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Overview of OpenVINO™ Toolkit Public Pre-Trained Models

:maxdepth: 1
:hidden:
:caption: Device Support

   Public Pre-Trained Models Device Support <omz_models_public_device_support>
   aclnet <omz_models_model_aclnet>
   aclnet-int8 <omz_models_model_aclnet_int8>
   anti-spoof-mn3 <omz_models_model_anti_spoof_mn3>
   background-matting-mobilenetv2 <omz_models_model_background_matting_mobilenetv2>
   bert-base-ner <omz_models_model_bert_base_ner>
   brain-tumor-segmentation-0002 <omz_models_model_brain_tumor_segmentation_0002>
   cocosnet <omz_models_model_cocosnet>
   colorization-siggraph <omz_models_model_colorization_siggraph>
   colorization-v2 <omz_models_model_colorization_v2>
   common-sign-language-0001 <omz_models_model_common_sign_language_0001>
   convnext-tiny <omz_models_model_convnext_tiny>
   ctdet_coco_dlav0_512 <omz_models_model_ctdet_coco_dlav0_512>
   ctpn <omz_models_model_ctpn>
   deeplabv3 <omz_models_model_deeplabv3>
   densenet-121-tf <omz_models_model_densenet_121_tf>
   detr-resnet50 <omz_models_model_detr_resnet50>
   dla-34 <omz_models_model_dla_34>
   drn-d-38 <omz_models_model_drn_d_38>
   efficientdet-d0-tf <omz_models_model_efficientdet_d0_tf>
   efficientdet-d1-tf <omz_models_model_efficientdet_d1_tf>
   efficientnet-b0 <omz_models_model_efficientnet_b0>
   efficientnet-b0-pytorch <omz_models_model_efficientnet_b0_pytorch>
   efficientnet-v2-b0 <omz_models_model_efficientnet_v2_b0>
   efficientnet-v2-s <omz_models_model_efficientnet_v2_s>
   erfnet <omz_models_model_erfnet>
   f3net <omz_models_model_f3net>
   face-recognition-resnet100-arcface-onnx <omz_models_model_face_recognition_resnet100_arcface_onnx>
   faceboxes-pytorch <omz_models_model_faceboxes_pytorch>
   facenet-20180408-102900 <omz_models_model_facenet_20180408_102900>
   fast-neural-style-mosaic-onnx <omz_models_model_fast_neural_style_mosaic_onnx>
   faster_rcnn_inception_resnet_v2_atrous_coco <omz_models_model_faster_rcnn_inception_resnet_v2_atrous_coco>
   faster_rcnn_resnet50_coco <omz_models_model_faster_rcnn_resnet50_coco>
   fastseg-large <omz_models_model_fastseg_large>
   fastseg-small <omz_models_model_fastseg_small>
   fbcnn <omz_models_model_fbcnn>
   fcrn-dp-nyu-depth-v2-tf <omz_models_model_fcrn_dp_nyu_depth_v2_tf>
   forward-tacotron (composite) <omz_models_model_forward_tacotron>
   gmcnn-places2-tf <omz_models_model_gmcnn_places2_tf>
   googlenet-v1-tf <omz_models_model_googlenet_v1_tf>
   googlenet-v2-tf <omz_models_model_googlenet_v2_tf>
   googlenet-v3 <omz_models_model_googlenet_v3>
   googlenet-v3-pytorch <omz_models_model_googlenet_v3_pytorch>
   googlenet-v4-tf <omz_models_model_googlenet_v4_tf>
   gpt-2 <omz_models_model_gpt_2>
   hbonet-0.25 <omz_models_model_hbonet_0_25>
   hbonet-1.0 <omz_models_model_hbonet_1_0>
   higher-hrnet-w32-human-pose-estimation <omz_models_model_higher_hrnet_w32_human_pose_estimation>
   hrnet-v2-c1-segmentation <omz_models_model_hrnet_v2_c1_segmentation>
   human-pose-estimation-3d-0001 <omz_models_model_human_pose_estimation_3d_0001>
   hybrid-cs-model-mri <omz_models_model_hybrid_cs_model_mri>
   i3d-rgb-tf <omz_models_model_i3d_rgb_tf>
   inception-resnet-v2-tf <omz_models_model_inception_resnet_v2_tf>
   levit-128s <omz_models_model_levit_128s>
   license-plate-recognition-barrier-0007 <omz_models_model_license_plate_recognition_barrier_0007>
   mask_rcnn_inception_resnet_v2_atrous_coco <omz_models_model_mask_rcnn_inception_resnet_v2_atrous_coco>
   mask_rcnn_resnet50_atrous_coco <omz_models_model_mask_rcnn_resnet50_atrous_coco>
   midasnet <omz_models_model_midasnet>
   mixnet-l <omz_models_model_mixnet_l>
   mobilenet-v1-0.25-128 <omz_models_model_mobilenet_v1_0_25_128>
