Toward Optimal Adenovirus Detection Using YOLO26

2026/07/20 by Olivier Rukundo
#cs.CV

paper · pdf

Abstract

This study systematically benchmarks different data augmentation setups across the baseline YOLO26 model size variants to determine the most effective setup for adenovirus detection in TEM images. The benchmarked setups include NAS, GAS, GMAS, and DAS, all evaluated under identical training conditions. A modified YOLO26 model leveraging P2, expanded STAL, increased topk, and MuSGD was also tested across the same benchmarked setups. The adenovirus dataset, selected from the published TEM virus dataset, was re-annotated by leveraging adenovirus particle positions to generate YOLO-compatible bounding box annotations. The modifications produced their largest performance gains under GAS and GMAS, with modified YOLO26x trained using GAS achieving a mAP@50 of 0.80, Precision of 0.81 and a Recall of 0.80

Citations

• Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models
• YOLOv4: Optimal Speed and Accuracy of Object Detection
• You Only Look Once: Unified, Real-Time Object Detection

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