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DeepStack_ExDark

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 https://docs.deepstack.cc

  • Download Custom Model: Download the trained custom model dark.pt 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\dark.pt

  • 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.

    E.g

    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 dark.pt 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 images/image.jpg of this repository

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

      pip install deepstack_sdk

    • Run the Python file detect.py in this repository.

      python detect.py

    • After the code runs, you will find a new image in images/image_detected.jpg 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 https://docs.deepstack.cc/custom-models-samples/ .

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