Model Zoo
FEATURED
YoloV5 Nano
YoloV4-tiny
Mobile Object Localizer
Mask R-CNN
MegaDepth
YOLOP
FastDepth
ready-to-use, open source models
YoloV5 Nano
Real-time Object detection with YoloV5n pre-trained on COCO data set.
Resolution
416x416x3
Task type
detection
FPS
/
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YoloV4-tiny
Real-time Object detection with YoloV4-tiny pre-trained on COCO data set.
Resolution
416x416x3
Task type
detection
FPS
39.8
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Mobile Object Localizer
A class-agnostic mobile object detector.
Resolution
192x192x3
Task type
detection
FPS
41.02
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Mask R-CNN
Instance Segmentation with Mask R-CNN pre-trained on COCO data set.
Resolution
300x300x3
Task type
instance_segmentation
FPS
3.11
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MegaDepth
Estimate depth from a RGB image.
Resolution
256x192x3
Task type
monocular_depth_estimation
FPS
4.8
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YOLOP
You Only Look at Once for Panoptic driving Perception.
Resolution
320x320x3
Task type
detection
FPS
11.4
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FastDepth
Estimate depth from RGB images using FastDepth from MIT.
Resolution
320x256x3
Task type
monocular_depth_estimation
FPS
40.32
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person-reidentification-retail-0031
This is a person reidentification model for a general scenario. It uses a whole body image as an input and outputs an embedding vector to match a pair of images by the cosine distance. The model is based on the RMNet backbone developed for fast inference. A single reidentification head from the 1/16 scale feature map outputs an embedding vector of 256 floats.
Resolution
48x96x3
Task type
named_entity_recognition
FPS
60
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YoloV4
Object detection with YoloV4 pre-trained on COCO data set.
Resolution
608x608x3
Task type
detection
FPS
1.31
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YoloV3
Object detection with YoloV3 pre-trained on COCO data set.
Resolution
416x416x3
Task type
detection
FPS
4.16
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DM-Count
Count dense or sparse crowds using density maps.
Resolution
960x540x3
Task type
feature_extraction
FPS
0.22
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FastDepth
Estimate depth from RGB images using FastDepth from MIT.
Resolution
640x480x3
Task type
monocular_depth_estimation
FPS
???
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Facial landmarks 68 detection
Detect 68 facial landmarks.
Resolution
160x160x3
Task type
head_pose_estimation
FPS
0
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Depth MobileNetV2
Estimate depth from a RGB image.
Resolution
320x240x3
Task type
monocular_depth_estimation
FPS
20.03
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HR-Depth
Depth estimation from RGB image using HR-Depth model.
Resolution
256x192x3
Task type
monocular_depth_estimation
FPS
4.8
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Image Quality Assesment Classification
Image quality assessment from RGB image using EdgeSegNet-Classifier.
Resolution
256x256x3
Task type
classification
FPS
13.65
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SC-Depth
Depth estimation from a RGB image using SC-Depth model.
Resolution
512x256x3
Task type
monocular_depth_estimation
FPS
12.89
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EAST
Detect text on images using EAST model.
Resolution
256x256x3
Task type
detection
FPS
22.5
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Depth MobileNetV2
Depth Estimation of a given input image.
Resolution
640x480x3
Task type
monocular_depth_estimation
FPS
/
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MicroNet-M0
Image classification with MicroNet-M0 pretrained on ImageNet.
Resolution
224x224x3
Task type
classification
FPS
34.36
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InceptionV4
Image classification with InceptionV4 pretrained on ImageNet.
Resolution
299x299x3
Task type
classification
FPS
8.02
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ShuffleNetV2
Image classification with ShuffleNetV2 pretrained on ImageNet.
Resolution
224x224x3
Task type
classification
FPS
151.72
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GhostNet
Image classification with GhostNet pretrained on ImageNet.
Resolution
256x320x3
Task type
classification
FPS
53.13