   mobilenet-v1-1.0-224-tf <omz_models_model_mobilenet_v1_1_0_224_tf>
   mobilenet-v2-1.0-224 <omz_models_model_mobilenet_v2_1_0_224>
   mobilenet-v2-1.4-224 <omz_models_model_mobilenet_v2_1_4_224>
   mobilenet-v2-pytorch <omz_models_model_mobilenet_v2_pytorch>
   mobilenet-v3-large-1.0-224-tf <omz_models_model_mobilenet_v3_large_1_0_224_tf>
   mobilenet-v3-small-1.0-224-tf <omz_models_model_mobilenet_v3_small_1_0_224_tf>
   mobilenet-yolo-v4-syg <omz_models_model_mobilenet_yolo_v4_syg>
   modnet-photographic-portrait-matting <omz_models_model_modnet_photographic_portrait_matting>
   modnet-webcam-portrait-matting <omz_models_model_modnet_webcam_portrait_matting>
   mozilla-deepspeech-0.6.1 <omz_models_model_mozilla_deepspeech_0_6_1>
   mozilla-deepspeech-0.8.2 <omz_models_model_mozilla_deepspeech_0_8_2>
   nanodet-m-1.5x-416 <omz_models_model_nanodet_m_1_5x_416>
   nanodet-plus-m-1.5x-416 <omz_models_model_nanodet_plus_m_1_5x_416>
   netvlad-tf <omz_models_model_netvlad_tf>
   nfnet-f0 <omz_models_model_nfnet_f0>
   open-closed-eye-0001 <omz_models_model_open_closed_eye_0001>
   pspnet-pytorch <omz_models_model_pspnet_pytorch>
   quartznet-15x5-en <omz_models_model_quartznet_15x5_en>
   regnetx-3.2gf <omz_models_model_regnetx_3_2gf>
   repvgg-a0 <omz_models_model_repvgg_a0>
   repvgg-b1 <omz_models_model_repvgg_b1>
   repvgg-b3 <omz_models_model_repvgg_b3>
   resnest-50-pytorch <omz_models_model_resnest_50_pytorch>
   resnet-18-pytorch <omz_models_model_resnet_18_pytorch>
   resnet-34-pytorch <omz_models_model_resnet_34_pytorch>
   resnet-50-pytorch <omz_models_model_resnet_50_pytorch>
   resnet-50-tf <omz_models_model_resnet_50_tf>
   retinaface-resnet50-pytorch <omz_models_model_retinaface_resnet50_pytorch>
   retinanet-tf <omz_models_model_retinanet_tf>
   rexnet-v1-x1.0 <omz_models_model_rexnet_v1_x1_0>
   rfcn-resnet101-coco-tf <omz_models_model_rfcn_resnet101_coco_tf>
   robust-video-matting-mobilenetv3 <omz_models_model_robust_video_matting_mobilenetv3>
   shufflenet-v2-x1.0 <omz_models_model_shufflenet_v2_x1_0>
   single-human-pose-estimation-0001 <omz_models_model_single_human_pose_estimation_0001>
   ssd_mobilenet_v1_coco <omz_models_model_ssd_mobilenet_v1_coco>
   ssd_mobilenet_v1_fpn_coco <omz_models_model_ssd_mobilenet_v1_fpn_coco>
   ssd-resnet34-1200-onnx <omz_models_model_ssd_resnet34_1200_onnx>
   ssdlite_mobilenet_v2 <omz_models_model_ssdlite_mobilenet_v2>
   swin-tiny-patch4-window7-224 <omz_models_model_swin_tiny_patch4_window7_224>
   t2t-vit-14 <omz_models_model_t2t_vit_14>
   text-recognition-resnet-fc <omz_models_model_text_recognition_resnet_fc>
   ultra-lightweight-face-detection-rfb-320 <omz_models_model_ultra_lightweight_face_detection_rfb_320>
   ultra-lightweight-face-detection-slim-320 <omz_models_model_ultra_lightweight_face_detection_slim_320>
   vehicle-license-plate-detection-barrier-0123 <omz_models_model_vehicle_license_plate_detection_barrier_0123>
   vehicle-reid-0001 <omz_models_model_vehicle_reid_0001>
   vitstr-small-patch16-224 <omz_models_model_vitstr_small_patch16_224>
   wav2vec2-base <omz_models_model_wav2vec2_base>
   wavernn (composite) <omz_models_model_wavernn>
   yolact-resnet50-fpn-pytorch <omz_models_model_yolact_resnet50_fpn_pytorch>
   yolo-v1-tiny-tf <omz_models_model_yolo_v1_tiny_tf>
   yolo-v2-tf <omz_models_model_yolo_v2_tf>
   yolo-v2-tiny-tf <omz_models_model_yolo_v2_tiny_tf>
   yolo-v3-onnx <omz_models_model_yolo_v3_onnx>
   yolo-v3-tf <omz_models_model_yolo_v3_tf>
   yolo-v3-tiny-onnx <omz_models_model_yolo_v3_tiny_onnx>
   yolo-v3-tiny-tf <omz_models_model_yolo_v3_tiny_tf>
   yolo-v4-tf <omz_models_model_yolo_v4_tf>
   yolo-v4-tiny-tf <omz_models_model_yolo_v4_tiny_tf>
   yolof <omz_models_model_yolof>
   yolox-tiny <omz_models_model_yolox_tiny>

