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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.

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Model source

Kind
Other
File size
22.7 MB
Version
vRevision 21d8e9c
Updated
Oct 10, 2026
Downloads
18
Source
Hugging Face

Source description

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 FacePrimaryVersion 21d8e9c3908966c79fc0c053e96e171e633495fe
huggingface.co/mfranzon/solar-panels-yolov8s-seg/tree/21d8e9c3908966c79fc0c053e96e171e633495fe

Verified Oct 10

Model artifacts

1 artifact

finloop_seg.pt

pt · 22.7 MB · SHA-256 9379f467a6e9…b94b · Hugging Face

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Source excerpts

3 excerpts

--- 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