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DeepStack_OpenLogo

This repository provides a custom DeepStack model that has been trained and can be used for creating a new object detection API for detecting all the 352 different logos in the OpenLogo QMUL-OpenLogo dataset. It also comes with details on training the model. Find more details below.

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

Create API and Detect Logos

All the logos the model provided in this repository can detect are in the openlogo_classes.txt file.

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 openlogo.pt for OpenLogo via this link. Create a folder on your machine and move the 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\openlogo.pt

• Run DeepStack: To run DeepStack AI Server with the custom openlogo 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 -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 openlogo model and now ready to start detecting logos in images via the API enpoint http://localhost:80/v1/vision/custom/openlogo or http://your_machine_ip:80/v1/vision/custom/openlogo

• Detect Logo in image: You can detect logos 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/fedex.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/fedex_new.jpg with the detection visualized, with the following results printed in the Terminal/Console.

    Name: fedex
    Confidence: 0.83026457
    x_min: 385
    x_max: 524
    y_min: 135
    y_max: 183
    -----------------------
    Name: fedex
    Confidence: 0.8716001
    x_min: 278
    x_max: 352
    y_min: 138
    y_max: 226
    -----------------------
    

    

  • You can try running detection for other images that contains the objects listed in the OpenLogo datasets.

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