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This a No-Code ObjectDetection Toolbox using Yolov5 and Detectron2 with Streamlit frontend

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NikhilSKashyap/ObjectDetectionToolbox

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ObjectDetectionToolbox

  1. Clone Detectron2 repo
    git clone https://github.com/facebookresearch/detectron2.git

  2. Git clone Yolov5
    git clone https://github.com/ultralytics/yolov5.git

  3. Create virtual environment and activate
    pip install virtualenv
    virtualenv venv
    source venv/bin/activate

  4. Install required packages
    pip install -r requirements.txt

  5. To run FastAPI server execute the following command in the terminal
    uvicorn main:app --reload --workers 1 --host 0.0.0.0 --port 8008

  6. Start the Streamlit server
    streamlit run app.py

Open this link http://localhost:8501 in your favorite browser to acess Streamlit frontend.

  1. As you can see, the below image is the landing page. image

  2. You can select the model from the left sidebar. Let's select Detectron2. image

  3. Input an image, choose a base_model and set the threshold score and click infer. image

  4. You can see the objects detected by Detectron2 in the output. You can also download the output image. image

  5. Similarly, you can choose Yolov5 from the sidebar and input the values. Click detect. image

  6. You can see the objects detected by Detectron2 in the output. You can also download the output image. image

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This a No-Code ObjectDetection Toolbox using Yolov5 and Detectron2 with Streamlit frontend

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