This repository provides a custom DeepStack model that has been trained and can be used for creating a new object detection API for detecting 12 common objects (including people) in the dark/night images and videos. The Model was trained on the ExDark dataset dataset.

  • Create API and Detect Objects
  • Discover more Custom Models
  • Train your own Model

Create API and Detect Objects

The Trained Model can detect the following objects in dark/night images and videos.

  • Bicycle
  • Boat
  • Bottle
  • Bus
  • Chair
  • Car
  • Cat
  • Cup
  • Dog
  • Motorbike
  • People
  • Table

To start detecting, follow the steps below

  • Install DeepStack: Install DeepStack AI Server with instructions on DeepStack's documentation via

  • Download Custom Model: Download the trained custom model for ExDark from this GitHub release. Create a folder on your machine and move the downloaded model to this folder.

    E.g A path on Windows Machine C\Users\MyUser\Documents\DeepStack-Models, which will make your model file path C\Users\MyUser\Documents\DeepStack-Models\

  • Run DeepStack: To run DeepStack AI Server with the custom ExDark model, run the command that applies to your machine as detailed on DeepStack's documentation linked here.


    For a Windows version, you run the command below

    deepstack --MODELSTORE-DETECTION "C\Users\MyUser\Documents\DeepStack-Models" --PORT 80

    For a Linux machine

    sudo docker run -v /home/MyUser/Documents/DeepStack-Models:/modelstore/detection -p 80:5000 deepquestai/deepstack

    Once DeepStack runs, you will see a log like the one below in your Terminal/Console

    That means DeepStack is running your custom model and now ready to start detecting objects in night/dark images via the API endpoint http://localhost:80/v1/vision/custom/dark or http://your_machine_ip:80/v1/vision/custom/dark

  • Detect Objects in night image: You can detect objects in an image by sending a POST request to the url mentioned above with the paramater image set to an image using any proggramming language or with a tool like POSTMAN. For the purpose of this repository, we have provided a sample Python code below.

    • A sample image can be found in of this repository

    • Install Python and install the DeepStack Python SDK via the command below

      pip install deepstack_sdk
    • Run the Python file in this repository.

    • After the code runs, you will find a new image in with the detection visualized, with the following results printed in the Terminal/Console.

      Name: People
      Confidence: 0.74210495
      x_min: 616
      x_max: 672
      y_min: 224
      y_max: 323
      Name: Dog
      Confidence: 0.82523036
      x_min: 250
      x_max: 327
      y_min: 288
      y_max: 349
      Name: Dog
      Confidence: 0.86660975
      x_min: 403
      x_max: 485
      y_min: 283
      y_max: 341
      Name: Dog
      Confidence: 0.87793124
      x_min: 508
      x_max: 609
      y_min: 309
      y_max: 370
      Name: Dog
      Confidence: 0.89132285
      x_min: 286
      x_max: 372
      y_min: 316
      y_max: 393

    • You can try running detection for other night/dark images.

Discover more Custom Models

For more custom DeepStack models that has been trained and ready to use, visit the Custom Models sample page on DeepStack's documentation .

Train your own Model

If you will like to train a custom model yourself, follow the instructions below.

  • Prepare and Annotate: Collect images on and annotate object(s) you plan to detect as detailed here
  • Train your Model: Train the model as detailed here