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Updated: Jan 7, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Visual Food Ingredient Prediction Using Deep Learning with Direct F-Score Optimization.

Nawanol Theera-Ampornpunt¹, Panisa Treepong¹

¹College of Computing, Prince of Songkla University, Phuket 83120, Thailand.

Foods (Basel, Switzerland)

|December 30, 2025

Summary

This study introduces a new method for food ingredient prediction from images, improving accuracy on imbalanced datasets. The novel approach efficiently optimizes the F-score, outperforming previous techniques.

Keywords:

F-score optimization cost-sensitive learning deep learning food ingredient prediction image classification loss function

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Food ingredient prediction from images is a complex multi-label classification problem.
Real-world datasets exhibit severe class imbalance, complicating model training and evaluation.
The F-score is crucial for evaluating performance in imbalanced classification tasks.

Purpose of the Study:

To develop a computationally efficient method for direct F-score optimization in food ingredient prediction.
To address the challenges posed by class imbalance in multi-label classification tasks.
To improve the state-of-the-art performance in food ingredient recognition.

Main Methods:

Reformulated direct F-score optimization as a cost-sensitive classifier optimization problem.
Developed an efficient algorithm for estimating optimal relative cost parameters.
Evaluated the proposed method on the Recipe1M dataset.

Main Results:

Achieved a micro F1 score of 0.5616 on the Recipe1M dataset.
Demonstrated a substantial improvement over the previous state-of-the-art score of 0.4927.
The proposed framework offers a principled and generalizable solution for class imbalance.

Conclusions:

The novel F-score optimization framework provides an efficient and effective solution for imbalanced multi-label classification.
This approach significantly enhances food ingredient prediction accuracy.
The method is generalizable to other domains facing similar class imbalance challenges.