The supervised plastic detection model uses customized Yolov5 object detection model that obtains the input from raspberry pi camera and then object detection is processed using a python code which is then directed to a website that displays the output of the captured image.

(back to top)

DETECTION RESULT : pick-the-plastic

• Python
• Yolov5

(back to top)

We need raspberry pi 3 or higher version along the pi OS

how to install them

• Pi OS

  [OS] https://www.raspberrypi.com/software/

• Google Colaboratory

Below is an example of how you can instruct your audience on installing and setting up your app. This template doesn't rely on any external dependencies or services.

1. clone the repository

git clone https://github.com/ultralytics/yolov5

2. Install the requirements

   cd yolov5
   pip install -qr requirements.txt

(back to top)

1. Create a new google colab

2. Upload the data and labelling data set and unzip it using the following code.

   !zip -r /content/yolov5/train_data.zip /content/yolov5/train_data
   !unzip -q /content/yolov5/train_data.zip -d ../

3. Replace the custom.yaml file with the coco128.yaml

4. Exexute the python script in google colab and place the output of runs folder into the raspberry pi