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Model Zoo

FEATURED
Mobile Object Localizer
YoloV6 Nano
MegaDepth
YOLOP
YoloV6 Tiny
YoloV6 Nano
Coneslayer YoloV7 Tiny
YoloV7 Tiny
YoloV5 Nano
FastDepth
YoloV4-tiny
Mask R-CNN
YoloV7
ready-to-use, open source models

Mobile Object Localizer

A class-agnostic mobile object detector.
Resolution
192x192x3
Task type
detection
FPS
41.02
Learn more

YoloV6 Nano

Real-time Object detection with YoloV6n pre-trained on COCO data set.
Resolution
416x416x3
Task type
detection
FPS
/
Learn more

MegaDepth

Estimate depth from a RGB image.
Resolution
256x192x3
Task type
monocular_depth_estimation
FPS
4.8
Learn more

YOLOP

You Only Look at Once for Panoptic driving Perception.
Resolution
320x320x3
Task type
detection
FPS
11.4
Learn more

YoloV6 Tiny

Real-time Object detection with YoloV6t pre-trained on COCO data set.
Resolution
416x416x3
Task type
detection
FPS
/
Learn more

YoloV6 Nano

Real-time Object detection with YoloV6n pre-trained on COCO data set.
Resolution
640x640x3
Task type
detection
FPS
/
Learn more

Coneslayer YoloV7 Tiny

A lightweight neural-network for rapid detection of orange traffic cones
Resolution
416x416x3
Task type
detection
FPS
30.0
Learn more

YoloV7 Tiny

Real-time Object detection with YoloV7-tiny pre-trained on COCO data set.
Resolution
416x416x3
Task type
detection
FPS
/
Learn more

YoloV5 Nano

Real-time Object detection with YoloV5n pre-trained on COCO data set.
Resolution
416x416x3
Task type
detection
FPS
/
Learn more

FastDepth

Estimate depth from RGB images using FastDepth from MIT.
Resolution
320x256x3
Task type
monocular_depth_estimation
FPS
40.32
Learn more

YoloV4-tiny

Real-time Object detection with YoloV4-tiny pre-trained on COCO data set.
Resolution
416x416x3
Task type
detection
FPS
39.8
Learn more

Mask R-CNN

Instance Segmentation with Mask R-CNN pre-trained on COCO data set.
Resolution
300x300x3
Task type
instance_segmentation
FPS
3.11
Learn more

YoloV7

Real-time Object detection with YoloV7 pre-trained on COCO data set.
Resolution
416x416x3
Task type
detection
FPS
/
Learn more

FastDepth

Estimate depth from RGB images using FastDepth from MIT.
Resolution
640x480x3
Task type
monocular_depth_estimation
FPS
???
Learn more

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
Learn more

YoloV3

Object detection with YoloV3 pre-trained on COCO data set.
Resolution
416x416x3
Task type
detection
FPS
4.16
Learn more

WeChat's QR code detection model

WeChat's QR code detection model
Resolution
384x384x3
Task type
detection
FPS
0
Learn more

Face-mask-detection

Face mask detection model, trained by the notebook available at depthai-ml-training repository.
Resolution
300x300x3
Task type
detection
FPS
/
Learn more

YoloV4

Object detection with YoloV4 pre-trained on COCO data set.
Resolution
608x608x3
Task type
detection
FPS
1.31
Learn more

DM-Count

Count dense or sparse crowds using density maps.
Resolution
960x540x3
Task type
feature_extraction
FPS
0.22
Learn more

Deeplab-V3+ person segmentation model

Deeplab-V3+ person segmentation model
Resolution
256x256x3
Task type
semantic_segmentation
FPS
0
Learn more

Deeplab-V3+ person segmentation model

Deeplab-V3+ person segmentation model
Resolution
513x513x3
Task type
semantic_segmentation
FPS
0
Learn more

SBD-mask classification

Face mask classification model
Resolution
224x224x3
Task type
classification
FPS
0
Learn more

YuNet face detection model

YuNet face detection model
Resolution
160x120x3
Task type
detection
FPS
0
Learn more

Face recognition MobileFaceNet ArcFace

Deep face recognition net with MobileFaceNet backbone and Arcface loss <https://arxiv.org/abs/1801.07698>
Resolution
112x112x3
Task type
face_recognition
FPS
0
Learn more

