Overview of OpenVINO™ Toolkit Intel’s Pre-Trained Models¶

OpenVINO™ toolkit provides a set of Intel’s 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 Intel’s Pre-Trained Models Device Support summarizes devices supported by each model.

The models can be downloaded via Model Downloader.

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. Networks that detect the same types of objects (for example, face-detection-adas-0001 and face-detection-retail-0004) provide a choice for higher accuracy/wider applicability at the cost of slower performance, so you can expect a “bigger” network to detect objects of the same type better.

Model Name	Complexity (GFLOPs)	Size (Mp)
faster-rcnn-resnet101-coco-sparse-60-0001	364.21	52.79
face-detection-adas-0001	2.835	1.053
face-detection-retail-0004	1.067	0.588
face-detection-retail-0005	0.982	1.021
face-detection-0200	0.785	1.828
face-detection-0202	1.767	1.842
face-detection-0204	2.405	1.851
face-detection-0205	2.853	2.392
face-detection-0206	339.597	69.920
person-detection-retail-0002	12.427	3.244
person-detection-retail-0013	2.300	0.723
person-detection-action-recognition-0005	7.140	1.951
person-detection-action-recognition-0006	8.225	2.001
person-detection-action-recognition-teacher-0002	7.140	1.951
person-detection-raisinghand-recognition-0001	7.138	1.951
person-detection-0200	0.786	1.817
person-detection-0201	1.768	1.817
person-detection-0202	3.143	1.817
person-detection-0203	6.519	2.394
person-detection-0301	79318.2158	55.557
person-detection-0302	370.208	51.164
person-detection-0303	24.758	3.630
person-detection-0106	404.264	71.565
pedestrian-detection-adas-0002	2.836	1.165
pedestrian-and-vehicle-detector-adas-0001	3.974	1.650
vehicle-detection-adas-0002	2.798	1.079
vehicle-detection-0200	0.786	1.817
vehicle-detection-0201	1.768	1.817
vehicle-detection-0202	3.143	1.817
person-vehicle-bike-detection-crossroad-0078	3.964	1.178
person-vehicle-bike-detection-crossroad-1016	3.560	2.887
person-vehicle-bike-detection-crossroad-yolov3-1020	65.984	61.922
person-vehicle-bike-detection-2000	0.787	1.821
person-vehicle-bike-detection-2001	1.770	1.821
person-vehicle-bike-detection-2002	3.163	1.821
person-vehicle-bike-detection-2003	6.550	2.416
person-vehicle-bike-detection-2004	1.811	2.327
vehicle-license-plate-detection-barrier-0106	0.349	0.634
product-detection-0001	3.598	3.212
person-detection-asl-0001	0.986	1.338
yolo-v2-ava-0001	29.38	48.29
yolo-v2-ava-sparse-35-0001	29.38	48.29
yolo-v2-ava-sparse-70-0001	29.38	48.29
yolo-v2-tiny-ava-0001	6.975	15.12
yolo-v2-tiny-ava-sparse-30-0001	6.975	15.12
yolo-v2-tiny-ava-sparse-60-0001	6.975	15.12
yolo-v2-tiny-vehicle-detection-0001	5.424	11.229
smartlab-object-detection-0001	1.077	0.8908
smartlab-object-detection-0002	1.073	0.8894
smartlab-object-detection-0003	1.077	0.8908
smartlab-object-detection-0004	1.073	0.8894

Object Recognition Models¶

Object recognition models are used for classification, regression, and character recognition. Use these networks after a respective detector (for example, Age/Gender recognition after Face Detection).

Model Name	Complexity (GFLOPs)	Size (Mp)
age-gender-recognition-retail-0013	0.094	2.138
head-pose-estimation-adas-0001	0.105	1.911
license-plate-recognition-barrier-0001	0.328	1.218
vehicle-attributes-recognition-barrier-0039	0.126	0.626
vehicle-attributes-recognition-barrier-0042	0.462	11.177
emotions-recognition-retail-0003	0.126	2.483
landmarks-regression-retail-0009	0.021	0.191
facial-landmarks-98-detection-0001	0.6	9.66
facial-landmarks-35-adas-0002	0.042	4.595
person-attributes-recognition-crossroad-0230	0.174	0.735
person-attributes-recognition-crossroad-0234	2.167	23.510
person-attributes-recognition-crossroad-0238	1.034	21.797
gaze-estimation-adas-0002	0.139	1.882

Reidentification Models¶

Precise tracking of objects in a video is a common application of Computer Vision (for example, for people counting). It is often complicated by a set of events that can be described as a “relatively long absence of an object”. For example, it can be caused by occlusion or out-of-frame movement. In such cases, it is better to recognize the object as “seen before” regardless of its current position in an image or the amount of time passed since last known position.

The following networks can be used in such scenarios. They take an image of a person and evaluate an embedding - a vector in high-dimensional space that represents an appearance of this person. This vector can be used for further evaluation: images that correspond to the same person will have embedding vectors that are “close” by L2 metric (Euclidean distance).

There are multiple models that provide various trade-offs between performance and accuracy (expect a bigger model to perform better).

Model Name	Complexity (GFLOPs)	Size (Mp)
face-reidentification-retail-0095	0.588	1.107
person-reidentification-retail-0288	0.174	0.183
person-reidentification-retail-0287	0.564	0.595
person-reidentification-retail-0286	1.170	1.234
person-reidentification-retail-0277	1.993	2.103

Semantic 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 (for example, free space on the road).

