Object Detection: Using a custom model

Object Detection: Using a custom model

The Object Detection Node allows to connect to your own custom model. At first, you need to configure the object detection node in your flow to use a custom model. You will do that by checking the Custom checkbox.

The custom model will be indicated with a downloadable URL (after the “Custom” checkbox is checked). Here are the requirements of the uploaded customized model:

  • The downloadable link should actually be a tar.xz file that includes the model files and the corresponding label file.

  • The model’s root folder should be matched with the model_name (the file name of the model).

    • Tensorflow V1(CPU, NVIDIA GPU)
      custom_model/custom_model.pb
      custom_model/custom_model.txt

    • Tensorflow V2(CPU, NVIDIA GPU)
      custom_model/saved_model/custom_model.pb or custom_model/saved_model/saved_model.pb
      custom_model/custom_model.txt

    • OpenVINO(MYRIAD)
      custom_model/custom_model.xml
      custom_model/custom_model.bin
      custom_model/custom_model.txt

    • Tflite(Google TPU)
      custom_model/custom_model.tflite
      custom_model/custom_model.txt

  • The model folder should be compressed with format tar.xz.


    • Related Articles

    • Overview Object Detection Node

      The Object Detection Node is used to detect several different objects off the shelf with pre-trained or custom deep learning models on GPU, CPU, VPU (Intel Movidius Myriad X) or TPU (Google Coral). Input and Output Input: Frame from a video file, IP ...

    • Build your own custom module, node and container

      All application logic and services used on edge devices are encapsulated in Docker containers. To configure these containers on Viso Suite, the visual programming interface called Viso Builder is used. Viso Builder is built on top of Node-RED, which ...

    • Overview Object Tracking Node

      The Object Tracking Node is used to track several objects which were previously detected by the object detection node across several frames.  Input and Output Input: Output of object detection node. Output: MQTT message containing the tracking ...

    • Object Detection: Available Models

      Available Models While the Object Detection module allows you to link your own private and customized models for object detection, we continuously test, evaluate and add available off-the-shelf models to be used immediately. Currently, we have added ...

    • Overview Object Segmentation Node

      The Object Segmentation Node is used to segment several different objects off the shelf with pre-trained or custom deep learning models on GPU, CPU, VPU (Myriad) and TPU. Input and Output Input: Frame from a video file, IP or USB camera. Output: MQTT ...