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Overview of OpenVINO™ Toolkit Public Models

OpenVINO™ toolkit provides a set of public 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 models can be downloaded via Model Downloader (<OPENVINO_INSTALL_DIR>/deployment_tools/open_model_zoo/tools/downloader).

Classification

Model Name Implementation OMZ Model Name Accuracy GFlops mParams
AlexNet Caffe* alexnet 1.5 60.965
CaffeNet Caffe* caffenet 1.5 60.965
DenseNet 121 Caffe*
TensorFlow*		 densenet-121
densenet-121-tf		 5.289~5.724 7.971
DenseNet 161 Caffe*
TensorFlow*		 densenet-161
densenet-161-tf		 14.128~15.561 28.666
DenseNet 169 Caffe*
TensorFlow*		 densenet-169
densenet-169-tf		 6.16~6.788 14.139
DenseNet 201 Caffe* densenet-201 8.673 20.001
Inception (GoogleNet) V1 Caffe* googlenet-v1 3.266 6.999
Inception (GoogleNet) V2 Caffe* googlenet-v2 4.058 11.185
Inception (GoogleNet) V3 Caffe*
PyTorch*		 googlenet-v3
googlenet-v3-pytorch		 11.469 23.817
Inception (GoogleNet) V4 Caffe* googlenet-v4 24.584 42.648
Inception-ResNet V2 Caffe*
TensorFlow*		 inception-resnet-v2
inception-resnet-v2-tf		 22.227~26.405 30.223~55.813
MobileNet V1 0.25 128 Caffe* mobilenet-v1-0.25-128 0.028 0.468
MobileNet V1 0.5 160 Caffe* mobilenet-v1-0.50-160 0.156 1.327
MobileNet V1 0.5 224 Caffe* mobilenet-v1-0.50-224 0.304 1.327
MobileNet V1 1.0 224 Caffe*
TensorFlow*		 mobilenet-v1-1.0-224
mobilenet-v1-1.0-224-tf		 1.148 4.221
MobileNet V2 1.0 224 Caffe*
TensorFlow*
PyTorch*		 mobilenet-v2
mobilenet-v2-1.0-224
mobilenet-v2-pytorch		 0.615~0.876 3.489
MobileNet V2 1.4 224 TensorFlow* mobilenet-v2-1.4-224 1.183 6.087
ResNet 50 Caffe*
PyTorch*		 resnet-50
resnet-50-pytorch		 6.996~8.216 25.53
ResNet 101 Caffe* resnet-101 14.441 44.496
ResNet 152 Caffe* resnet-152 21.89 60.117
SE-Inception Caffe* se-inception 4.091 11.922
SE-ResNet 50 Caffe* se-resnet-50 7.775 28.061
SE-ResNet 101 Caffe* se-resnet-101 15.239 49.274
SE-ResNet 152 Caffe* se-resnet-152 22.709 66.746
SE-ResNeXt 50 Caffe* se-resnext-50 8.533 27.526
SE-ResNeXt 101 Caffe* se-resnext-101 16.054 48.886
SqueezeNet v1.0 Caffe* squeezenet1.0 1.737 1.248
SqueezeNet v1.1 Caffe* squeezenet1.1 0.785 1.236
VGG 16 Caffe* vgg16 30.974 138.358
VGG 19 Caffe* vgg19 39.3 143.667

Octave Convolutions Networks

This is are modifications of networks using Octave Convolutions. More details can be found here.

Model Name Implementation OMZ Model Name Accuracy GFlops mParams
DenseNet 121, alpha=0.125 MXNet* octave-densenet-121-0.125 4.883 7.977
ResNet 26, alpha=0.25 MXNet* octave-resnet-26-0.25 3.768 15.99
ResNet 50, alpha=0.125 MXNet* octave-resnet-50-0.125 7.221 25.551
ResNet 101, alpha=0.125 MXNet* octave-resnet-101-0.125 13.387 44.543
ResNet 200, alpha=0.125 MXNet* octave-resnet-200-0.125 25.407 64.667
ResNeXt 50, alpha=0.25 MXNet* octave-resnext-50-0.25 6.444 25.02
ResNeXt 101, alpha=0.25 MXNet* octave-resnext-101-0.25 11.521 44.169
SE-ResNet 50, alpha=0.125 MXNet* octave-se-resnet-50-0.125 7.246 28.082

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 GFlops mParams
DeepLab V3 TensorFlow* deeplabv3 11.469 23.819

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 GFlops mParams
Mask R-CNN Inception ResNet V2 TensorFlow* mask_rcnn_inception_resnet_v2_atrous_coco 675.314 92.368
Mask R-CNN Inception V2 TensorFlow* mask_rcnn_inception_v2_coco 54.926 21.772
Mask R-CNN ResNet 50 TensorFlow* mask_rcnn_resnet50_atrous_coco 294.738 50.222
Mask R-CNN ResNet 101 TensorFlow* mask_rcnn_resnet101_atrous_coco 674.58 69.188

3D Semantic Segmentation

Model Name Implementation OMZ Model Name GFlops mParams
Brain Tumor Segmentation MXNet* brain-tumor-segmentation-0001 409.996 38.192

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 GFlops mParams
CTPN TensorFlow* ctpn 55.813 17.237
Faster R-CNN with Inception-ResNet v2 TensorFlow* faster_rcnn_inception_resnet_v2_atrous_coco 30.687 13.307
Faster R-CNN with Inception v2 TensorFlow* faster_rcnn_inception_v2_coco 30.687 13.307
Faster R-CNN with ResNet 50 TensorFlow* faster_rcnn_resnet50_coco 57.203 29.162
Faster R-CNN with ResNet 101 TensorFlow* faster_rcnn_resnet101_coco 112.052 48.128
MTCNN Caffe*:
proposal
refine
output
mtcnn-p
mtcnn-r
mtcnn-o
SSD 300 Caffe* ssd300 62.815 26.285
SSD 512 Caffe* ssd512 180.611 27.189
SSD with MobileNet Caffe*
TensorFlow*		 mobilenet-ssd
ssd_mobilenet_v1_coco		 2.316~2.494 5.783~6.807
SSD with MobileNet FPN TensorFlow* ssd_mobilenet_v1_fpn_coco 123.309 36.188
SSD with MobileNet V2 TensorFlow* ssd_mobilenet_v2_coco 3.775 16.818
SSD lite with MobileNet V2 TensorFlow* ssdlite_mobilenet_v2 1.525 4.475

Face Recognition

Model Name Implementation OMZ Model Name GFlops mParams
FaceNet TensorFlow* facenet-20180408-102900 2.846 23.469
LResNet34E-IR,ArcFace@ms1m-refine-v1 MXNet* face-recognition-resnet34-arcface 8.934 34.129
LResNet50E-IR,ArcFace@ms1m-refine-v1 MXNet* face-recognition-resnet50-arcface 12.637 43.576
LResNet100E-IR,ArcFace@ms1m-refine-v2 MXNet* face-recognition-resnet100-arcface 24.209 65.131
MobileFaceNet,ArcFace@ms1m-refine-v1 MXNet* face-recognition-mobilefacenet-arcface 0.449 0.993
SphereFace Caffe* Sphereface 3.504 22.671

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