Model Name	Complexity (GFLOPs)	Size (Mp)
road-segmentation-adas-0001	4.770	0.184
semantic-segmentation-adas-0001	58.572	6.686
unet-camvid-onnx-0001	260.1	31.03
icnet-camvid-ava-0001	151.82	25.45
icnet-camvid-ava-sparse-30-0001	151.82	25.45
icnet-camvid-ava-sparse-60-0001	151.82	25.45

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	Complexity (GFLOPs)	Size (Mp)
instance-segmentation-security-0002	423.0842	48.3732
instance-segmentation-security-0091	828.6324	101.236
instance-segmentation-security-0228	147.2352	49.8328
instance-segmentation-security-1039	13.9672	10.5674
instance-segmentation-security-1040	29.334	13.5673
instance-segmentation-person-0007	4.8492	7.2996

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	Complexity (GFLOPs)	Size (Mp)
human-pose-estimation-0001	15.435	4.099
human-pose-estimation-0005	5.9393	8.1504
human-pose-estimation-0006	8.8720	8.1504
human-pose-estimation-0007	14.3707	8.1504

Image Processing¶

Deep Learning models find their application in various image processing tasks to increase the quality of the output.

Model Name	Complexity (GFLOPs)	Size (Mp)
single-image-super-resolution-1032	11.654	0.030
single-image-super-resolution-1033	30.97	16.062
text-image-super-resolution-0001	1.379	0.003

Text Detection¶

Deep Learning models for text detection in various applications.

Model Name	Complexity (GFLOPs)	Size (Mp)
text-detection-0003	51.256	6.747
text-detection-0004	23.305	4.328
horizontal-text-detection-0001	7.718	2.259

Text Recognition¶

Deep Learning models for text recognition in various applications.

Model Name	Complexity (GFLOPs)	Size (Mp)
text-recognition-0012	1.485	5.568
text-recognition-0014	0.5442	2.839
text-recognition-0015
encoder	12.4	398
decoder	0.03	4.33
text-recognition-0016
encoder	9.27	88.1
decoder	0.08	4.28
handwritten-score-recognition-0003	0.792	5.555
handwritten-japanese-recognition-0001	117.136	15.31
handwritten-simplified-chinese-recognition-0001	134.513	17.270
handwritten-english-recognition-0001	1.3182	0.1413
formula-recognition-medium-scan-0001
encoder	16.56	1.86
decoder	1.69	2.56
formula-recognition-polynomials-handwritten-0001
encoder	12.8447	0.2017
decoder	8.6838	2.5449

Text Spotting¶

Deep Learning models for text spotting (simultaneous detection and recognition).

Model Name	Complexity (GFLOPs)	Size (Mp)
text-spotting-0005
text-spotting-0005-detector	184.495	27.010
text-spotting-0005-recognizer-encoder	2.082	1.328
text-spotting-0005-recognizer-decoder	0.002	0.273

Action Recognition Models¶

Action Recognition models predict action that is being performed on a short video clip (tensor formed by stacking sampled frames from input video). Some models (for example driver-action-recognition-adas-0002 may use precomputed high-level spatial or spatio-temporal) features (embeddings) from individual clip fragments and then aggregate them in a temporal model to predict a vector with classification scores. Models that compute embeddings are called encoder, while models that predict an actual labels are called decoder.

Model Name	Complexity (GFLOPs)	Size (Mp)
driver-action-recognition-adas-0002
driver-action-recognition-adas-0002-encoder	0.676	2.863
driver-action-recognition-adas-0002-decoder	0.147	4.205
action-recognition-0001
action-recognition-0001-encoder	7.340	21.276
action-recognition-0001-decoder	0.147	4.405
asl-recognition-0004	6.660	4.133
common-sign-language-0002	4.227	4.113
weld-porosity-detection-0001	3.636	11.173

Image Retrieval¶

Deep Learning models for image retrieval (ranking ‘gallery’ images according to their similarity to some ‘probe’ image).

Model Name	Complexity (GFLOPs)	Size (Mp)
image-retrieval-0001	0.613	2.535

Compressed models¶

Deep Learning compressed models

Model Name	Complexity (GFLOPs)	Size (Mp)
resnet50-binary-0001	1.002	7.446
resnet18-xnor-binary-onnx-0001	-	-

Question Answering¶

Model Name	Complexity (GFLOPs)	Size (Mp)
bert-large-uncased-whole-word-masking-squad-0001	246.93	333.96
bert-large-uncased-whole-word-masking-squad-int8-0001	246.93	333.96
bert-large-uncased-whole-word-masking-squad-emb-0001	246.93 (for [1,384] input size)	333.96
bert-small-uncased-whole-word-masking-squad-0001	23.9	57.94
bert-small-uncased-whole-word-masking-squad-0002	23.9	41.1
bert-small-uncased-whole-word-masking-squad-int8-0002	23.9	41.1
bert-small-uncased-whole-word-masking-squad-emb-int8-0001	23.9 (for [1,384] input size)	41.1

Machine Translation¶

Model Name	Complexity (GFLOPs)	Size (Mp)
machine-translation-nar-en-ru-0002	23.17	69.29
machine-translation-nar-ru-en-0002	23.17	69.29
machine-translation-nar-en-de-0002	23.19	77.47
machine-translation-nar-de-en-0002	23.19	77.47

Text To Speech¶

Deep Learning models for speech synthesis (mel spectrogram generation and wave form generation).

Model Name	Complexity (GFLOPs)	Size (Mp)
text-to-speech-en-0001
text-to-speech-en-0001-duration-prediction	15.84	13.569
text-to-speech-en-0001-regression	7.65	4.96
text-to-speech-en-0001-generation	48.38	12.77