Facial landmarks 68 detection

Detect 68 facial landmarks.
Resolution
160x160x3
Task type
head_pose_estimation
FPS
0
Learn more

Depth MobileNetV2

Estimate depth from a RGB image.
Resolution
320x240x3
Task type
monocular_depth_estimation
FPS
20.03
Learn more

Image Quality Assesment Classification

Image quality assessment from RGB image using EdgeSegNet-Classifier.
Resolution
256x256x3
Task type
classification
FPS
13.65
Learn more

GhostNet

Image classification with GhostNet pretrained on ImageNet.
Resolution
256x320x3
Task type
classification
FPS
53.13
Learn more

Depth MobileNetV2

Depth Estimation of a given input image.
Resolution
640x480x3
Task type
monocular_depth_estimation
FPS
/
Learn more

SC-Depth

Depth estimation from a RGB image using SC-Depth model.
Resolution
512x256x3
Task type
monocular_depth_estimation
FPS
12.89
Learn more

EAST

Detect text on images using EAST model.
Resolution
256x256x3
Task type
detection
FPS
22.5
Learn more

PaDiM-Wood

PaDiM anomaly detection model trained to detect anomalies in wood
Resolution
256x256x3
Task type
detection
FPS
9.62
Learn more

MediaPipe Facemesh - 468 facial landmarks

MediaPipe Facemesh model that provides 468 facial landmarks
Resolution
192x192x3
Task type
object_attributes
FPS
/
Learn more

MicroNet-M0

Image classification with MicroNet-M0 pretrained on ImageNet.
Resolution
224x224x3
Task type
classification
FPS
34.36
Learn more

MobileNetV2

Feature extractor with MobileNetV2 pretrained on ImageNet.
Resolution
224x224x3
Task type
feature_extraction
FPS
47.27
Learn more

ShuffleNetV2

Image classification with ShuffleNetV2 pretrained on ImageNet.
Resolution
224x224x3
Task type
classification
FPS
151.72
Learn more

Mediapipe's Palm detection model

Mediapipe's Palm detection model
Resolution
128x128x3
Task type
detection
FPS
0
Learn more

HR-Depth

Depth estimation from RGB image using HR-Depth model.
Resolution
256x192x3
Task type
monocular_depth_estimation
FPS
4.8
Learn more

InceptionV4

Image classification with InceptionV4 pretrained on ImageNet.
Resolution
299x299x3
Task type
classification
FPS
8.02
Learn more

Mobile Object Localizer

No Depth
Description

A class-agnostic mobile object detector.

Input
  
    Name: normalized_input_image_tensor
    Type: image
    Shape: 3, 192, 192
FPS
  Mean: 41.02
  STD: 0.01
License
Apache-2.0 

YoloV6 Nano

No Depth
Description

Real-time Object detection with YoloV6n pre-trained on COCO data set.

Input
  
    Name: images
    Type: image
    Shape: 3, 416, 416
FPS
  Mean: /
  STD: /
License
GNU GPL-3 

MegaDepth

No Depth
Description

Estimate depth from a RGB image.

Input
  
    Name: input.1
    Type: image
    Shape: 3, 192, 256
FPS
  Mean: 4.8
  STD: 0.01
License
MIT 

YOLOP

No Depth
Description

You Only Look at Once for Panoptic driving Perception.

Input
  
    Name: images
    Type: image
    Shape: 3, 320, 320
FPS
  Mean: 11.4
  STD: 0.01
License
MIT 

YoloV6 Tiny

No Depth
Description

Real-time Object detection with YoloV6t pre-trained on COCO data set.

Input
  
    Name: images
    Type: image
    Shape: 3, 416, 416
FPS
  Mean: /
  STD: /
License
GNU GPL-3 

YoloV6 Nano

No Depth
Description

Real-time Object detection with YoloV6n pre-trained on COCO data set.

Input
  
    Name: images
    Type: image
    Shape: 3, 640, 640
FPS
  Mean: /
  STD: /
License
GNU GPL-3 

Coneslayer YoloV7 Tiny

No Depth
Description

A lightweight neural-network for rapid detection of orange traffic cones

Input
  
    Name: images
    Type: image
    Shape: 3, 416, 416
FPS
  Mean: 30.0
  STD: 0.01
License
GNU GPL-3 

YoloV7 Tiny

No Depth
Description

Real-time Object detection with YoloV7-tiny pre-trained on COCO data set.

