areas with complex shape.

Semantic Segmentation Models

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

DeepLab V3

TensorFlow*

deeplabv3

68.41%

11.469

23.819

DRN-D-38

PyTorch*

drn-d-38

71.31%

1768.3276

25.9939

HRNet V2 C1 Segmentation

PyTorch*

hrnet-v2-c1-segmentation

77.69%

81.993

66.4768

Fastseg MobileV3Large LR-ASPP, F=128

PyTorch*

fastseg-large

72.67%

140.9611

3.2

Fastseg MobileV3Small LR-ASPP, F=128

PyTorch*

fastseg-small

67.15%

69.2204

1.1

PSPNet R-50-D8

PyTorch*

pspnet-pytorch

70.6%

357.1719

46.5827

OCRNet HRNet_w48

Paddle*

ocrnet-hrnet-w48-paddle

82.15%

324.66

70.47

Instance Segmentation Models

Instance segmentation is an extension of object detection and semantic segmentation problems. Instead of predicting a bounding box around each object instance instance segmentation model outputs pixel-wise masks for all instances.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

Mask R-CNN Inception ResNet V2

TensorFlow*

mask_rcnn_inception_resnet_v2_atrous_coco

39.86%/35.36%

675.314

92.368

Mask R-CNN ResNet 50

TensorFlow*

mask_rcnn_resnet50_atrous_coco

29.75%/27.46%

294.738

50.222

YOLACT ResNet 50 FPN

PyTorch*

yolact-resnet50-fpn-pytorch

28.0%/30.69%

118.575

36.829

3D Semantic Segmentation Models

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

Brain Tumor Segmentation

MXNet*

brain-tumor-segmentation-0001

92.4003%

409.996

38.192

Brain Tumor Segmentation 2

PyTorch*

brain-tumor-segmentation-0002

91.4826%

300.801

4.51

Object Detection Models

Several detection models can be used to detect a set of the most popular objects - for example, faces, people, vehicles. Most of the networks are SSD-based and provide reasonable accuracy/performance trade-offs.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