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
AntiSpoofNet PyTorch* anti-spoof-mn3 3.81% 0.15 3.02
ConvNeXt Tiny PyTorch* convnext-tiny 82.05%/95.86% 8.9419 28.5892
DenseNet 121 densenet-121-tf 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 TensorFlow* googlenet-v1-tf 69.814%/89.6% 3.016~3.266 6.619~6.999
Inception (GoogleNet) V2 TensorFlow* googlenet-v2-tf 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-tf 71.03%/89.94% 1.148 4.221
MobileNet V2 1.0 224 TensorFlow*
PyTorch*		 mobilenet-v2-1.0-224
mobilenet-v2-pytorch		 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%/87.44% 0.1168 2.537
MobileNet V3 Large 1.0 TensorFlow* mobilenet-v3-large-1.0-224-tf 75.30%/92.62% 0.4450 5.4721
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
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-pytorchresnet-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
Shufflenet V2 x1.0 PyTorch* shufflenet-v2-x1.0 69.36%/88.32% 0.2957 2.2705
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

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
Erfnet PyTorch* erfnet 76.47% 11.13 7.87
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 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 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
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
Mobilenet-yolo-v4-syg Keras* mobilenet-yolo-v4-syg 86.35% 65.981 61.922
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
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 with MobileNet TensorFlow* ssd_mobilenet_v1_coco 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

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

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

See Also

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