Input
  
    Name: images
    Type: image
    Shape: 3, 416, 416
FPS
  Mean: /
  STD: /
License
GNU GPL-3 

YoloV5 Nano

No Depth
Description

Real-time Object detection with YoloV5n pre-trained on COCO data set.

Input
  
    Name: inputs
    Type: image
    Shape: 3, 416, 416
FPS
  Mean: /
  STD: /
License
GNU GPL-3 

FastDepth

No Depth
Description

Estimate depth from RGB images using FastDepth from MIT.

Input
  
    Name: input.1
    Type: image
    Shape: 3, 256, 320
FPS
  Mean: 40.32
  STD: 0.14
License
MIT 

YoloV4-tiny

No Depth
Description

Real-time Object detection with YoloV4-tiny pre-trained on COCO data set.

Input
  
    Name: inputs
    Type: image
    Shape: 3, 416, 416
FPS
  Mean: 39.8
  STD: 1.33
License
YOLO 

Mask R-CNN

No Depth
Description

Instance Segmentation with Mask R-CNN pre-trained on COCO data set.

Input
  
    Name: 960
    Type: image
    Shape: 3, 300, 300
FPS
  Mean: 3.11
  STD: 0.01
License
Apache-2.0 

YoloV7

No Depth
Description

Real-time Object detection with YoloV7 pre-trained on COCO data set.

Input
  
    Name: images
    Type: image
    Shape: 3, 416, 416
FPS
  Mean: /
  STD: /
License
GNU GPL-3 

FastDepth

No Depth
Description

Estimate depth from RGB images using FastDepth from MIT.

Input
  
    Name: input.1
    Type: image
    Shape: 3, 480, 640
FPS
  Mean: ???
  STD: ???
License
MIT 

person-reidentification-retail-0031

No Depth
Description

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.

Input
  
    Name: data
    Type: image
    Shape: 3, 96, 48
FPS
  Mean: 60
  STD: 0
License
Apache 

YoloV3

No Depth
Description

Object detection with YoloV3 pre-trained on COCO data set.

Input
  
    Name: inputs
    Type: image
    Shape: 3, 416, 416
FPS
  Mean: 4.16
  STD: 0.14
License
YOLO 

WeChat's QR code detection model

No Depth
Description

WeChat's QR code detection model

Input
  
    Name: input
    Type: image
    Shape: 3, 384, 384
FPS
  Mean: 0
  STD: 0
License
Apache License Version 2.0 

Face-mask-detection

No Depth
Description

Face mask detection model, trained by the notebook available at depthai-ml-training repository.

Input
  
    Name: image_tensor
    Type: image
    Shape: 3, 300, 300
FPS
  Mean: /
  STD: /
License
None 

YoloV4

No Depth
Description

Object detection with YoloV4 pre-trained on COCO data set.

Input
  
    Name: inputs
    Type: image
    Shape: 3, 608, 608
FPS
  Mean: 1.31
  STD: 0.01
License
YOLO 

DM-Count

No Depth
Description

Count dense or sparse crowds using density maps.

Input
  
    Name: input.1
    Type: image
    Shape: 3, 540, 960
FPS
  Mean: 0.22
  STD: 0.01
License
MIT 

Deeplab-V3+ person segmentation model

No Depth
Description

Deeplab-V3+ person segmentation model

Input
  
    Name: data
    Type: image
    Shape: 3, 256, 256
FPS
  Mean: 0
  STD: 0
License
MIT License 

Deeplab-V3+ person segmentation model

No Depth
Description

Deeplab-V3+ person segmentation model

Input
  
    Name: data
    Type: image
    Shape: 3, 513, 513
FPS
  Mean: 0
  STD: 0
License
MIT License 

SBD-mask classification

No Depth
Description

Face mask classification model

Input
  
    Name: 32:0
    Type: image
    Shape: 3, 224, 224
FPS
  Mean: 0
  STD: 0
License
None 

YuNet face detection model

No Depth
Description

YuNet face detection model

Input
  
    Name: data
    Type: image
    Shape: 3, 120, 160
FPS
  Mean: 0
  STD: 0
License
 

Face recognition MobileFaceNet ArcFace

No Depth
Description

Deep face recognition net with MobileFaceNet backbone and Arcface loss <https://arxiv.org/abs/1801.07698>

Input
  
    Name: data
    Type: image
    Shape: 3, 112, 112
FPS
  Mean: 0
  STD: 0
License
 

Facial landmarks 68 detection

No Depth
Description

Detect 68 facial landmarks.

