Filled Ready bottles Counter By Yolo Model

This project involves building a machine learning model using YOLO (You Only Look Once) to count filled ready bottles on a production line. The model detects and counts bottles in real-time, helping to ensure accurate inventory management and quality control.

Project Objective

The objective of this project is to accurately count filled ready bottles on a production line using a YOLO model. This helps in maintaining an accurate count of inventory and ensures that the production process is running smoothly.

Steps Involved:

Data Preparation:

• Collect images of filled bottles on the production line.
• Annotate the images to mark the filled bottles for training the YOLO model.
• Split the annotated data into training and validation sets.

Model Selection:

• Selected the YOLO model for its efficiency in real-time object detection.
• Configured the model with appropriate parameters for detecting filled bottles.

Model Training:

• Trained the YOLO model on the annotated dataset of filled bottles.
• Validated the model's performance on the validation set and fine-tuned it for better accuracy.

Evaluation:

• Assessed the model's performance using metrics like Precision, Recall, and F1-Score.
• Visualized the detection results to ensure the model accurately counts the filled bottles.

Filled Ready Bottles Counting Results:

After training the model, we were able to accurately count filled ready bottles on the production line. The model's real-time detection helps in maintaining an accurate count of inventory and ensures the production process is efficient.

Example Bottle Counting:

For example, given an image of the production line with multiple filled bottles, the YOLO model was able to detect and count all the bottles accurately, providing a count of 50 filled bottles in the image.

Results:

By applying the YOLO model to count filled ready bottles, businesses can improve inventory management and ensure quality control. This model provides valuable insights into the production process, helping to enhance operational efficiency and accuracy.

Images from the Project