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Cold Drinks Detection & Counting

Overview

An AI-powered object detection system that identifies and counts cold drink products from images using YOLOv8 and a Django web interface.

Retail shops, warehouses, and inventory teams often need to count different cold drink products from shelves, images, or stored stock visuals. Manual counting can take time and may lead to mistakes, especially when multiple drink types are placed together.

This project solves that problem by building a YOLOv8-based cold drinks detection and counting system integrated with a Django web application. Users can upload images, and the system automatically detects cold drink items, identifies different types, and counts them accurately.

The platform provides a simple interface for uploading images, viewing detection results, and tracking drink counts. It is optimized for high-precision detection in different environments, making it useful for inventory checking, retail monitoring, and product counting automation.

Key benefits

  1. Detects cold drink products automatically from images
  2. Counts different drink types in real time
  3. Uses YOLOv8 for accurate object detection
  4. Easy Django-based upload and result interface
  5. Helps reduce manual counting errors in retail or inventory tasks

What's included

  1. YOLOv8-based cold drink detection model
  2. Image upload and result viewing system
  3. Automatic product counting feature
  4. OpenCV-based image processing workflow
  5. Django web application interface

Use Cases

  1. Retail product counting
  2. Cold drink inventory checking
  3. Shelf monitoring system
  4. Image-based object detection
  5. Product recognition automation

Key deliverables

  1. Cold drink detection system
  2. YOLOv8 object detection model
  3. Automatic counting workflow
  4. Django web application
  5. OpenCV image processing pipeline
  6. User-friendly result display interface
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