CTPN

TensorFlow*

ctpn

73.67%

55.813

17.237

CenterNet (CTDET with DLAV0) 512x512

ONNX*

ctdet_coco_dlav0_512

44.2756%

62.211

17.911

DETR-ResNet50

PyTorch*

detr-resnet50

39.27% / 42.36%

174.4708

41.3293

EfficientDet-D0

TensorFlow*

efficientdet-d0-tf

31.95%

2.54

3.9

EfficientDet-D1

TensorFlow*

efficientdet-d1-tf

37.54%

6.1

6.6

FaceBoxes

PyTorch*

faceboxes-pytorch

83.565%

1.8975

1.0059

Face Detection Retail

Caffe*

face-detection-retail-0044

83.00%

1.067

0.588

Faster R-CNN with Inception-ResNet v2

TensorFlow*

faster_rcnn_inception_resnet_v2_atrous_coco

40.69%

30.687

13.307

Faster R-CNN with ResNet 50

TensorFlow*

faster_rcnn_resnet50_coco

31.09%

57.203

29.162

MobileFace Detection V1

MXNet*

mobilefacedet-v1-mxnet

78.7488%

3.5456

7.6828

Mobilenet-yolo-v4-syg

Keras*

mobilenet-yolo-v4-syg

86.35%

65.981

61.922

MTCNN

Caffe*

mtcnn : mtcnn-p mtcnn-r mtcnn-o

48.1308%/62.2625%

3.3715 0.0031 0.0263

0.0066 0.1002 0.3890

NanoDet with ShuffleNetV2 1.5x, size=416

PyTorch*

nanodet-m-1.5x-416

27.38%/26.63%

2.3895

2.0534

NanoDet Plus with ShuffleNetV2 1.5x, size=416

PyTorch*

nanodet-plus-m-1.5x-416

34.53%/33.77%

3.0147

2.4614

Pelee

Caffe*

pelee-coco

21.9761%

1.290

5.98

RetinaFace with ResNet 50

PyTorch*

retinaface-resnet50-pytorch

91.78%

88.8627

27.2646

RetinaNet with Resnet 50

TensorFlow*

retinanet-tf

33.15%

238.9469

64.9706

R-FCN with Resnet-101

TensorFlow*

rfcn-resnet101-coco-tf

28.40%/45.02%

53.462

171.85

SSD 300

Caffe*

ssd300

87.09%

62.815

26.285

SSD 512

Caffe*

ssd512

91.07%

180.611

27.189

SSD with MobileNet

Caffe* TensorFlow*

mobilenet-ssd ssd_mobilenet_v1_coco

67.00% 23.32%

2.316~2.494

5.783~6.807

SSD with MobileNet FPN

TensorFlow*

ssd_mobilenet_v1_fpn_coco

35.5453%

123.309

36.188

SSD lite with MobileNet V2

TensorFlow*

ssdlite_mobilenet_v2

24.2946%

1.525

4.475

SSD with ResNet 34 1200x1200

PyTorch*

ssd-resnet34-1200-onnx

20.7198%/39.2752%

433.411

20.058

Ultra Lightweight Face Detection RFB 320

PyTorch*

ultra-lightweight-face-detection-rfb-320

84.78%

0.2106

0.3004

Ultra Lightweight Face Detection slim 320

PyTorch*

ultra-lightweight-face-detection-slim-320

83.32%

0.1724

0.2844

Vehicle License Plate Detection Barrier

TensorFlow*

vehicle-license-plate-detection-barrier-0123

99.52%

0.271

0.547

YOLO v1 Tiny

TensorFlow.js*

yolo-v1-tiny-tf

54.79%

6.9883

15.8587

YOLO v2 Tiny

Keras*

yolo-v2-tiny-tf

27.3443%/29.1184%

5.4236

11.2295

YOLO v2

Keras*

yolo-v2-tf

53.1453%/56.483%

63.0301

50.9526

YOLO v3

Keras* ONNX*

yolo-v3-tf yolo-v3-onnx

62.2759%/67.7221% 48.30%/47.07%

65.9843~65.998

61.9221~61.930

YOLO v3 Tiny

Keras* ONNX*

yolo-v3-tiny-tf yolo-v3-tiny-onnx

35.9%/39.7% 17.07%/13.64%

5.582

8.848~8.8509

YOLO v4

Keras*

yolo-v4-tf

71.23%/77.40%/50.26%

129.5567

64.33

YOLO v4 Tiny

Keras*

yolo-v4-tiny-tf

6.9289

6.0535

YOLOF

PyTorch*

yolof

60.69%/66.23%/43.63%

175.37942

48.228

YOLOX Tiny

PyTorch*

yolox-tiny

47.85%/52.56%/31.82%

6.4813

5.0472

Face Recognition Models

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

FaceNet

TensorFlow*

facenet-20180408-102900

99.14%

2.846

23.469

LResNet100E-IR,ArcFace@ms1m-refine-v2

MXNet*

face-recognition-resnet100-arcface-onnx

99.68%

24.2115

65.1320

SphereFace

Caffe*

Sphereface

98.8321%

3.504

22.671

Human Pose Estimation Models

Human pose estimation task is to predict a pose: body skeleton, which consists of keypoints and connections between them, for every person in an input image or video. Keypoints are body joints, i.e. ears, eyes, nose, shoulders, knees, etc. There are two major groups of such methods: top-down and bottom-up. The first detects persons in a given frame, crops or rescales detections, then runs pose estimation network for every detection. These methods are very accurate. The second finds all keypoints in a given frame, then groups them by person instances, thus faster than previous, because network runs once.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

human-pose-estimation-3d-0001

PyTorch*

human-pose-estimation-3d-0001

100.44437mm

18.998

5.074

single-human-pose-estimation-0001

PyTorch*

single-human-pose-estimation-0001

69.0491%

60.125

33.165

higher-hrnet-w32-human-pose-estimation

PyTorch*

higher-hrnet-w32-human-pose-estimation

64.64%

92.8364

28.6180

Monocular Depth Estimation Models

The task of monocular depth estimation is to predict a depth (or inverse depth) map based on a single input image. Since this task contains - in the general setting - some ambiguity, the resulting depth maps are often only defined up to an unknown scaling factor.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

midasnet

PyTorch*

midasnet

0.07071

207.25144

104.081

FCRN ResNet50-Upproj

TensorFlow*

fcrn-dp-nyu-depth-v2-tf

0.573

63.5421

34.5255

Image Inpainting Models

Image inpainting task is to estimate suitable pixel information to fill holes in images.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

GMCNN Inpainting

TensorFlow*

gmcnn-places2-tf

33.47Db

691.1589

12.7773

Hybrid-CS-Model-MRI

TensorFlow*

hybrid-cs-model-mri

34.27Db

146.6037

11.3313

Style Transfer Models

Style transfer task is to transfer the style of one image to another.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

fast-neural-style-mosaic-onnx

ONNX*

fast-neural-style-mosaic-onnx

12.04dB

15.518

1.679

Action Recognition Models

The task of action recognition is to predict action that is being performed on a short video clip (tensor formed by stacking sampled frames from input video).

