Introduction:
Artificial Intelligence (AI) is everywhere, in every industry, and the food sector is no exception. Leveraging the power of AI to assess food quality can revolutionize the food industry by ensuring the highest quality standards and reducing food waste. In this blog, we will dive into a fascinating project where we used a state-of-the-art object detection model, YOLOv8, to evaluate the quality of bananas.
Project Overview:
The project is named 'Computer Vision for Food Quality Assessment'. The aim was to build an AI model that could accurately classify a banana's quality as ripe, fresh, or rotten. We chose to work with YOLOv8, the latest development in the YOLO (You Only Look Once) series of models.
Project code:
Why YOLOv8 for Food Quality Prediction?
Ultralytics YOLOv8 is a cutting-edge, state-of-the-art model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification, and pose estimation tasks.
Training the Model:
We trained our model for 100 epochs. An epoch is when the entire dataset is passed forward and backward through the neural network once. As epochs increase, the model is expected to improve its accuracy thanks to the optimization of the weights.
The training process took approximately 2.8 hours, with each epoch yielding information about the training losses (box loss, classification loss, and direction feature loss) and metrics (precision, recall, mAP).
Model Performance:
The model showed significant improvement during the training process. The training losses, including box loss, classification loss, and direction feature loss, decreased consistently, suggesting that the model was progressively learning and improving its ability to predict labels.
The precision, recall, and mAP (mean Average Precision) of the model also showed a general upward trend, indicating the model's improving ability to correctly identify and classify objects in the images. By the 100th epoch, the precision had increased to 0.71759, and the recall to 0.58136, demonstrating the model's robust performance.
Inference Time:
One of the key advantages of YOLO models is their speed, which makes them suitable for real-time applications. For a 1.30-minute video, our model took approximately 55 seconds to infer, which is quite impressive.
Conclusion:
In conclusion, using YOLOv8 for food quality assessment proved to be a promising approach. The model demonstrated solid performance metrics and fast inference times, making it a viable tool for real-time food quality assessment.
Deep learning offers a wealth of possibilities for the food industry, from quality control to waste reduction. As this project demonstrates, when we apply these advanced technologies to everyday challenges, we can drive significant improvements and efficiencies.
Future Work:
There is always room for improvement. Techniques like model pruning or quantization could further reduce the inference time. Experimenting with different training parameters or using more training data could also enhance the model's performance.
The world of AI never stands still, and neither do we. We will continue to explore new models, techniques, and datasets to push the boundaries of what's possible in food quality assessment. Stay tuned for more exciting work ahead!
I hope you found this blog helpful and that it sparks your interest in the intersection of AI and food technology. I welcome your thoughts and questions, so please feel free to leave a comment or get in touch. Happy reading!
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