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 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
DenseNet 121 Caffe*
TensorFlow*
Caffe2*
densenet-121
densenet-121-tf
densenet-121-caffe2
74.42%/92.136%
74.46%/92.13%
74.904%/92.192%
5.723~5.7287 7.971
DenseNet 161 Caffe*
TensorFlow*
densenet-161
densenet-161-tf
77.55%/93.92%
76.446%/93.228%
14.128~15.561 28.666
DenseNet 169 Caffe*
TensorFlow*
densenet-169
densenet-169-tf
76.106%/93.106%
76.14%/93.12%
6.788~6.7932 14.139
DenseNet 201 Caffe*
TensorFlow*
densenet-201
densenet-201-tf
76.886%/93.556%
76.93%/93.56%
8.673~8.6786 20.001
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%
76.91%/93.21%
0.819 5.268
EfficientNet B0 AutoAugment TensorFlow* efficientnet-b0_auto_aug 76.43%/93.04% 0.819 5.268
EfficientNet B5 TensorFlow*
PyTorch*
efficientnet-b5
efficientnet-b5-pytorch
83.33%/96.67%
83.69%/96.71%
21.252 30.303
EfficientNet B7 PyTorch* efficientnet-b7-pytorch 84.42%/96.91% 77.618 66.193
EfficientNet B7 AutoAugment TensorFlow* efficientnet-b7_auto_aug 84.68%/97.09% 77.618 66.193
HBONet 1.0 PyTorch* hbonet-1.0 73.1%/91.0% 0.6208 4.5443
HBONet 0.5 PyTorch* hbonet-0.5 67.0%/86.9% 0.1977 2.5287
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 80.14%/95.10% 22.227 30.223
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 0.5 160 Caffe* mobilenet-v1-0.50-160 59.86%/82.04% 0.156 1.327
MobileNet V1 0.5 224 Caffe* mobilenet-v1-0.50-224 63.042%/84.934% 0.304 1.327
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* mobilenet-v3-small-1.0-224-tf 67.36%/87.45% 0.121 2.537
MobileNet V3 Large 1.0 TensorFlow* mobilenet-v3-large-1.0-224-tf 75.70%/92.76% 0.4536 5.4721
NFNet F0 PyTorch* nfnet-f0 83.34%/96.56% 24.8053 71.4444
DenseNet 121, alpha=0.125 MXNet* octave-densenet-121-0.125 76.066%/93.044% 4.883 7.977
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
ResNet 50, alpha=0.125 MXNet* octave-resnet-50-0.125 78.19%/93.862% 7.221 25.551
ResNet 101, alpha=0.125 MXNet* octave-resnet-101-0.125 79.182%/94.42% 13.387 44.543
ResNet 200, alpha=0.125 MXNet* octave-resnet-200-0.125 79.99%/94.866% 25.407 64.667
ResNeXt 50, alpha=0.25 MXNet* octave-resnext-50-0.25 78.772%/94.18% 6.444 25.02
ResNeXt 101, alpha=0.25 MXNet* octave-resnext-101-0.25 79.556%/94.444% 11.521 44.169
SE-ResNet 50, alpha=0.125 MXNet* octave-se-resnet-50-0.125 78.706%/94.09% 7.246 28.082
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*
Caffe2*
TensorFlow*
resnet-50-pytorch
resnet-50-caffe2
resnet-50-tf
75.168%/92.212%
76.128%/92.858%
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-ResNet 101 Caffe* se-resnet-101 78.252%/94.206% 15.239 49.274
SE-ResNet 152 Caffe* se-resnet-152 78.506%/94.45% 22.709 66.746
SE-ResNeXt 50 Caffe* se-resnext-50 78.968%/94.63% 8.533 27.526
SE-ResNeXt 101 Caffe* se-resnext-101 80.168%/95.19% 16.054 48.886
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*
Caffe2*
squeezenet1.1
squeezenet1.1-caffe2
58.382%/81%
56.502%/79.576%
0.785 1.236
VGG 16 Caffe* vgg16 70.968%/89.878% 30.974 138.358
VGG 19 Caffe*
Caffe2*
vgg19
vgg19-caffe2
71.062%/89.832%
71.062%/89.832%
39.3 143.667

Segmentation

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

Model Name Implementation OMZ Model Name Accuracy GFlops mParams
DeepLab V3 TensorFlow* deeplabv3 66.85% 11.469 23.819
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

Instance Segmentation

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 Inception V2 TensorFlow* mask_rcnn_inception_v2_coco 27.12%/21.48% 54.926 21.772
Mask R-CNN ResNet 50 TensorFlow* mask_rcnn_resnet50_atrous_coco 29.75%/27.46% 294.738 50.222
Mask R-CNN ResNet 101 TensorFlow* mask_rcnn_resnet101_atrous_coco 34.92%/31.30% 674.58 69.188
YOLACT ResNet 50 FPN PyTorch* yolact-resnet50-fpn-pytorch 28.0%/30.69% 118.575 36.829

3D Semantic Segmentation

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

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) 384x384 ONNX* ctdet_coco_dlav0_384 41.6105% 34.994 17.911
CenterNet (CTDET with DLAV0) 512x512 ONNX* ctdet_coco_dlav0_512 44.2756% 62.211 17.911
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 Inception v2 TensorFlow* faster_rcnn_inception_v2_coco 26.24% 30.687 13.307
Faster R-CNN with ResNet 50 TensorFlow* faster_rcnn_resnet50_coco 31.09% 57.203 29.162
Faster R-CNN with ResNet 101 TensorFlow* faster_rcnn_resnet101_coco 35.72% 112.052 48.128
MobileFace Detection V1 MXNet* mobilefacedet-v1-mxnet 78.7488% 3.5456 7.6828
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
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 with MobileNet V2 TensorFlow* ssd_mobilenet_v2_coco 24.9452% 3.775 16.818
SSD lite with MobileNet V2 TensorFlow* ssdlite_mobilenet_v2 24.2946% 1.525 4.475
SSD with ResNet-50 V1 FPN TensorFlow* ssd_resnet50_v1_fpn_coco 38.4557% 178.6807 59.9326
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* yolo-v3-tf 62.2759%/67.7221% 65.9843 61.9221
YOLO v3 Tiny Keras* yolo-v3-tiny-tf 35.9%/39.7% 5.582 8.848
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

Face Recognition

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

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

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

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

Style Transfer

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

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 65.96%/86.01% 278.9815 12.6900
common-sign-language-0001 PyTorch* common-sign-language-0001 93.58% 4.2269 4.1128

Colorization

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

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

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

Image Translation

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

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

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

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

Salient object detection

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 Recognition

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 icdar 13 (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

Text to Speech

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

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

Model Name Implementation OMZ Model Name Accuracy GFlops mParams
vehicle-reid-0001 PyTorch* vehicle-reid-0001 96.31%/85.15 % 2.643 2.183

See Also

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