Intel® Distribution of OpenVINO™ Toolkit
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Which model to use and where to download dataset



I want to test the person-vehicle-bike-detection-crossroad on both: my Raspberry Pi 4 (4Gb) with an Intel Neural Compute Stick 2 (NCS2) and on my Ubuntu 20.x in my WSL (I successfully setup the OpenVINO toolkit on both devices).  However, I see several types of this Pre-Trained Models which are person-vehicle-bike-detection-crossroad-2000, ...2001, 2002, 2003, 2004, 0078, 1016 and 1020. I like the person-vehicle-bike-detection-crossroad-1016 ( only because I can better understand the Specification values than the other ones but, I am not sure this is the best one to use. I am also struggling on finding the correct dataset and images for -any of Intel's Pre-trained models. I am following this page ( but it is not clear to me where to download the respective dataset. However, I downloaded the "2017 Val images and 2017 Train/Val annotations" files from this OpenVINO page ( but I can't tell if these files are the correct ones for the person-vehicle-bike-detection-crossroad-1016 pre-trained model.

So I have the following two Questions:

  1. Which pre-trained model should I use in my Pi4 with an NCS2 (and why)?
  2. Where can I find the right dataset (annotations and images) for every dataset (particularly, the recommended intel's pre-trained model for my Pi4)?

Thank you and will be waiting for your reply,

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4 Replies

Hi RGVGreatCoder,

Thanks for reaching out to us.


To answer your first question, the Inference Engine MYRIAD plugin has been developed for inference of neural networks on Intel® Neural Compute Stick 2 (Intel® NCS2).


First, to configure your Intel® NCS2, please execute the following command:

sudo usermod -a -G users "$(whoami)"

source /opt/intel/openvino_2021/bin/

sh /opt/intel/openvino_2021/install_dependencies/


For more information, please refer to Add USB Rules for an Intel® Neural Compute Stick 2 device.


Next, OpenVINO™ Toolkit provides a set of Intel’s Pre-Trained Models and Public Pre-Trained Models that you can use for learning and demo purposes or for developing deep learning software.


Please refer to Intel’s Pre-Trained Models Device Support and Public Pre-Trained Models Device Support and choose any model that is supported by MYRIAD plugin to use with Intel® NCS2.


For example, person-vehicle-bike-detection-crossroad-1016 is supported by MYRIAD plugin and it can be used with Intel® NCS2. However, resnet18-xnor-binary-onnx-0001 is not supported by MYRIAD plugin, hence it cannot be used with Intel® NCS2.



For your second question, you don’t have to get the dataset (annotations and images) to run inference in your Raspberry Pi with Intel® NCS2 or WSL2.


If you would like to obtain sample media files that are available for the demo application, you can use the images and videos from


Please get back to us shall you face any issues.







Thank you for your reply.

Can you please provide me a link with a Python sample code where I can infer the person-vehicle-bike-detection-crossroad-1016 model to detect an object in a picture and in a video?


Hi RGVGreatCoder,


You can use Single Human Pose Estimation Demo, a Python demo from Open Model Zoo that showcases top-down pipeline for human pose estimation on video or image.


You can run the following command to do inference on a CPU:

python3 -d CPU --model_od <path_to_model>/mobilenet-ssd.xml --model_hpe <path_to_model>/ person-vehicle-bike-detection-crossroad-1016.xml --input <path_to_video_or_image>


Supported models for this demo application are shown as follows:

  • mobilenet-ssd
  • pedestrian-and-vehicle-detector-adas-0001
  • pedestrian-detection-adas-0002
  • person-detection-retail-0013
  • person-vehicle-bike-detection-crossroad-0078
  • person-vehicle-bike-detection-crossroad-1016
  • ssd300
  • ssd512
  • ssd_mobilenet_v1_coco
  • ssd_mobilenet_v1_fpn_coco
  • ssd_mobilenet_v2_coco
  • ssdlite_mobilenet_v2
  • single-human-pose-estimation-0001







Hi RGVGreatCoder,


This thread will no longer be monitored since we have provided a suggestion. 

If you need any additional information from Intel, please submit a new question.