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Solar Panels Yolov8s Seg

Instance segmentation of individual PV arrays from drone and nadir aerial imagery, with tight masks on the panels themselves. Part of the Model Garden collection.

TypeOtherVersionRevision 21d8e9cBClasse

Source du modèle

Type
Other
Taille du fichier
22,7 MB
Version
vRevision 21d8e9c
Mis à jour
10 oct. 2026
Téléchargements
18
Source
Hugging Face

Description de la source

Aerial Solar Panels (YOLOv8s-seg)

Instance segmentation of individual PV arrays from drone and nadir aerial imagery, with tight masks on the panels themselves. Part of the Model Garden collection.

Class

solar-panel

Provenance

Weights derive from finloop/yolov8s-seg-solar-panels, re-hosted here for the Model Garden showcase. On a Wikimedia solar-farm test set it locked panels at conf 0.90 to 0.97 on 6 of 8 shots, missing only very distant obliques.

Training

  • Base: YOLOv8s-seg
  • Task: instance segmentation

Usage

from ultralytics import YOLO

model = YOLO("finloop_seg.pt")
results = model("aerial.jpg")
results[0].show()

Sources

1 source
Hugging FacePrincipalVersion 21d8e9c3908966c79fc0c053e96e171e633495fe
huggingface.co/mfranzon/solar-panels-yolov8s-seg/tree/21d8e9c3908966c79fc0c053e96e171e633495fe

Vérifié 10 oct.

Artefacts du modèle

1 artefact

finloop_seg.pt

pt · 22,7 MB · SHA-256 9379f467a6e9…b94b · Hugging Face

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Extraits de sources

3 extraits

--- license: agpl-3.0 tags: - ultralytics - yolov8 - instance-segmentation - solar-panel - aerial - remote-sensing library_name: ultralytics pipeline_tag: image-segmentation --- # Aerial Solar Panels (YOLOv8s-seg) Instance segmentation of individual PV arrays from **drone and nadir aerial** imagery, with tight masks on the panels themselves. Part of the [Model Garden](https://github.com/computer-vision-with-marco) collection. ## Class `solar-panel` ## Provenance Weights derive from [`finloop/yolov8s-seg-solar-panels`](https://huggingface.co/finloop/yolov8s-seg-solar-panels), re-hosted here for the Model Garden showcase. On a Wikimedia solar-farm test set it locked panels at conf 0.90 to 0.97 on 6 of 8 shots, missing only very distant obliques. ## Training - **Base:** YOLOv8s-seg - **Task:** instance segmentation ## Usage ```python from ultralytics import YOLO model = YOLO("finloop_seg.pt") results = model("aerial.jpg") results[0].show() ```

Source context: 18 downloads · 1 likes · Pipeline image-segmentation · Library ultralytics · Repo mfranzon/solar-panels-yolov8s-seg

Source context: 25 downloads · 0 likes · Pipeline image-segmentation · Library ultralytics · Repo mfranzon/solar-panels-yolov8s-seg