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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.

TipoOtherVersãoRevision 21d8e9cBnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
22,7 MB
Versão
vRevision 21d8e9c
Atualizado
10 de out. de 2026
Downloads
18
Fonte
Hugging Face

Descrição da fonte

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()

Fontes

1 fonte
Hugging FacePrimárioVersão 21d8e9c3908966c79fc0c053e96e171e633495fe
huggingface.co/mfranzon/solar-panels-yolov8s-seg/tree/21d8e9c3908966c79fc0c053e96e171e633495fe

Verificado 10 de out.

Artefatos de modelo

1 artefato

finloop_seg.pt

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

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Trechos de fonte

3 trechos

--- 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