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

RGB-I3D, pretrained on ImageNet*

TensorFlow*

i3d-rgb-tf

64.83%/84.58%

278.9815

12.6900

common-sign-language-0001

PyTorch*

common-sign-language-0001

93.58%

4.2269

4.1128

Colorization Models

Colorization task is to predict colors of scene from grayscale image.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

colorization-v2

PyTorch*

colorization-v2

26.99dB

83.6045

32.2360

colorization-siggraph

PyTorch*

colorization-siggraph

27.73dB

150.5441

34.0511

Sound Classification Models

The task of sound classification is to predict what sounds are in an audio fragment.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

ACLNet

PyTorch*

aclnet

86%/92%

1.4

2.7

ACLNet-int8

PyTorch*

aclnet-int8

87%/93%

1.41

2.71

Speech Recognition Models

The task of speech recognition is to recognize and translate spoken language into text.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

DeepSpeech V0.6.1

TensorFlow*

mozilla-deepspeech-0.6.1

7.55%

0.0472

47.2

DeepSpeech V0.8.2

TensorFlow*

mozilla-deepspeech-0.8.2

6.13%

0.0472

47.2

QuartzNet

PyTorch*

quartznet-15x5-en

3.86%

2.4195

18.8857

Wav2Vec 2.0 Base

PyTorch*

wav2vec2-base

3.39%

26.843

94.3965

Image Translation Models

The task of image translation is to generate the output based on exemplar.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

CoCosNet

PyTorch*

cocosnet

12.93dB

1080.7032

167.9141

Optical Character Recognition Models

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

license-plate-recognition-barrier-0007

TensorFlow*

license-plate-recognition-barrier-0007

98%

0.347

1.435

Place Recognition Models

The task of place recognition is to quickly and accurately recognize the location of a given query photograph.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

NetVLAD

TensorFlow*

netvlad-tf

82.0321%

36.6374

149.0021

Deblurring Models

The task of image deblurring.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

DeblurGAN-v2

PyTorch*

deblurgan-v2

28.25Db

80.8919

2.1083

JPEG Artifacts Removal Models

The task of restoration images from jpeg format.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

FBCNN

PyTorch*

fbcnn

34.34Db

1420.78235

71.922

Salient Object Detection Models

Salient object detection is a task-based on a visual attention mechanism, in which algorithms aim to explore objects or regions more attentive than the surrounding areas on the scene or images.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

F3Net

PyTorch*

f3net

84.21%

31.2883

25.2791

Text Prediction Models

Text prediction is a task to predict the next word, given all of the previous words within some text.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

GPT-2

PyTorch*

gpt-2

29.00%

293.0489

175.6203

Text Recognition Models

Scene text recognition is a task to recognize text on a given image. Researchers compete on creating algorithms which are able to recognize text of different shapes, fonts and background. See details about datasets in here The reported metric is collected over the alphanumeric subset of ICDAR13 (1015 images) in case-insensitive mode.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

Resnet-FC

PyTorch*

text-recognition-resnet-fc

90.94%

40.3704

177.9668

ViTSTR Small patch=16, size=224

PyTorch*

vitstr-small-patch16-224

90.34%

9.1544

21.5061

Text to Speech Models

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

ForwardTacotron

PyTorch*

forward-tacotron : forward-tacotron-duration-prediction forward-tacotron-regression

6.66 4.91

13.81 3.05

WaveRNN

PyTorch*

wavernn : wavernn-upsampler wavernn-rnn

0.37 0.06

0.4 3.83

Named Entity Recognition Models

Named entity recognition (NER) is the task of tagging entities in text with their corresponding type.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

bert-base-NER

PyTorch*

bert-base-ner

94.45%

22.3874

107.4319

Vehicle Reidentification Models

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

vehicle-reid-0001

PyTorch*

vehicle-reid-0001

96.31%/85.15 %

2.643

2.183

Background Matting Models

Background matting is a method of separating a foreground from a background in an image or video, wherein some pixels may belong to foreground as well as background, such pixels are called partial or mixed pixels. This distinguishes background matting from segmentation approaches where the result is a binary mask.

Model Name

Implementation

OMZ Model Name

Accuracy

GFlops

mParams

background-matting-mobilenetv2

PyTorch*

background-matting-mobilenetv2

4.32/1.0/2.48/2.7

6.7419

5.052

modnet-photographic-portrait-matting

PyTorch*

modnet-photographic-portrait-matting

5.21/727.95

31.1564

6.4597

modnet-webcam-portrait-matting

PyTorch*

modnet-webcam-portrait-matting

5.66/762.52

31.1564

6.4597

robust-video-matting-mobilenetv3

PyTorch*

robust-video-matting-mobilenetv3

20.8/15.1/4.42/4.05

9.3892

3.7363