DeepStack_FireNET

This repository provides a custom DeepStack model that has been trained and can be used for creating a new object detection API for detecting fire present indoor and outdoor using FireNET Dataset. Also included in this repository is that dataset with the YOLO annotations.

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

Download DeepStack Model and Dataset

You can download the pre-trained DeepStack_FireNET model and the annotated dataset via the links below.

• YOLOv5x DeepStack Model

• FireNET with YOLO annotation

Create API and Detect Fire

The Trained Model can detect fire in images and videos.

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 firenetv1.pt 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\firenetv1.pt

• Run DeepStack: To run DeepStack AI Server with the custom FireNET 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 firenet.pt model and now ready to start detecting fire images via the API endpoint http://localhost:80/v1/vision/custom/firenet or http://your_machine_ip:80/v1/vision/custom/firenet

• Detect fire in 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/test.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/test_detected.jpg with the detection visualized, with the following results printed in the Terminal/Console.

    Name: fire, Confidence: 0.92534935, x_min: 607, y_min: 348, x_max: 797, y_max: 530
    

  

• Fire detection sample 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