Input
  
    Name: images
    Type: image
    Shape: 3, 160, 160
FPS
  Mean: 0
  STD: 0
License
Apache-2.0 License 

Depth MobileNetV2

No Depth
Description

Estimate depth from a RGB image.

Input
  
    Name: input.1
    Type: image
    Shape: 3, 240, 320
FPS
  Mean: 20.03
  STD: 0.7
License
MIT 

Image Quality Assesment Classification

No Depth
Description

Image quality assessment from RGB image using EdgeSegNet-Classifier.

Input
  
    Name: input.1
    Type: image
    Shape: 3, 256, 256
FPS
  Mean: 13.65
  STD: 2.73
License
MIT 

GhostNet

No Depth
Description

Image classification with GhostNet pretrained on ImageNet.

Input
  
    Name: input.1
    Type: image
    Shape: 3, 320, 256
FPS
  Mean: 53.13
  STD: 0.92
License
None 

Depth MobileNetV2

No Depth
Description

Depth Estimation of a given input image.

Input
  
    Name: input.1
    Type: image
    Shape: 3, 480, 640
FPS
  Mean: /
  STD: /
License
MIT 

SC-Depth

No Depth
Description

Depth estimation from a RGB image using SC-Depth model.

Input
  
    Name: input.1
    Type: image
    Shape: 3, 256, 512
FPS
  Mean: 12.89
  STD: 0.33
License
GPL-3.0 

EAST

No Depth
Description

Detect text on images using EAST model.

Input
  
    Name: input_images
    Type: image
    Shape: 3, 256, 256
FPS
  Mean: 22.5
  STD: 0.22
License
GPL-3.0 

PaDiM-Wood

No Depth
Description

PaDiM anomaly detection model trained to detect anomalies in wood

Input
  
    Name: input.1
    Type: image
    Shape: 3, 256, 256
FPS
  Mean: 9.62
  STD: 0.03
License
Apache 2.0 

MediaPipe Facemesh - 468 facial landmarks

No Depth
Description

MediaPipe Facemesh model that provides 468 facial landmarks

Input
  
    Name: images
    Type: image
    Shape: 3, 192, 192
FPS
  Mean: /
  STD: /
License
Apache License Version 2.0 

MicroNet-M0

No Depth
Description

Image classification with MicroNet-M0 pretrained on ImageNet.

Input
  
    Name: input.1
    Type: image
    Shape: 3, 224, 224
FPS
  Mean: 34.36
  STD: 0.04
License
MIT 

MobileNetV2

No Depth
Description

Feature extractor with MobileNetV2 pretrained on ImageNet.

Input
  
    Name: input.1
    Type: image
    Shape: 3, 224, 224
FPS
  Mean: 47.27
  STD: 0.03
License
BSD-3 

ShuffleNetV2

No Depth
Description

Image classification with ShuffleNetV2 pretrained on ImageNet.

Input
  
    Name: input.1
    Type: image
    Shape: 3, 224, 224
FPS
  Mean: 151.72
  STD: 2.55
License
BSD-3 

Mediapipe's Palm detection model

No Depth
Description

Mediapipe's Palm detection model

Input
  
    Name: images
    Type: image
    Shape: 3, 128, 128
FPS
  Mean: 0
  STD: 0
License
Apache-2.0 License 

HR-Depth

No Depth
Description

Depth estimation from RGB image using HR-Depth model.

Input
  
    Name: input.1
    Type: image
    Shape: 3, 192, 256
FPS
  Mean: 4.8
  STD: 0.01
License
MIT 

InceptionV4

No Depth
Description

Image classification with InceptionV4 pretrained on ImageNet.

Input
  
    Name: input
    Type: image
    Shape: 3, 299, 299
FPS
  Mean: 8.02
  STD: 0.03
License
Apache-2.0