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PET CV project. The application simulates the operation of smart scales, which themselves classify the product and offer options to the customer.

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AlekseyScorpi/fruits-vegetables-CV

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🍏 Python FRUITS-VEGETABLES SMART SCALES PET PROJECT

🚀 PET project, featuring:

  • 📸 YOLO (nano/small) models for detecting fruits and vegetables (YOLOv8)
  • 🖥️ Qt application (simulation of smart scales for automatic product detection)
  • 🗄️ Database (PostgreSQL in a Docker container)
    👉 Repository with the container: fruits-vegetables-cv-docker

Python YOLOv8 PostgreSQL


🛠 Installation Guide

1️⃣ Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/macOS
venv\Scripts\activate     # Windows

2️⃣ Install dependencies:

pip install -r requirements.txt

3️⃣ Configure the .env file:

Create a .env file and add the following parameters:

Variable Example Value Description
DEBUG TRUE or FALSE Debug mode
MODEL NANO or SMALL YOLO version
DATABASE_DBNAME smart_scales_db Database name
DATABASE_USERNAME smart_scales_user Database user
DATABASE_PASSWORD your_password Database password
DATABASE_HOST localhost or IP Database host
DATABASE_PORT 5432 (or 5433) Database port

🔹 Note:
The parameters DBNAME, USERNAME, and PASSWORD are set in the PostgreSQL container. Details can be found in the repository fruits-vegetables-cv-docker.


🚀 Running the Application

Simply execute the following command:

python main.py

🏆 About the Models

📖 More details about our models and the problem-solving approach can be found in our article:
📝 elibrary.ru/item.asp?id=80257050 (in Russian)

📂 Training logs and model weights 1-YOLOv8 (small), 2-YOLOv8 (nano), 3-YOLOv11 (nano), 4-YOLOv11 (small):
📎 Google Drive


✉ Contact

📧 Email: timoshin_aleksey02@mail.ru
🐙 GitHub: AlekseyScorpi

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PET CV project. The application simulates the operation of smart scales, which themselves classify the product and offer options to the customer.

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