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From the basic recyclable materials, put them into the YOLOv8 model to detect and suggest products that can be made and then provide instructions to do so.

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Creative product recommendation from Recycled trash detection | Champion of GDSC Hackathon Vietnam 2024

This project is the submission for the GDSC Hackathon Vietnam 2024. The application is deployed using Streamlit. The app allows users to capture an image, perform object recognition, and calculate the total quantity of each recognized ingredient in the image. Subsequently, it suggests suitable items that can be created from the recognized materials. After selecting the desired item, the application provides detailed information including: ID, name, URL, difficulty level, danger level, and any surplus or deficient ingredients.

Introduction

Project Introduction Video

Brief introduction to the project. You can embed a video here showcasing the project in action or providing an overview.

Installation

Download pre-trained yolov8 here:

Usage

  1. Clone the repository:

    git clone https://github.com/H2NsayHi/Creative-product-recommendation-from-Recycled-trash-detection
  2. Navigate to the project directory:

    cd Creative-product-recommendation-from-Recycled-trash-detection
  3. Install the required dependencies:

    pip install -r requirements.txt
  4. Run the Streamlit application:

    streamlit run app.py
  5. Once the application is running, access it through your web browser at `http://localhost:8501

Contributing

License

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This project is licensed under the MIT License.

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From the basic recyclable materials, put them into the YOLOv8 model to detect and suggest products that can be made and then provide instructions to do so.

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