Method / resourcemodel-written summary

Fine tuning on the Global Wheat Head Detection Challenge dataset further improved performance.

A claim from A Semi-self-supervised Learning Approach for Wheat Head Detection using Extremely Small Number of Labeled Samples · 2021

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Fine tuning on the Global Wheat Head Detection Challenge dataset further improved performance.

Outcome
performancee0
Direction
improvede0
Comparator
Global Wheat Head Detection Challengee0

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier

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

Triticum aestivum
Triticum aestivum · TaxonRasbak · CC BY-SA 3.0 · Wikimedia Commons ↗

Verbatim

t0

“Wheat Head Detection”

✓ located verbatim in the source · title, characters 45–65
e0

“When fine-tuned on the dataset from the Global Wheat Head Detection Challenge, the performance was further improved.”

✓ located verbatim in the source · abstract, characters 724–840

Title

A Semi-self-supervised Learning Approach for Wheat Head Detection using Extremely Small Number of Labeled Samples

Keyhan Najafian; Alireza Ghanbari; Ian Stavness; et al. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) 2021. doi:10.1109/iccvw54120.2021.00155

Other claims from this paper (2)

Record rev-e4f9656e1c33e89d06965f60b5f193268789c5069e299a75965c966fa0580c37 · schema claims-v0.9 · position 2 in the model’s answer