Method / resourcemodel-written summary

YOLOV8s reached 121.5 FPS, faster than YOLOV5 at 102.7 FPS and Faster R-CNN at 11 FPS.

A claim from A Deep-Learning-Based Model for the Detection of Diseased Tomato Leaves · 2024

Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

YOLOV8s reached 121.5 FPS, faster than YOLOV5 at 102.7 FPS and Faster R-CNN at 11 FPS.

Outcome
frame ratee0
Direction
superiore0
Value
121.5 FPSe0
Qualifier
Faster R-CNN’s 11 FPSe0
Comparator
YOLOV5e0

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSetting

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Solanum lycopersicum
Solanum lycopersicum · TaxonKod B · CC BY-SA 4.0 · Wikimedia Commons ↗

Verbatim

e0

“The YOLOV8s model has a significantly superior frame rate, reaching 121.5 FPS, in contrast to YOLOV5’s 102.7 FPS and Faster R-CNN’s 11 FPS”

✓ located verbatim in the source · abstract, characters 963–1101
t0

“Tomato Leaves”

✓ located verbatim in the source · title, characters 58–71

Title

A Deep-Learning-Based Model for the Detection of Diseased Tomato Leaves

Akram Abdullah; Gehad Abdullah Amran; S. M. Ahanaf Tahmid; et al. Agronomy 2024. doi:10.3390/agronomy14071593

Other claims from this paper (2)

Record rev-4e86e4d5e2ab311153cb3b258260b13c44335b2ccafb395f91d02c6bc26dc837 · schema claims-v0.9 · position 3 in the model’s answer