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Updated: Jan 13, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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Influences and Training Strategies for Effective Object Detection in Challenging Environments Using YOLO NAS-L.

Gerald Steindl1, Arnold Baca1, Philipp Kornfeind1

  • 1Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, 1150 Vienna, Austria.

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|January 10, 2026
PubMed
Summary

Optimizing the YOLO NAS-L model for detecting small objects like swimmers requires careful tuning. Key factors for improved performance include batch accumulation, pre-trained weights, and training epochs, not just batch size or image resolution.

Keywords:
F1-scoreYOLO NAS-Lbatch accumulationbatch sizedata augmentationhyperparametersmean Average Precision (mAP) valuepre-trained weightsprecision and recalltraining strategies

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Object Detection

Background:

  • YOLO (You Only Look Once) is a one-stage object detector.
  • YOLO NAS-L is designed for enhanced small object detection.
  • Detecting swimmers in aquatic environments presents a unique challenge.

Purpose of the Study:

  • To investigate the impact of dataset characteristics, training strategies, and hyperparameters on YOLO NAS-L performance.
  • To optimize YOLO NAS-L for detecting swimmers in challenging aquatic environments.
  • To identify critical factors for efficient development of high-performing YOLO NAS-L models.

Main Methods:

  • Systematic investigation of YOLO NAS-L performance.
  • Evaluation using mean Average Precision (mAP) and F1-score.
  • Analysis of hyperparameters: batch size, batch accumulation, epochs, image resolution, pre-trained weights, and data augmentation.

Main Results:

  • Batch size and image resolution showed limited impact on performance.
  • Batch accumulation, pre-trained weights, and training epochs were critical for optimization.
  • Optimized hyperparameters and training strategies significantly improved model performance.

Conclusions:

  • Effective YOLO NAS-L model development relies on a combination of optimized hyperparameters and training strategies.
  • Pre-trained weights are crucial for achieving high performance in object detection tasks.
  • Careful tuning of training epochs is essential for maximizing model effectiveness.